Test and Evaluation for Enhanced Security: A Quantitative Method to Incorporate Expert Knowledge into Test Planning Decisions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rizzo, Davinia; Blackburn, Mark

Complex systems are comprised of technical, social, political and environmental factors as well as the programmatic factors of cost, schedule and risk. Testing these systems for enhanced security requires expert knowledge in many different fields. It is important to test these systems to ensure effectiveness, but testing is limited to due cost, schedule, safety, feasibility and a myriad of other reasons. Without an effective decision framework for Test and Evaluation (T&E) planning that can take into consideration technical as well as programmatic factors and leverage expert knowledge, security in complex systems may not be assessed effectively. Therefore, this paper coversmore » the identification of the current T&E planning problem and an approach to include the full variety of factors and leverage expert knowledge in T&E planning through the use of Bayesian Networks (BN).« less

A novel method for expediting the development of patient-reported outcome measures and an evaluation across several populations.

PubMed

Garrard, Lili; Price, Larry R; Bott, Marjorie J; Gajewski, Byron J

2016-10-01

Item response theory (IRT) models provide an appropriate alternative to the classical ordinal confirmatory factor analysis (CFA) during the development of patient-reported outcome measures (PROMs). Current literature has identified the assessment of IRT model fit as both challenging and underdeveloped (Sinharay & Johnson, 2003; Sinharay, Johnson, & Stern, 2006). This study evaluates the performance of Ordinal Bayesian Instrument Development (OBID), a Bayesian IRT model with a probit link function approach, through applications in two breast cancer-related instrument development studies. The primary focus is to investigate an appropriate method for comparing Bayesian IRT models in PROMs development. An exact Bayesian leave-one-out cross-validation (LOO-CV) approach (Vehtari & Lampinen, 2002) is implemented to assess prior selection for the item discrimination parameter in the IRT model and subject content experts' bias (in a statistical sense and not to be confused with psychometric bias as in differential item functioning) toward the estimation of item-to-domain correlations. Results support the utilization of content subject experts' information in establishing evidence for construct validity when sample size is small. However, the incorporation of subject experts' content information in the OBID approach can be sensitive to the level of expertise of the recruited experts. More stringent efforts need to be invested in the appropriate selection of subject experts to efficiently use the OBID approach and reduce potential bias during PROMs development.

A Bayesian Analysis of a Randomized Clinical Trial Comparing Antimetabolite Therapies for Non-Infectious Uveitis

PubMed Central

Browne, Erica N; Rathinam, Sivakumar R; Kanakath, Anuradha; Thundikandy, Radhika; Babu, Manohar; Lietman, Thomas M; Acharya, Nisha R

2017-01-01

Purpose To conduct a Bayesian analysis of a randomized clinical trial (RCT) for non-infectious uveitis using expert opinion as a subjective prior belief. Methods A RCT was conducted to determine which antimetabolite, methotrexate or mycophenolate mofetil, is more effective as an initial corticosteroid-sparing agent for the treatment of intermediate, posterior, and pan- uveitis. Before the release of trial results, expert opinion on the relative effectiveness of these two medications was collected via online survey. Members of the American Uveitis Society executive committee were invited to provide an estimate for the relative decrease in efficacy with a 95% credible interval (CrI). A prior probability distribution was created from experts’ estimates. A Bayesian analysis was performed using the constructed expert prior probability distribution and the trial’s primary outcome. Results 11 of 12 invited uveitis specialists provided estimates. Eight of 11 experts (73%) believed mycophenolate mofetil is more effective. The group prior belief was that the odds of treatment success for patients taking mycophenolate mofetil were 1.4-fold the odds of those taking methotrexate (95% CrI 0.03 – 45.0). The odds of treatment success with mycophenolate mofetil compared to methotrexate was 0.4 from the RCT (95% confidence interval 0.1–1.2) and 0.7 (95% CrI 0.2–1.7) from the Bayesian analysis. Conclusions A Bayesian analysis combining expert belief with the trial’s result did not indicate preference for one drug. However, the wide credible interval leaves open the possibility of a substantial treatment effect. This suggests clinical equipoise necessary to allow a larger, more definitive RCT. PMID:27982726

Smallholder farms as stepping stone corridors for crop-raiding elephant in northern Tanzania: integration of Bayesian expert system and network simulator.

PubMed

Pittiglio, Claudia; Skidmore, Andrew K; van Gils, Hein A M J; McCall, Michael K; Prins, Herbert H T

2014-03-01

Crop-raiding elephants affect local livelihoods, undermining conservation efforts. Yet, crop-raiding patterns are poorly understood, making prediction and protection difficult. We hypothesized that raiding elephants use corridors between daytime refuges and farmland. Elephant counts, crop-raiding records, household surveys, Bayesian expert system, and least-cost path simulation were used to predict four alternative categories of daily corridors: (1) footpaths, (2) dry river beds, (3) stepping stones along scattered small farms, and (4) trajectories of shortest distance to refuges. The corridor alignments were compared in terms of their minimum cumulative resistance to elephant movement and related to crop-raiding zones quantified by a kernel density function. The "stepping stone" corridors predicted the crop-raiding patterns. Elephant presence was confirmed along these corridors, demonstrating that small farms located between refuges and contiguous farmland increase habitat connectivity for elephant. Our analysis successfully predicted elephant occurrence in farmland where daytime counts failed to detect nocturnal presence. These results have conservation management implications.

Bayesian Islamic medication expert system (B-IMES)

NASA Astrophysics Data System (ADS)

Daud, Hanita; Razali, Radzuan; Jung, Low Tan; Zaida, Shahnaz

2015-12-01

This paper discusses on the development of an expert system (ES) that applies Bayesian Probability concept for Islamic Medication practice that is made available on web platform. This ES allows user to choose sickness such as headache, stomachache, toothache and etc that he/she may have and list of symptoms related to the sickness will appear for the user to choose. Once symptom(s) is/are chosen the diagnosis is being carried out to suggest percentage of possible specific sickness such as classic migraine, common migraine, tension headache and etc if headache was chosen. This diagnosis is being carried out using Bayes' Theorem and the ES will suggest the treatments or therapy that he/she needs to perform in reference to Muslim Holy Quran and Hadith. This ES was developed to preserve Islamic medication and to create awareness among the young generation and make it accessible at anytime and anywhere and to save users time to meet Islamic Medication practitioners who are not easily available in Malaysia and other parts of the world.

A probabilistic method to diagnose faults of air handling units

NASA Astrophysics Data System (ADS)

Dey, Debashis

Air handling unit (AHU) is one of the most extensively used equipment in large commercial buildings. This device is typically customized and lacks quality system integration which can result in hardwire failures and controller errors. Air handling unit Performance Assessment Rules (APAR) is a fault detection tool that uses a set of expert rules derived from mass and energy balances to detect faults in air handling units. APAR is computationally simple enough that it can be embedded in commercial building automation and control systems and relies only upon sensor data and control signals that are commonly available in these systems. Although APAR has many advantages over other methods, for example no training data required and easy to implement commercially, most of the time it is unable to provide the diagnosis of the faults. For instance, a fault on temperature sensor could be fixed bias, drifting bias, inappropriate location, complete failure. Also a fault in mixing box can be return and outdoor damper leak or stuck. In addition, when multiple rules are satisfied the list of faults increases. There is no proper way to have the correct diagnosis for rule based fault detection system. To overcome this limitation we proposed Bayesian Belief Network (BBN) as a diagnostic tool. BBN can be used to simulate diagnostic thinking of FDD experts through a probabilistic way. In this study we developed a new way to detect and diagnose faults in AHU through combining APAR rules and Bayesian Belief network. Bayesian Belief Network is used as a decision support tool for rule based expert system. BBN is highly capable to prioritize faults when multiple rules are satisfied simultaneously. Also it can get information from previous AHU operating conditions and maintenance records to provide proper diagnosis. The proposed model is validated with real time measured data of a campus building at University of Texas at San Antonio (UTSA).The results show that BBN is correctly able to prioritize faults which can be verified by manual investigation.

Bayesian networks for maritime traffic accident prevention: benefits and challenges.

PubMed

Hänninen, Maria

2014-12-01

Bayesian networks are quantitative modeling tools whose applications to the maritime traffic safety context are becoming more popular. This paper discusses the utilization of Bayesian networks in maritime safety modeling. Based on literature and the author's own experiences, the paper studies what Bayesian networks can offer to maritime accident prevention and safety modeling and discusses a few challenges in their application to this context. It is argued that the capability of representing rather complex, not necessarily causal but uncertain relationships makes Bayesian networks an attractive modeling tool for the maritime safety and accidents. Furthermore, as the maritime accident and safety data is still rather scarce and has some quality problems, the possibility to combine data with expert knowledge and the easy way of updating the model after acquiring more evidence further enhance their feasibility. However, eliciting the probabilities from the maritime experts might be challenging and the model validation can be tricky. It is concluded that with the utilization of several data sources, Bayesian updating, dynamic modeling, and hidden nodes for latent variables, Bayesian networks are rather well-suited tools for the maritime safety management and decision-making. Copyright © 2014 Elsevier Ltd. All rights reserved.

Fuzzy Bayesian Network-Bow-Tie Analysis of Gas Leakage during Biomass Gasification

PubMed Central

Yan, Fang; Xu, Kaili; Yao, Xiwen; Li, Yang

2016-01-01

Biomass gasification technology has been rapidly developed recently. But fire and poisoning accidents caused by gas leakage restrict the development and promotion of biomass gasification. Therefore, probabilistic safety assessment (PSA) is necessary for biomass gasification system. Subsequently, Bayesian network-bow-tie (BN-bow-tie) analysis was proposed by mapping bow-tie analysis into Bayesian network (BN). Causes of gas leakage and the accidents triggered by gas leakage can be obtained by bow-tie analysis, and BN was used to confirm the critical nodes of accidents by introducing corresponding three importance measures. Meanwhile, certain occurrence probability of failure was needed in PSA. In view of the insufficient failure data of biomass gasification, the occurrence probability of failure which cannot be obtained from standard reliability data sources was confirmed by fuzzy methods based on expert judgment. An improved approach considered expert weighting to aggregate fuzzy numbers included triangular and trapezoidal numbers was proposed, and the occurrence probability of failure was obtained. Finally, safety measures were indicated based on the obtained critical nodes. The theoretical occurrence probabilities in one year of gas leakage and the accidents caused by it were reduced to 1/10.3 of the original values by these safety measures. PMID:27463975

Probabilistic inversion of expert assessments to inform projections about Antarctic ice sheet responses.

PubMed

Fuller, Robert William; Wong, Tony E; Keller, Klaus

2017-01-01

The response of the Antarctic ice sheet (AIS) to changing global temperatures is a key component of sea-level projections. Current projections of the AIS contribution to sea-level changes are deeply uncertain. This deep uncertainty stems, in part, from (i) the inability of current models to fully resolve key processes and scales, (ii) the relatively sparse available data, and (iii) divergent expert assessments. One promising approach to characterizing the deep uncertainty stemming from divergent expert assessments is to combine expert assessments, observations, and simple models by coupling probabilistic inversion and Bayesian inversion. Here, we present a proof-of-concept study that uses probabilistic inversion to fuse a simple AIS model and diverse expert assessments. We demonstrate the ability of probabilistic inversion to infer joint prior probability distributions of model parameters that are consistent with expert assessments. We then confront these inferred expert priors with instrumental and paleoclimatic observational data in a Bayesian inversion. These additional constraints yield tighter hindcasts and projections. We use this approach to quantify how the deep uncertainty surrounding expert assessments affects the joint probability distributions of model parameters and future projections.

Common problems in the elicitation and analysis of expert opinion affecting probabilistic safety assessments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meyer, M.A.; Booker, J.M.

1990-01-01

Expert opinion is frequently used in probabilistic safety assessment (PSA), particularly in estimating low probability events. In this paper, we discuss some of the common problems encountered in eliciting and analyzing expert opinion data and offer solutions or recommendations. The problems are: that experts are not naturally Bayesian. People fail to update their existing information to account for new information as it becomes available, as would be predicted by the Bayesian philosophy; that experts cannot be fully calibrated. To calibrate experts, the feedback from the known quantities must be immediate, frequent, and specific to the task; that experts are limitedmore » in the number of things that they can mentally juggle at a time to 7 {plus minus} 2; that data gatherers and analysts can introduce bias by unintentionally causing an altering of the expert's thinking or answers; that the level of detail the data, or granularity, can affect the analyses; and the conditioning effect poses difficulties in gathering and analyzing of the expert data. The data that the expert gives can be conditioned on a variety of factors that can affect the analysis and the interpretation of the results. 31 refs.« less

The Lifecycle of Bayesian Network Models Developed for Multi-Source Signature Assessment of Nuclear Programs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gastelum, Zoe N.; White, Amanda M.; Whitney, Paul D.

2013-06-04

The Multi-Source Signatures for Nuclear Programs project, part of Pacific Northwest National Laboratory’s (PNNL) Signature Discovery Initiative, seeks to computationally capture expert assessment of multi-type information such as text, sensor output, imagery, or audio/video files, to assess nuclear activities through a series of Bayesian network (BN) models. These models incorporate knowledge from a diverse range of information sources in order to help assess a country’s nuclear activities. The models span engineering topic areas, state-level indicators, and facility-specific characteristics. To illustrate the development, calibration, and use of BN models for multi-source assessment, we present a model that predicts a country’s likelihoodmore » to participate in the international nuclear nonproliferation regime. We validate this model by examining the extent to which the model assists non-experts arrive at conclusions similar to those provided by nuclear proliferation experts. We also describe the PNNL-developed software used throughout the lifecycle of the Bayesian network model development.« less

A Bayesian approach to reliability and confidence

NASA Technical Reports Server (NTRS)

Barnes, Ron

1989-01-01

The historical evolution of NASA's interest in quantitative measures of reliability assessment is outlined. The introduction of some quantitative methodologies into the Vehicle Reliability Branch of the Safety, Reliability and Quality Assurance (SR and QA) Division at Johnson Space Center (JSC) was noted along with the development of the Extended Orbiter Duration--Weakest Link study which will utilize quantitative tools for a Bayesian statistical analysis. Extending the earlier work of NASA sponsor, Richard Heydorn, researchers were able to produce a consistent Bayesian estimate for the reliability of a component and hence by a simple extension for a system of components in some cases where the rate of failure is not constant but varies over time. Mechanical systems in general have this property since the reliability usually decreases markedly as the parts degrade over time. While they have been able to reduce the Bayesian estimator to a simple closed form for a large class of such systems, the form for the most general case needs to be attacked by the computer. Once a table is generated for this form, researchers will have a numerical form for the general solution. With this, the corresponding probability statements about the reliability of a system can be made in the most general setting. Note that the utilization of uniform Bayesian priors represents a worst case scenario in the sense that as researchers incorporate more expert opinion into the model, they will be able to improve the strength of the probability calculations.

A conceptual model for site-level ecology of the giant gartersnake (Thamnophis gigas) in the Sacramento Valley, California

USGS Publications Warehouse

Halstead, Brian J.; Wylie, Glenn D.; Casazza, Michael L.; Hansen, Eric C.; Scherer, Rick D.; Patterson, Laura C.

2015-08-14

Bayesian networks further provide a clear visual display of the model that facilitates understanding among various stakeholders (Marcot and others, 2001; Uusitalo , 2007). Empirical data and expert judgment can be combined, as continuous or categorical variables, to update knowledge about the system (Marcot and others, 2001; Uusitalo , 2007). Importantly, Bayesian network models allow inference from causes to consequences, but also from consequences to causes, so that data can inform the states of nodes (values of different random variables) in either direction (Marcot and others, 2001; Uusitalo , 2007). Because they can incorporate both decision nodes that represent management actions and utility nodes that quantify the costs and benefits of outcomes, Bayesian networks are ideally suited to risk analysis and adaptive management (Nyberg and others, 2006; Howes and others, 2010). Thus, Bayesian network models are useful in situations where empirical data are not available, such as questions concerning the responses of giant gartersnakes to management.

Probabilistic inversion of expert assessments to inform projections about Antarctic ice sheet responses

PubMed Central

Wong, Tony E.; Keller, Klaus

2017-01-01

The response of the Antarctic ice sheet (AIS) to changing global temperatures is a key component of sea-level projections. Current projections of the AIS contribution to sea-level changes are deeply uncertain. This deep uncertainty stems, in part, from (i) the inability of current models to fully resolve key processes and scales, (ii) the relatively sparse available data, and (iii) divergent expert assessments. One promising approach to characterizing the deep uncertainty stemming from divergent expert assessments is to combine expert assessments, observations, and simple models by coupling probabilistic inversion and Bayesian inversion. Here, we present a proof-of-concept study that uses probabilistic inversion to fuse a simple AIS model and diverse expert assessments. We demonstrate the ability of probabilistic inversion to infer joint prior probability distributions of model parameters that are consistent with expert assessments. We then confront these inferred expert priors with instrumental and paleoclimatic observational data in a Bayesian inversion. These additional constraints yield tighter hindcasts and projections. We use this approach to quantify how the deep uncertainty surrounding expert assessments affects the joint probability distributions of model parameters and future projections. PMID:29287095

Predicting Mycobacterium tuberculosis Complex Clades Using Knowledge-Based Bayesian Networks

PubMed Central

Bennett, Kristin P.

2014-01-01

We develop a novel approach for incorporating expert rules into Bayesian networks for classification of Mycobacterium tuberculosis complex (MTBC) clades. The proposed knowledge-based Bayesian network (KBBN) treats sets of expert rules as prior distributions on the classes. Unlike prior knowledge-based support vector machine approaches which require rules expressed as polyhedral sets, KBBN directly incorporates the rules without any modification. KBBN uses data to refine rule-based classifiers when the rule set is incomplete or ambiguous. We develop a predictive KBBN model for 69 MTBC clades found in the SITVIT international collection. We validate the approach using two testbeds that model knowledge of the MTBC obtained from two different experts and large DNA fingerprint databases to predict MTBC genetic clades and sublineages. These models represent strains of MTBC using high-throughput biomarkers called spacer oligonucleotide types (spoligotypes), since these are routinely gathered from MTBC isolates of tuberculosis (TB) patients. Results show that incorporating rules into problems can drastically increase classification accuracy if data alone are insufficient. The SITVIT KBBN is publicly available for use on the World Wide Web. PMID:24864238

On the use of multi-agent systems for the monitoring of industrial systems

NASA Astrophysics Data System (ADS)

Rezki, Nafissa; Kazar, Okba; Mouss, Leila Hayet; Kahloul, Laid; Rezki, Djamil

2016-03-01

The objective of the current paper is to present an intelligent system for complex process monitoring, based on artificial intelligence technologies. This system aims to realize with success all the complex process monitoring tasks that are: detection, diagnosis, identification and reconfiguration. For this purpose, the development of a multi-agent system that combines multiple intelligences such as: multivariate control charts, neural networks, Bayesian networks and expert systems has became a necessity. The proposed system is evaluated in the monitoring of the complex process Tennessee Eastman process.

Development of uncertainty-based work injury model using Bayesian structural equation modelling.

PubMed

Chatterjee, Snehamoy

2014-01-01

This paper proposed a Bayesian method-based structural equation model (SEM) of miners' work injury for an underground coal mine in India. The environmental and behavioural variables for work injury were identified and causal relationships were developed. For Bayesian modelling, prior distributions of SEM parameters are necessary to develop the model. In this paper, two approaches were adopted to obtain prior distribution for factor loading parameters and structural parameters of SEM. In the first approach, the prior distributions were considered as a fixed distribution function with specific parameter values, whereas, in the second approach, prior distributions of the parameters were generated from experts' opinions. The posterior distributions of these parameters were obtained by applying Bayesian rule. The Markov Chain Monte Carlo sampling in the form Gibbs sampling was applied for sampling from the posterior distribution. The results revealed that all coefficients of structural and measurement model parameters are statistically significant in experts' opinion-based priors, whereas, two coefficients are not statistically significant when fixed prior-based distributions are applied. The error statistics reveals that Bayesian structural model provides reasonably good fit of work injury with high coefficient of determination (0.91) and less mean squared error as compared to traditional SEM.

Linking medical records to an expert system

NASA Technical Reports Server (NTRS)

Naeymi-Rad, Frank; Trace, David; Desouzaalmeida, Fabio

1991-01-01

This presentation will be done using the IMR-Entry (Intelligent Medical Record Entry) system. IMR-Entry is a software program developed as a front-end to our diagnostic consultant software MEDAS (Medical Emergency Decision Assistance System). MEDAS (the Medical Emergency Diagnostic Assistance System) is a diagnostic consultant system using a multimembership Bayesian design for its inference engine and relational database technology for its knowledge base maintenance. Research on MEDAS began at the University of Southern California and the Institute of Critical Care in the mid 1970's with support from NASA and NSF. The MEDAS project moved to Chicago in 1982; its current progress is due to collaboration between Illinois Institute of Technology, The Chicago Medical School, Lake Forest College and NASA at KSC. Since the purpose of an expert system is to derive a hypothesis, its communication vocabulary is limited to features used by its knowledge base. The development of a comprehensive problem based medical record entry system which could handshake with an expert system while creating an electronic medical record at the same time was studied. IMR-E is a computer based patient record that serves as a front end to the expert system MEDAS. IMR-E is a graphically oriented comprehensive medical record. The programs major components are demonstrated.

IMPLICATIONS OF USING ROBUST BAYESIAN ANALYSIS TO REPRESENT DIVERSE SOURCES OF UNCERTAINTY IN INTEGRATED ASSESSMENT

EPA Science Inventory

In our previous research, we showed that robust Bayesian methods can be used in environmental modeling to define a set of probability distributions for key parameters that captures the effects of expert disagreement, ambiguity, or ignorance. This entire set can then be update...

Seeing Like a Geologist: Bayesian Use of Expert Categories in Location Memory

ERIC Educational Resources Information Center

Holden, Mark P.; Newcombe, Nora S.; Resnick, Ilyse; Shipley, Thomas F.

2016-01-01

Memory for spatial location is typically biased, with errors trending toward the center of a surrounding region. According to the category adjustment model (CAM), this bias reflects the optimal, Bayesian combination of fine-grained and categorical representations of a location. However, there is disagreement about whether categories are malleable.…

Potential Uses of Bayesian Networks as Tools for Synthesis of Systematic Reviews of Complex Interventions

ERIC Educational Resources Information Center

Stewart, G. B.; Mengersen, K.; Meader, N.

2014-01-01

Bayesian networks (BNs) are tools for representing expert knowledge or evidence. They are especially useful for synthesising evidence or belief concerning a complex intervention, assessing the sensitivity of outcomes to different situations or contextual frameworks and framing decision problems that involve alternative types of intervention.…

Evaluating Vegetation Potential for Wildfire Impacted Watershed Using a Bayesian Network Modeling Approach

NASA Astrophysics Data System (ADS)

Jaramillo, L. V.; Stone, M. C.; Morrison, R. R.

2017-12-01

Decision-making for natural resource management is complex especially for fire impacted watersheds in the Southwestern US because of the vital importance of water resources, exorbitant cost of fire management and restoration, and the risks of the wildland-urban interface (WUI). While riparian and terrestrial vegetation are extremely important to ecosystem health and provide ecosystem services, loss of vegetation due to wildfire, post-fire flooding, and debris flows can lead to further degradation of the watershed and increased vulnerability to erosion and debris flow. Land managers are charged with taking measures to mitigate degradation of the watershed effectively and efficiently with limited time, money, and data. For our study, a Bayesian network (BN) approach is implemented to understand vegetation potential for Kashe-Katuwe Tent Rocks National Monument in the fire-impacted Peralta Canyon Watershed, New Mexico, USA. We implement both two-dimensional hydrodynamic and Bayesian network modeling to incorporate spatial variability in the system. Our coupled modeling framework presents vegetation recruitment and succession potential for three representative plant types (native riparian, native terrestrial, and non-native) under several hydrologic scenarios and management actions. In our BN model, we use variables that address timing, hydrologic, and groundwater conditions as well as recruitment and succession constraints for the plant types based on expert knowledge and literature. Our approach allows us to utilize small and incomplete data, incorporate expert knowledge, and explicitly account for uncertainty in the system. Our findings can be used to help land managers and local decision-makers determine their plan of action to increase watershed health and resilience.

Mine Burial Expert System for Change of MIW Doctrine

DTIC Science & Technology

2011-09-01

allowed the mine to move vertically and horizontally, as well as rotate about the y axis. The first of these second generation impact models was...bearing strength and use multilayered sediments. Although they improve the knowledge of mine movement in two dimensions and rotation in one direction...conditional independence. Bayesian networks were originally developed 24 to handle uncertainty in a quantitative manner. They are statistical models

Confirmatory Factor Analysis Alternative: Free, Accessible CBID Software.

PubMed

Bott, Marjorie; Karanevich, Alex G; Garrard, Lili; Price, Larry R; Mudaranthakam, Dinesh Pal; Gajewski, Byron

2018-02-01

New software that performs Classical and Bayesian Instrument Development (CBID) is reported that seamlessly integrates expert (content validity) and participant data (construct validity) to produce entire reliability estimates with smaller sample requirements. The free CBID software can be accessed through a website and used by clinical investigators in new instrument development. Demonstrations are presented of the three approaches using the CBID software: (a) traditional confirmatory factor analysis (CFA), (b) Bayesian CFA using flat uninformative prior, and (c) Bayesian CFA using content expert data (informative prior). Outcomes of usability testing demonstrate the need to make the user-friendly, free CBID software available to interdisciplinary researchers. CBID has the potential to be a new and expeditious method for instrument development, adding to our current measurement toolbox. This allows for the development of new instruments for measuring determinants of health in smaller diverse populations or populations of rare diseases.

A Bayesian Belief Network approach to assess the potential of non wood forest products for small scale forest owners

NASA Astrophysics Data System (ADS)

Vacik, Harald; Huber, Patrick; Hujala, Teppo; Kurtilla, Mikko; Wolfslehner, Bernhard

2015-04-01

It is an integral element of the European understanding of sustainable forest management to foster the design and marketing of forest products, non-wood forest products (NWFPs) and services that go beyond the production of timber. Despite the relevance of NWFPs in Europe, forest management and planning methods have been traditionally tailored towards wood and wood products, because most forest management models and silviculture techniques were developed to ensure a sustained production of timber. Although several approaches exist which explicitly consider NWFPs as management objectives in forest planning, specific models are needed for the assessment of their production potential in different environmental contexts and for different management regimes. Empirical data supporting a comprehensive assessment of the potential of NWFPs are rare, thus making development of statistical models particularly problematic. However, the complex causal relationships between the sustained production of NWFPs, the available ecological resources, as well as the organizational and the market potential of forest management regimes are well suited for knowledge-based expert models. Bayesian belief networks (BBNs) are a kind of probabilistic graphical model that have become very popular to practitioners and scientists mainly due to the powerful probability theory involved, which makes BBNs suitable to deal with a wide range of environmental problems. In this contribution we present the development of a Bayesian belief network to assess the potential of NWFPs for small scale forest owners. A three stage iterative process with stakeholder and expert participation was used to develop the Bayesian Network within the frame of the StarTree Project. The group of participants varied in the stages of the modelling process. A core team, consisting of one technical expert and two domain experts was responsible for the entire modelling process as well as for the first prototype of the network structure, including nodes and relationships. A top-level causal network, was further decomposed to sub level networks. Stakeholder participation including a group of experts from different related subject areas was used in model verification and validation. We demonstrate that BBNs can be used to transfer expert knowledge from science to practice and thus have the ability to contribute to improved problem understanding of non-expert decision makers for a sustainable production of NWFPs.

SeTES, a Self-Teaching Expert System for the analysis, design and prediction of gas production from shales and a prototype for a new generation of Expert Systems in the Earth Sciences

NASA Astrophysics Data System (ADS)

Kuzma, H. A.; Boyle, K.; Pullman, S.; Reagan, M. T.; Moridis, G. J.; Blasingame, T. A.; Rector, J. W.; Nikolaou, M.

2010-12-01

A Self Teaching Expert System (SeTES) is being developed for the analysis, design and prediction of gas production from shales. An Expert System is a computer program designed to answer questions or clarify uncertainties that its designers did not necessarily envision which would otherwise have to be addressed by consultation with one or more human experts. Modern developments in computer learning, data mining, database management, web integration and cheap computing power are bringing the promise of expert systems to fruition. SeTES is a partial successor to Prospector, a system to aid in the identification and evaluation of mineral deposits developed by Stanford University and the USGS in the late 1970s, and one of the most famous early expert systems. Instead of the text dialogue used in early systems, the web user interface of SeTES helps a non-expert user to articulate, clarify and reason about a problem by navigating through a series of interactive wizards. The wizards identify potential solutions to queries by retrieving and combining together relevant records from a database. Inferences, decisions and predictions are made from incomplete and noisy inputs using a series of probabilistic models (Bayesian Networks) which incorporate records from the database, physical laws and empirical knowledge in the form of prior probability distributions. The database is mainly populated with empirical measurements, however an automatic algorithm supplements sparse data with synthetic data obtained through physical modeling. This constitutes the mechanism for how SeTES self-teaches. SeTES’ predictive power is expected to grow as users contribute more data into the system. Samples are appropriately weighted to favor high quality empirical data over low quality or synthetic data. Finally, a set of data visualization tools digests the output measurements into graphical outputs.

Rule groupings in expert systems using nearest neighbour decision rules, and convex hulls

NASA Technical Reports Server (NTRS)

Anastasiadis, Stergios

1991-01-01

Expert System shells are lacking in many areas of software engineering. Large rule based systems are not semantically comprehensible, difficult to debug, and impossible to modify or validate. Partitioning a set of rules found in CLIPS (C Language Integrated Production System) into groups of rules which reflect the underlying semantic subdomains of the problem, will address adequately the concerns stated above. Techniques are introduced to structure a CLIPS rule base into groups of rules that inherently have common semantic information. The concepts involved are imported from the field of A.I., Pattern Recognition, and Statistical Inference. Techniques focus on the areas of feature selection, classification, and a criteria of how 'good' the classification technique is, based on Bayesian Decision Theory. A variety of distance metrics are discussed for measuring the 'closeness' of CLIPS rules and various Nearest Neighbor classification algorithms are described based on the above metric.

What is an expert? A systems perspective on expertise.

PubMed

Caley, Michael Julian; O'Leary, Rebecca A; Fisher, Rebecca; Low-Choy, Samantha; Johnson, Sandra; Mengersen, Kerrie

2014-02-01

Expert knowledge is a valuable source of information with a wide range of research applications. Despite the recent advances in defining expert knowledge, little attention has been given to how to view expertise as a system of interacting contributory factors for quantifying an individual's expertise. We present a systems approach to expertise that accounts for many contributing factors and their inter-relationships and allows quantification of an individual's expertise. A Bayesian network (BN) was chosen for this purpose. For illustration, we focused on taxonomic expertise. The model structure was developed in consultation with taxonomists. The relative importance of the factors within the network was determined by a second set of taxonomists (supra-experts) who also provided validation of the model structure. Model performance was assessed by applying the model to hypothetical career states of taxonomists designed to incorporate known differences in career states for model testing. The resulting BN model consisted of 18 primary nodes feeding through one to three higher-order nodes before converging on the target node (Taxonomic Expert). There was strong consistency among node weights provided by the supra-experts for some nodes, but not others. The higher-order nodes, "Quality of work" and "Total productivity", had the greatest weights. Sensitivity analysis indicated that although some factors had stronger influence in the outer nodes of the network, there was relatively equal influence of the factors leading directly into the target node. Despite the differences in the node weights provided by our supra-experts, there was good agreement among assessments of our hypothetical experts that accurately reflected differences we had specified. This systems approach provides a way of assessing the overall level of expertise of individuals, accounting for multiple contributory factors, and their interactions. Our approach is adaptable to other situations where it is desirable to understand components of expertise.

Development of a Bayesian Belief Network Runway Incursion Model

NASA Technical Reports Server (NTRS)

Green, Lawrence L.

2014-01-01

In a previous paper, a statistical analysis of runway incursion (RI) events was conducted to ascertain their relevance to the top ten Technical Challenges (TC) of the National Aeronautics and Space Administration (NASA) Aviation Safety Program (AvSP). The study revealed connections to perhaps several of the AvSP top ten TC. That data also identified several primary causes and contributing factors for RI events that served as the basis for developing a system-level Bayesian Belief Network (BBN) model for RI events. The system-level BBN model will allow NASA to generically model the causes of RI events and to assess the effectiveness of technology products being developed under NASA funding. These products are intended to reduce the frequency of RI events in particular, and to improve runway safety in general. The development, structure and assessment of that BBN for RI events by a Subject Matter Expert panel are documented in this paper.

New insights into the classification and nomenclature of cortical GABAergic interneurons.

PubMed

DeFelipe, Javier; López-Cruz, Pedro L; Benavides-Piccione, Ruth; Bielza, Concha; Larrañaga, Pedro; Anderson, Stewart; Burkhalter, Andreas; Cauli, Bruno; Fairén, Alfonso; Feldmeyer, Dirk; Fishell, Gord; Fitzpatrick, David; Freund, Tamás F; González-Burgos, Guillermo; Hestrin, Shaul; Hill, Sean; Hof, Patrick R; Huang, Josh; Jones, Edward G; Kawaguchi, Yasuo; Kisvárday, Zoltán; Kubota, Yoshiyuki; Lewis, David A; Marín, Oscar; Markram, Henry; McBain, Chris J; Meyer, Hanno S; Monyer, Hannah; Nelson, Sacha B; Rockland, Kathleen; Rossier, Jean; Rubenstein, John L R; Rudy, Bernardo; Scanziani, Massimo; Shepherd, Gordon M; Sherwood, Chet C; Staiger, Jochen F; Tamás, Gábor; Thomson, Alex; Wang, Yun; Yuste, Rafael; Ascoli, Giorgio A

2013-03-01

A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria. Using Bayesian analysis and clustering algorithms on the resulting data, we investigated the suitability of several anatomical terms and neuron names for cortical GABAergic interneurons. Moreover, we show that supervised classification models could automatically categorize interneurons in agreement with experts' assignments. These results demonstrate a practical and objective approach to the naming, characterization and classification of neurons based on community consensus.

A multi-agent intelligent environment for medical knowledge.

PubMed

Vicari, Rosa M; Flores, Cecilia D; Silvestre, André M; Seixas, Louise J; Ladeira, Marcelo; Coelho, Helder

2003-03-01

AMPLIA is a multi-agent intelligent learning environment designed to support training of diagnostic reasoning and modelling of domains with complex and uncertain knowledge. AMPLIA focuses on the medical area. It is a system that deals with uncertainty under the Bayesian network approach, where learner-modelling tasks will consist of creating a Bayesian network for a problem the system will present. The construction of a network involves qualitative and quantitative aspects. The qualitative part concerns the network topology, that is, causal relations among the domain variables. After it is ready, the quantitative part is specified. It is composed of the distribution of conditional probability of the variables represented. A negotiation process (managed by an intelligent MediatorAgent) will treat the differences of topology and probability distribution between the model the learner built and the one built-in in the system. That negotiation process occurs between the agents that represent the expert knowledge domain (DomainAgent) and the agent that represents the learner knowledge (LearnerAgent).

Hot news recommendation system from heterogeneous websites based on bayesian model.

PubMed

Xia, Zhengyou; Xu, Shengwu; Liu, Ningzhong; Zhao, Zhengkang

2014-01-01

The most current news recommendations are suitable for news which comes from a single news website, not for news from different heterogeneous news websites. Previous researches about news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing hundreds of heterogeneous news websites to provide top hot news services for group customers (e.g., government staffs). In this paper, we propose a hot news recommendation model based on Bayesian model, which is from hundreds of different news websites. In the model, we determine whether the news is hot news by calculating the joint probability of the news. We evaluate and compare our proposed recommendation model with the results of human experts on the real data sets. Experimental results demonstrate the reliability and effectiveness of our method. We also implement this model in hot news recommendation system of Hangzhou city government in year 2013, which achieves very good results.

Hot News Recommendation System from Heterogeneous Websites Based on Bayesian Model

PubMed Central

Xia, Zhengyou; Xu, Shengwu; Liu, Ningzhong; Zhao, Zhengkang

2014-01-01

The most current news recommendations are suitable for news which comes from a single news website, not for news from different heterogeneous news websites. Previous researches about news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing hundreds of heterogeneous news websites to provide top hot news services for group customers (e.g., government staffs). In this paper, we propose a hot news recommendation model based on Bayesian model, which is from hundreds of different news websites. In the model, we determine whether the news is hot news by calculating the joint probability of the news. We evaluate and compare our proposed recommendation model with the results of human experts on the real data sets. Experimental results demonstrate the reliability and effectiveness of our method. We also implement this model in hot news recommendation system of Hangzhou city government in year 2013, which achieves very good results. PMID:25093207

Bayesian Networks for Modeling Dredging Decisions

DTIC Science & Technology

2011-10-01

change scenarios. Arctic Expert elicitation Netica Bacon et al . 2002 Identify factors that might lead to a change in land use from farming to...tree) algorithms developed by Lauritzen and Spiegelhalter (1988) and Jensen et al . (1990). Statistical inference is simply the process of...causality when constructing a Bayesian network (Kjaerulff and Madsen 2008, Darwiche 2009, Marcot et al . 2006). A knowledge representation approach is the

Bayesian networks in overlay recipe optimization

NASA Astrophysics Data System (ADS)

Binns, Lewis A.; Reynolds, Greg; Rigden, Timothy C.; Watkins, Stephen; Soroka, Andrew

2005-05-01

Currently, overlay measurements are characterized by "recipe", which defines both physical parameters such as focus, illumination et cetera, and also the software parameters such as algorithm to be used and regions of interest. Setting up these recipes requires both engineering time and wafer availability on an overlay tool, so reducing these requirements will result in higher tool productivity. One of the significant challenges to automating this process is that the parameters are highly and complexly correlated. At the same time, a high level of traceability and transparency is required in the recipe creation process, so a technique that maintains its decisions in terms of well defined physical parameters is desirable. Running time should be short, given the system (automatic recipe creation) is being implemented to reduce overheads. Finally, a failure of the system to determine acceptable parameters should be obvious, so a certainty metric is also desirable. The complex, nonlinear interactions make solution by an expert system difficult at best, especially in the verification of the resulting decision network. The transparency requirements tend to preclude classical neural networks and similar techniques. Genetic algorithms and other "global minimization" techniques require too much computational power (given system footprint and cost requirements). A Bayesian network, however, provides a solution to these requirements. Such a network, with appropriate priors, can be used during recipe creation / optimization not just to select a good set of parameters, but also to guide the direction of search, by evaluating the network state while only incomplete information is available. As a Bayesian network maintains an estimate of the probability distribution of nodal values, a maximum-entropy approach can be utilized to obtain a working recipe in a minimum or near-minimum number of steps. In this paper we discuss the potential use of a Bayesian network in such a capacity, reducing the amount of engineering intervention. We discuss the benefits of this approach, especially improved repeatability and traceability of the learning process, and quantification of uncertainty in decisions made. We also consider the problems associated with this approach, especially in detailed construction of network topology, validation of the Bayesian network and the recipes it generates, and issues arising from the integration of a Bayesian network with a complex multithreaded application; these primarily relate to maintaining Bayesian network and system architecture integrity.

Developing and Testing a Model to Predict Outcomes of Organizational Change

PubMed Central

Gustafson, David H; Sainfort, François; Eichler, Mary; Adams, Laura; Bisognano, Maureen; Steudel, Harold

2003-01-01

Objective To test the effectiveness of a Bayesian model employing subjective probability estimates for predicting success and failure of health care improvement projects. Data Sources Experts' subjective assessment data for model development and independent retrospective data on 221 healthcare improvement projects in the United States, Canada, and the Netherlands collected between 1996 and 2000 for validation. Methods A panel of theoretical and practical experts and literature in organizational change were used to identify factors predicting the outcome of improvement efforts. A Bayesian model was developed to estimate probability of successful change using subjective estimates of likelihood ratios and prior odds elicited from the panel of experts. A subsequent retrospective empirical analysis of change efforts in 198 health care organizations was performed to validate the model. Logistic regression and ROC analysis were used to evaluate the model's performance using three alternative definitions of success. Data Collection For the model development, experts' subjective assessments were elicited using an integrative group process. For the validation study, a staff person intimately involved in each improvement project responded to a written survey asking questions about model factors and project outcomes. Results Logistic regression chi-square statistics and areas under the ROC curve demonstrated a high level of model performance in predicting success. Chi-square statistics were significant at the 0.001 level and areas under the ROC curve were greater than 0.84. Conclusions A subjective Bayesian model was effective in predicting the outcome of actual improvement projects. Additional prospective evaluations as well as testing the impact of this model as an intervention are warranted. PMID:12785571

Bayesian networks for satellite payload testing

NASA Astrophysics Data System (ADS)

Przytula, Krzysztof W.; Hagen, Frank; Yung, Kar

1999-11-01

Satellite payloads are fast increasing in complexity, resulting in commensurate growth in cost of manufacturing and operation. A need exists for a software tool, which would assist engineers in production and operation of satellite systems. We have designed and implemented a software tool, which performs part of this task. The tool aids a test engineer in debugging satellite payloads during system testing. At this stage of satellite integration and testing both the tested payload and the testing equipment represent complicated systems consisting of a very large number of components and devices. When an error is detected during execution of a test procedure, the tool presents to the engineer a ranked list of potential sources of the error and a list of recommended further tests. The engineer decides this on this basis if to perform some of the recommended additional test or replace the suspect component. The tool has been installed in payload testing facility. The tool is based on Bayesian networks, a graphical method of representing uncertainty in terms of probabilistic influences. The Bayesian network was configured using detailed flow diagrams of testing procedures and block diagrams of the payload and testing hardware. The conditional and prior probability values were initially obtained from experts and refined in later stages of design. The Bayesian network provided a very informative model of the payload and testing equipment and inspired many new ideas regarding the future test procedures and testing equipment configurations. The tool is the first step in developing a family of tools for various phases of satellite integration and operation.

Detecting ‘Wrong Blood in Tube’ Errors: Evaluation of a Bayesian Network Approach

PubMed Central

Doctor, Jason N.; Strylewicz, Greg

2010-01-01

Objective In an effort to address the problem of laboratory errors, we develop and evaluate a method to detect mismatched specimens from nationally collected blood laboratory data in two experiments. Methods In Experiment 1 and 2 using blood labs from National Health and Nutrition Examination Survey (NHANES) and values derived from the Diabetes Prevention Program (DPP) respectively, a proportion of glucose and HbA1c specimens were randomly mismatched. A Bayesian network that encoded probabilistic relationships among analytes was used to predict mismatches. In Experiment 1 the performance of the network was compared against existing error detection software. In Experiment 2 the network was compared against 11 human experts recruited from the American Academy of Clinical Chemists. Results were compared via area under the receiver-operating characteristics curves (AUCs) and with agreement statistics. Results In Experiment 1 the network was most predictive of mismatches that produced clinically significant discrepancies between true and mismatched scores ((AUC of 0.87 (±0.04) for HbA1c and 0.83 (±0.02) for glucose), performed well in identifying errors among those self-reporting diabetes (N = 329) (AUC = 0.79 (± 0.02)) and performed significantly better than the established approach it was tested against (in all cases p < .0.05). In Experiment 2 it performed better (and in no case worse) than 7 of the 11 human experts. Average percent agreement was 0.79. and Kappa (κ) was 0.59, between experts and the Bayesian network. Conclusions Bayesian network can accurately identify mismatched specimens. The algorithm is best at identifying mismatches that result in a clinically significant magnitude of error. PMID:20566275

A Bayesian hierarchical model for mortality data from cluster-sampling household surveys in humanitarian crises.

PubMed

Heudtlass, Peter; Guha-Sapir, Debarati; Speybroeck, Niko

2018-05-31

The crude death rate (CDR) is one of the defining indicators of humanitarian emergencies. When data from vital registration systems are not available, it is common practice to estimate the CDR from household surveys with cluster-sampling design. However, sample sizes are often too small to compare mortality estimates to emergency thresholds, at least in a frequentist framework. Several authors have proposed Bayesian methods for health surveys in humanitarian crises. Here, we develop an approach specifically for mortality data and cluster-sampling surveys. We describe a Bayesian hierarchical Poisson-Gamma mixture model with generic (weakly informative) priors that could be used as default in absence of any specific prior knowledge, and compare Bayesian and frequentist CDR estimates using five different mortality datasets. We provide an interpretation of the Bayesian estimates in the context of an emergency threshold and demonstrate how to interpret parameters at the cluster level and ways in which informative priors can be introduced. With the same set of weakly informative priors, Bayesian CDR estimates are equivalent to frequentist estimates, for all practical purposes. The probability that the CDR surpasses the emergency threshold can be derived directly from the posterior of the mean of the mixing distribution. All observation in the datasets contribute to the estimation of cluster-level estimates, through the hierarchical structure of the model. In a context of sparse data, Bayesian mortality assessments have advantages over frequentist ones already when using only weakly informative priors. More informative priors offer a formal and transparent way of combining new data with existing data and expert knowledge and can help to improve decision-making in humanitarian crises by complementing frequentist estimates.

Capturing changes in flood risk with Bayesian approaches for flood damage assessment

NASA Astrophysics Data System (ADS)

Vogel, Kristin; Schröter, Kai; Kreibich, Heidi; Thieken, Annegret; Müller, Meike; Sieg, Tobias; Laudan, Jonas; Kienzler, Sarah; Weise, Laura; Merz, Bruno; Scherbaum, Frank

2016-04-01

Flood risk is a function of hazard as well as of exposure and vulnerability. All three components are under change over space and time and have to be considered for reliable damage estimations and risk analyses, since this is the basis for an efficient, adaptable risk management. Hitherto, models for estimating flood damage are comparatively simple and cannot sufficiently account for changing conditions. The Bayesian network approach allows for a multivariate modeling of complex systems without relying on expert knowledge about physical constraints. In a Bayesian network each model component is considered to be a random variable. The way of interactions between those variables can be learned from observations or be defined by expert knowledge. Even a combination of both is possible. Moreover, the probabilistic framework captures uncertainties related to the prediction and provides a probability distribution for the damage instead of a point estimate. The graphical representation of Bayesian networks helps to study the change of probabilities for changing circumstances and may thus simplify the communication between scientists and public authorities. In the framework of the DFG-Research Training Group "NatRiskChange" we aim to develop Bayesian networks for flood damage and vulnerability assessments of residential buildings and companies under changing conditions. A Bayesian network learned from data, collected over the last 15 years in flooded regions in the Elbe and Danube catchments (Germany), reveals the impact of many variables like building characteristics, precaution and warning situation on flood damage to residential buildings. While the handling of incomplete and hybrid (discrete mixed with continuous) data are the most challenging issues in the study on residential buildings, a similar study, that focuses on the vulnerability of small to medium sized companies, bears new challenges. Relying on a much smaller data set for the determination of the model parameters, overly complex models should be avoided. A so called Markov Blanket approach aims at the identification of the most relevant factors and constructs a Bayesian network based on those findings. With our approach we want to exploit a major advantage of Bayesian networks which is their ability to consider dependencies not only pairwise, but to capture the joint effects and interactions of driving forces. Hence, the flood damage network does not only show the impact of precaution on the building damage separately, but also reveals the mutual effects of precaution and the quality of warning for a variety of flood settings. Thus, it allows for a consideration of changing conditions and different courses of action and forms a novel and valuable tool for decision support. This study is funded by the Deutsche Forschungsgemeinschaft (DFG) within the research training program GRK 2043/1 "NatRiskChange - Natural hazards and risks in a changing world" at the University of Potsdam.

The Calculus of Uncertainty in Artificial Intelligence and Expert Systems. Proceedings of a Conference Held on December 28-29, 1984.

DTIC Science & Technology

1986-01-15

within that - group , has led naturally to the study of the possible role of belief functions in medicine (Gordon & Shortliffe, 1984). Much attention is...Street ,- Washington, DC 20052 Arlington, VA 22217 HENRY SOLOMON BEN P. WISE Graduate School of Arts & Carnegie-Mellon University Sciences Robotics...Bayesian methods and place the theory of belief functions in this iii--’ historical context. Sections 3 studies some strands of the development within

Population forecasts for Bangladesh, using a Bayesian methodology.

PubMed

Mahsin, Md; Hossain, Syed Shahadat

2012-12-01

Population projection for many developing countries could be quite a challenging task for the demographers mostly due to lack of availability of enough reliable data. The objective of this paper is to present an overview of the existing methods for population forecasting and to propose an alternative based on the Bayesian statistics, combining the formality of inference. The analysis has been made using Markov Chain Monte Carlo (MCMC) technique for Bayesian methodology available with the software WinBUGS. Convergence diagnostic techniques available with the WinBUGS software have been applied to ensure the convergence of the chains necessary for the implementation of MCMC. The Bayesian approach allows for the use of observed data and expert judgements by means of appropriate priors, and a more realistic population forecasts, along with associated uncertainty, has been possible.

Scientific expertise and the Athlete Biological Passport: 3 years of experience.

PubMed

Schumacher, Yorck Olaf; d'Onofrio, Giuseppe

2012-06-01

Expert evaluation of biological data is a key component of the Athlete Biological Passport approach in the fight against doping. The evaluation consists of a longitudinal assessment of biological variables to determine the probability of the data being physiological on the basis of the athlete's on own previous values (performed by an automated software system using a Bayesian model) and a subjective evaluation of the results in view of possible causes (performed by experts). The role of the expert is therefore a key component in the process. Experts should be qualified to evaluate the data regarding possible explanations related to the influence of doping products and methods, analytical issues, and the influence of exercise or pathological conditions. The evaluation provides a scientific basis for the decision taken by a disciplinary panel. This evaluation should therefore encompass and balance all possible causes for a given blood profile and provide a likelihood for potential scenarios (pathology, normal variation, doping) that might have caused the pattern. It should comply with the standards for the evaluation of scientific evidence in forensics. On the basis of their evaluation of profiles, experts might provide assistance in planning appropriate target testing schemes.

Comparing models for quantitative risk assessment: an application to the European Registry of foreign body injuries in children.

PubMed

Berchialla, Paola; Scarinzi, Cecilia; Snidero, Silvia; Gregori, Dario

2016-08-01

Risk Assessment is the systematic study of decisions subject to uncertain consequences. An increasing interest has been focused on modeling techniques like Bayesian Networks since their capability of (1) combining in the probabilistic framework different type of evidence including both expert judgments and objective data; (2) overturning previous beliefs in the light of the new information being received and (3) making predictions even with incomplete data. In this work, we proposed a comparison among Bayesian Networks and other classical Quantitative Risk Assessment techniques such as Neural Networks, Classification Trees, Random Forests and Logistic Regression models. Hybrid approaches, combining both Classification Trees and Bayesian Networks, were also considered. Among Bayesian Networks, a clear distinction between purely data-driven approach and combination of expert knowledge with objective data is made. The aim of this paper consists in evaluating among this models which best can be applied, in the framework of Quantitative Risk Assessment, to assess the safety of children who are exposed to the risk of inhalation/insertion/aspiration of consumer products. The issue of preventing injuries in children is of paramount importance, in particular where product design is involved: quantifying the risk associated to product characteristics can be of great usefulness in addressing the product safety design regulation. Data of the European Registry of Foreign Bodies Injuries formed the starting evidence for risk assessment. Results showed that Bayesian Networks appeared to have both the ease of interpretability and accuracy in making prediction, even if simpler models like logistic regression still performed well. © The Author(s) 2013.

Space Shuttle RTOS Bayesian Network

NASA Technical Reports Server (NTRS)

Morris, A. Terry; Beling, Peter A.

2001-01-01

With shrinking budgets and the requirements to increase reliability and operational life of the existing orbiter fleet, NASA has proposed various upgrades for the Space Shuttle that are consistent with national space policy. The cockpit avionics upgrade (CAU), a high priority item, has been selected as the next major upgrade. The primary functions of cockpit avionics include flight control, guidance and navigation, communication, and orbiter landing support. Secondary functions include the provision of operational services for non-avionics systems such as data handling for the payloads and caution and warning alerts to the crew. Recently, a process to selection the optimal commercial-off-the-shelf (COTS) real-time operating system (RTOS) for the CAU was conducted by United Space Alliance (USA) Corporation, which is a joint venture between Boeing and Lockheed Martin, the prime contractor for space shuttle operations. In order to independently assess the RTOS selection, NASA has used the Bayesian network-based scoring methodology described in this paper. Our two-stage methodology addresses the issue of RTOS acceptability by incorporating functional, performance and non-functional software measures related to reliability, interoperability, certifiability, efficiency, correctness, business, legal, product history, cost and life cycle. The first stage of the methodology involves obtaining scores for the various measures using a Bayesian network. The Bayesian network incorporates the causal relationships between the various and often competing measures of interest while also assisting the inherently complex decision analysis process with its ability to reason under uncertainty. The structure and selection of prior probabilities for the network is extracted from experts in the field of real-time operating systems. Scores for the various measures are computed using Bayesian probability. In the second stage, multi-criteria trade-off analyses are performed between the scores. Using a prioritization of measures from the decision-maker, trade-offs between the scores are used to rank order the available set of RTOS candidates.

A novel method for expediting the development of patient-reported outcome measures and an evaluation across several populations

PubMed Central

Garrard, Lili; Price, Larry R.; Bott, Marjorie J.; Gajewski, Byron J.

2016-01-01

Item response theory (IRT) models provide an appropriate alternative to the classical ordinal confirmatory factor analysis (CFA) during the development of patient-reported outcome measures (PROMs). Current literature has identified the assessment of IRT model fit as both challenging and underdeveloped (Sinharay & Johnson, 2003; Sinharay, Johnson, & Stern, 2006). This study evaluates the performance of Ordinal Bayesian Instrument Development (OBID), a Bayesian IRT model with a probit link function approach, through applications in two breast cancer-related instrument development studies. The primary focus is to investigate an appropriate method for comparing Bayesian IRT models in PROMs development. An exact Bayesian leave-one-out cross-validation (LOO-CV) approach (Vehtari & Lampinen, 2002) is implemented to assess prior selection for the item discrimination parameter in the IRT model and subject content experts’ bias (in a statistical sense and not to be confused with psychometric bias as in differential item functioning) toward the estimation of item-to-domain correlations. Results support the utilization of content subject experts’ information in establishing evidence for construct validity when sample size is small. However, the incorporation of subject experts’ content information in the OBID approach can be sensitive to the level of expertise of the recruited experts. More stringent efforts need to be invested in the appropriate selection of subject experts to efficiently use the OBID approach and reduce potential bias during PROMs development. PMID:27667878

Exposure Models for the Prior Distribution in Bayesian Decision Analysis for Occupational Hygiene Decision Making

PubMed Central

Lee, Eun Gyung; Kim, Seung Won; Feigley, Charles E.; Harper, Martin

2015-01-01

This study introduces two semi-quantitative methods, Structured Subjective Assessment (SSA) and Control of Substances Hazardous to Health (COSHH) Essentials, in conjunction with two-dimensional Monte Carlo simulations for determining prior probabilities. Prior distribution using expert judgment was included for comparison. Practical applications of the proposed methods were demonstrated using personal exposure measurements of isoamyl acetate in an electronics manufacturing facility and of isopropanol in a printing shop. Applicability of these methods in real workplaces was discussed based on the advantages and disadvantages of each method. Although these methods could not be completely independent of expert judgments, this study demonstrated a methodological improvement in the estimation of the prior distribution for the Bayesian decision analysis tool. The proposed methods provide a logical basis for the decision process by considering determinants of worker exposure. PMID:23252451

A Bayesian approach for calibrating probability judgments

NASA Astrophysics Data System (ADS)

Firmino, Paulo Renato A.; Santana, Nielson A.

2012-10-01

Eliciting experts' opinions has been one of the main alternatives for addressing paucity of data. In the vanguard of this area is the development of calibration models (CMs). CMs are models dedicated to overcome miscalibration, i.e. judgment biases reflecting deficient strategies of reasoning adopted by the expert when inferring about an unknown. One of the main challenges of CMs is to determine how and when to intervene against miscalibration, in order to enhance the tradeoff between costs (time spent with calibration processes) and accuracy of the resulting models. The current paper dedicates special attention to this issue by presenting a dynamic Bayesian framework for monitoring, diagnosing, and handling miscalibration patterns. The framework is based on Beta-, Uniform, or Triangular-Bernoulli models and classes of judgmental calibration theories. Issues regarding the usefulness of the proposed framework are discussed and illustrated via simulation studies.

Bayesian Networks for enterprise risk assessment

NASA Astrophysics Data System (ADS)

Bonafede, C. E.; Giudici, P.

2007-08-01

According to different typologies of activity and priority, risks can assume diverse meanings and it can be assessed in different ways. Risk, in general, is measured in terms of a probability combination of an event (frequency) and its consequence (impact). To estimate the frequency and the impact (severity) historical data or expert opinions (either qualitative or quantitative data) are used. Moreover, qualitative data must be converted in numerical values or bounds to be used in the model. In the case of enterprise risk assessment the considered risks are, for instance, strategic, operational, legal and of image, which many times are difficult to be quantified. So in most cases only expert data, gathered by scorecard approaches, are available for risk analysis. The Bayesian Networks (BNs) are a useful tool to integrate different information and in particular to study the risk's joint distribution by using data collected from experts. In this paper we want to show a possible approach for building a BN in the particular case in which only prior probabilities of node states and marginal correlations between nodes are available, and when the variables have only two states.

Proceedings of the Seventh International Symposium on Methodologies for Intelligent Systems (Poster Session)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harber, K.S.

1993-05-01

This report contains the following papers: Implications in vivid logic; a self-learning bayesian expert system; a natural language generation system for a heterogeneous distributed database system; competence-switching'' managed by intelligent systems; strategy acquisition by an artificial neural network: Experiments in learning to play a stochastic game; viewpoints and selective inheritance in object-oriented modeling; multivariate discretization of continuous attributes for machine learning; utilization of the case-based reasoning method to resolve dynamic problems; formalization of an ontology of ceramic science in CLASSIC; linguistic tools for intelligent systems; an application of rough sets in knowledge synthesis; and a relational model for imprecise queries.more » These papers have been indexed separately.« less

Proceedings of the Seventh International Symposium on Methodologies for Intelligent Systems (Poster Session)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harber, K.S.

1993-05-01

This report contains the following papers: Implications in vivid logic; a self-learning Bayesian Expert System; a natural language generation system for a heterogeneous distributed database system; ``competence-switching`` managed by intelligent systems; strategy acquisition by an artificial neural network: Experiments in learning to play a stochastic game; viewpoints and selective inheritance in object-oriented modeling; multivariate discretization of continuous attributes for machine learning; utilization of the case-based reasoning method to resolve dynamic problems; formalization of an ontology of ceramic science in CLASSIC; linguistic tools for intelligent systems; an application of rough sets in knowledge synthesis; and a relational model for imprecise queries.more » These papers have been indexed separately.« less

A Bayesian Framework for Analysis of Pseudo-Spatial Models of Comparable Engineered Systems with Application to Spacecraft Anomaly Prediction Based on Precedent Data

NASA Astrophysics Data System (ADS)

Ndu, Obibobi Kamtochukwu

To ensure that estimates of risk and reliability inform design and resource allocation decisions in the development of complex engineering systems, early engagement in the design life cycle is necessary. An unfortunate constraint on the accuracy of such estimates at this stage of concept development is the limited amount of high fidelity design and failure information available on the actual system under development. Applying the human ability to learn from experience and augment our state of knowledge to evolve better solutions mitigates this limitation. However, the challenge lies in formalizing a methodology that takes this highly abstract, but fundamentally human cognitive, ability and extending it to the field of risk analysis while maintaining the tenets of generalization, Bayesian inference, and probabilistic risk analysis. We introduce an integrated framework for inferring the reliability, or other probabilistic measures of interest, of a new system or a conceptual variant of an existing system. Abstractly, our framework is based on learning from the performance of precedent designs and then applying the acquired knowledge, appropriately adjusted based on degree of relevance, to the inference process. This dissertation presents a method for inferring properties of the conceptual variant using a pseudo-spatial model that describes the spatial configuration of the family of systems to which the concept belongs. Through non-metric multidimensional scaling, we formulate the pseudo-spatial model based on rank-ordered subjective expert perception of design similarity between systems that elucidate the psychological space of the family. By a novel extension of Kriging methods for analysis of geospatial data to our "pseudo-space of comparable engineered systems", we develop a Bayesian inference model that allows prediction of the probabilistic measure of interest.

Potential uses of Bayesian networks as tools for synthesis of systematic reviews of complex interventions.

PubMed

Stewart, G B; Mengersen, K; Meader, N

2014-03-01

Bayesian networks (BNs) are tools for representing expert knowledge or evidence. They are especially useful for synthesising evidence or belief concerning a complex intervention, assessing the sensitivity of outcomes to different situations or contextual frameworks and framing decision problems that involve alternative types of intervention. Bayesian networks are useful extensions to logic maps when initiating a review or to facilitate synthesis and bridge the gap between evidence acquisition and decision-making. Formal elicitation techniques allow development of BNs on the basis of expert opinion. Such applications are useful alternatives to 'empty' reviews, which identify knowledge gaps but fail to support decision-making. Where review evidence exists, it can inform the development of a BN. We illustrate the construction of a BN using a motivating example that demonstrates how BNs can ensure coherence, transparently structure the problem addressed by a complex intervention and assess sensitivity to context, all of which are critical components of robust reviews of complex interventions. We suggest that BNs should be utilised to routinely synthesise reviews of complex interventions or empty reviews where decisions must be made despite poor evidence. Copyright © 2013 John Wiley & Sons, Ltd.

A Bayesian Machine Learning Model for Estimating Building Occupancy from Open Source Data

DOE PAGES

Stewart, Robert N.; Urban, Marie L.; Duchscherer, Samantha E.; ...

2016-01-01

Understanding building occupancy is critical to a wide array of applications including natural hazards loss analysis, green building technologies, and population distribution modeling. Due to the expense of directly monitoring buildings, scientists rely in addition on a wide and disparate array of ancillary and open source information including subject matter expertise, survey data, and remote sensing information. These data are fused using data harmonization methods which refer to a loose collection of formal and informal techniques for fusing data together to create viable content for building occupancy estimation. In this paper, we add to the current state of the artmore » by introducing the Population Data Tables (PDT), a Bayesian based informatics system for systematically arranging data and harmonization techniques into a consistent, transparent, knowledge learning framework that retains in the final estimation uncertainty emerging from data, expert judgment, and model parameterization. PDT probabilistically estimates ambient occupancy in units of people/1000ft2 for over 50 building types at the national and sub-national level with the goal of providing global coverage. The challenge of global coverage led to the development of an interdisciplinary geospatial informatics system tool that provides the framework for capturing, storing, and managing open source data, handling subject matter expertise, carrying out Bayesian analytics as well as visualizing and exporting occupancy estimation results. We present the PDT project, situate the work within the larger community, and report on the progress of this multi-year project.Understanding building occupancy is critical to a wide array of applications including natural hazards loss analysis, green building technologies, and population distribution modeling. Due to the expense of directly monitoring buildings, scientists rely in addition on a wide and disparate array of ancillary and open source information including subject matter expertise, survey data, and remote sensing information. These data are fused using data harmonization methods which refer to a loose collection of formal and informal techniques for fusing data together to create viable content for building occupancy estimation. In this paper, we add to the current state of the art by introducing the Population Data Tables (PDT), a Bayesian model and informatics system for systematically arranging data and harmonization techniques into a consistent, transparent, knowledge learning framework that retains in the final estimation uncertainty emerging from data, expert judgment, and model parameterization. PDT probabilistically estimates ambient occupancy in units of people/1000 ft 2 for over 50 building types at the national and sub-national level with the goal of providing global coverage. The challenge of global coverage led to the development of an interdisciplinary geospatial informatics system tool that provides the framework for capturing, storing, and managing open source data, handling subject matter expertise, carrying out Bayesian analytics as well as visualizing and exporting occupancy estimation results. We present the PDT project, situate the work within the larger community, and report on the progress of this multi-year project.« less

Bayesian nonparametric adaptive control using Gaussian processes.

PubMed

Chowdhary, Girish; Kingravi, Hassan A; How, Jonathan P; Vela, Patricio A

2015-03-01

Most current model reference adaptive control (MRAC) methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed a priori, often through expert judgment. An example of such an adaptive element is radial basis function networks (RBFNs), with RBF centers preallocated based on the expected operating domain. If the system operates outside of the expected operating domain, this adaptive element can become noneffective in capturing and canceling the uncertainty, thus rendering the adaptive controller only semiglobal in nature. This paper investigates a Gaussian process-based Bayesian MRAC architecture (GP-MRAC), which leverages the power and flexibility of GP Bayesian nonparametric models of uncertainty. The GP-MRAC does not require the centers to be preallocated, can inherently handle measurement noise, and enables MRAC to handle a broader set of uncertainties, including those that are defined as distributions over functions. We use stochastic stability arguments to show that GP-MRAC guarantees good closed-loop performance with no prior domain knowledge of the uncertainty. Online implementable GP inference methods are compared in numerical simulations against RBFN-MRAC with preallocated centers and are shown to provide better tracking and improved long-term learning.

Second-hand market as an alternative in reverse logistics

NASA Astrophysics Data System (ADS)

Pochampally, Kishore K.; Gupta, Surendra M.

2004-02-01

Collectors of discarded products seldom know when those products were bought and why they are discarded. Also, the products do not indicate their remaining life periods. So, it is difficult to decide if it is "sensible" to repair (if necessary) a particular product for subsequent sale on the second-hand market or to disassemble it partially or completely for subsequent remanufacture and/or recycle. To this end, we build an expert system using Bayesian updating process and fuzzy set theory, to aid such decision-making. A numerical example demonstrates the building approach.

Parameter Estimation of Partial Differential Equation Models.

PubMed

Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Carroll, Raymond J; Maity, Arnab

2013-01-01

Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown, and need to be estimated from the measurements of the dynamic system in the present of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE, and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from LIDAR data.

Machine Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.

The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networksmore » and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.« less

Support vector machines

NASA Technical Reports Server (NTRS)

Garay, Michael J.; Mazzoni, Dominic; Davies, Roger; Wagstaff, Kiri

2004-01-01

Support Vector Machines (SVMs) are a type of supervised learning algorith,, other examples of which are Artificial Neural Networks (ANNs), Decision Trees, and Naive Bayesian Classifiers. Supervised learning algorithms are used to classify objects labled by a 'supervisor' - typically a human 'expert.'.

Aviation Safety Risk Modeling: Lessons Learned From Multiple Knowledge Elicitation Sessions

NASA Technical Reports Server (NTRS)

Luxhoj, J. T.; Ancel, E.; Green, L. L.; Shih, A. T.; Jones, S. M.; Reveley, M. S.

2014-01-01

Aviation safety risk modeling has elements of both art and science. In a complex domain, such as the National Airspace System (NAS), it is essential that knowledge elicitation (KE) sessions with domain experts be performed to facilitate the making of plausible inferences about the possible impacts of future technologies and procedures. This study discusses lessons learned throughout the multiple KE sessions held with domain experts to construct probabilistic safety risk models for a Loss of Control Accident Framework (LOCAF), FLightdeck Automation Problems (FLAP), and Runway Incursion (RI) mishap scenarios. The intent of these safety risk models is to support a portfolio analysis of NASA's Aviation Safety Program (AvSP). These models use the flexible, probabilistic approach of Bayesian Belief Networks (BBNs) and influence diagrams to model the complex interactions of aviation system risk factors. Each KE session had a different set of experts with diverse expertise, such as pilot, air traffic controller, certification, and/or human factors knowledge that was elicited to construct a composite, systems-level risk model. There were numerous "lessons learned" from these KE sessions that deal with behavioral aggregation, conditional probability modeling, object-oriented construction, interpretation of the safety risk results, and model verification/validation that are presented in this paper.

Capturing Ecosystem Services, Stakeholders' Preferences and Trade-Offs in Coastal Aquaculture Decisions: A Bayesian Belief Network Application

PubMed Central

Schmitt, Laetitia Helene Marie; Brugere, Cecile

2013-01-01

Aquaculture activities are embedded in complex social-ecological systems. However, aquaculture development decisions have tended to be driven by revenue generation, failing to account for interactions with the environment and the full value of the benefits derived from services provided by local ecosystems. Trade-offs resulting from changes in ecosystem services provision and associated impacts on livelihoods are also often overlooked. This paper proposes an innovative application of Bayesian belief networks - influence diagrams - as a decision support system for mediating trade-offs arising from the development of shrimp aquaculture in Thailand. Senior experts were consulted (n = 12) and primary farm data on the economics of shrimp farming (n = 20) were collected alongside secondary information on ecosystem services, in order to construct and populate the network. Trade-offs were quantitatively assessed through the generation of a probabilistic impact matrix. This matrix captures nonlinearity and uncertainty and describes the relative performance and impacts of shrimp farming management scenarios on local livelihoods. It also incorporates export revenues and provision and value of ecosystem services such as coastal protection and biodiversity. This research shows that Bayesian belief modeling can support complex decision-making on pathways for sustainable coastal aquaculture development and thus contributes to the debate on the role of aquaculture in social-ecological resilience and economic development. PMID:24155876

Combining Bayesian Networks and Agent Based Modeling to develop a decision-support model in Vietnam

NASA Astrophysics Data System (ADS)

Nong, Bao Anh; Ertsen, Maurits; Schoups, Gerrit

2016-04-01

Complexity and uncertainty in natural resources management have been focus themes in recent years. Within these debates, with the aim to define an approach feasible for water management practice, we are developing an integrated conceptual modeling framework for simulating decision-making processes of citizens, in our case in the Day river area, Vietnam. The model combines Bayesian Networks (BNs) and Agent-Based Modeling (ABM). BNs are able to combine both qualitative data from consultants / experts / stakeholders, and quantitative data from observations on different phenomena or outcomes from other models. Further strengths of BNs are that the relationship between variables in the system is presented in a graphical interface, and that components of uncertainty are explicitly related to their probabilistic dependencies. A disadvantage is that BNs cannot easily identify the feedback of agents in the system once changes appear. Hence, ABM was adopted to represent the reaction among stakeholders under changes. The modeling framework is developed as an attempt to gain better understanding about citizen's behavior and factors influencing their decisions in order to reduce uncertainty in the implementation of water management policy.

Probabilistic expert systems for forensic inference from DNA markers in horses: applications to confirm genealogies with lack of genetic data.

PubMed

Dobosz, Marina; Bocci, Chiara; Bonuglia, Margherita; Grasso, Cinzia; Merigioli, Sara; Russo, Alessandra; De Iuliis, Paolo

2010-01-01

Microsatellites have been used for parentage testing and individual identification in forensic science because they are highly polymorphic and show abundant sequences dispersed throughout most eukaryotic nuclear genomes. At present, genetic testing based on DNA technology is used for most domesticated animals, including horses, to confirm identity, to determine parentage, and to validate registration certificates. But if genetic data of one of the putative parents are missing, verifying a genealogy could be questionable. The aim of this paper is to illustrate a new approach to analyze complex cases of disputed relationship with microsatellites markers. These cases were solved by analyzing the genotypes of the offspring and other horses' genotypes in the pedigrees of the putative dam/sire with probabilistic expert systems (PESs). PES was especially efficient in supplying reliable, error-free Bayesian probabilities in complex cases with missing pedigree data. One of these systems was developed for forensic purposes (FINEX program) and is particularly valuable in human analyses. We applied this program to parentage analysis in horses, and we will illustrate how different cases have been successfully worked out.

The weighted priors approach for combining expert opinions in logistic regression experiments

DOE PAGES

Quinlan, Kevin R.; Anderson-Cook, Christine M.; Myers, Kary L.

2017-04-24

When modeling the reliability of a system or component, it is not uncommon for more than one expert to provide very different prior estimates of the expected reliability as a function of an explanatory variable such as age or temperature. Our goal in this paper is to incorporate all information from the experts when choosing a design about which units to test. Bayesian design of experiments has been shown to be very successful for generalized linear models, including logistic regression models. We use this approach to develop methodology for the case where there are several potentially non-overlapping priors under consideration.more » While multiple priors have been used for analysis in the past, they have never been used in a design context. The Weighted Priors method performs well for a broad range of true underlying model parameter choices and is more robust when compared to other reasonable design choices. Finally, we illustrate the method through multiple scenarios and a motivating example. Additional figures for this article are available in the online supplementary information.« less

The weighted priors approach for combining expert opinions in logistic regression experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Quinlan, Kevin R.; Anderson-Cook, Christine M.; Myers, Kary L.

When modeling the reliability of a system or component, it is not uncommon for more than one expert to provide very different prior estimates of the expected reliability as a function of an explanatory variable such as age or temperature. Our goal in this paper is to incorporate all information from the experts when choosing a design about which units to test. Bayesian design of experiments has been shown to be very successful for generalized linear models, including logistic regression models. We use this approach to develop methodology for the case where there are several potentially non-overlapping priors under consideration.more » While multiple priors have been used for analysis in the past, they have never been used in a design context. The Weighted Priors method performs well for a broad range of true underlying model parameter choices and is more robust when compared to other reasonable design choices. Finally, we illustrate the method through multiple scenarios and a motivating example. Additional figures for this article are available in the online supplementary information.« less

Aminoglycoside Therapy Manager: An Advanced Computer Program for Decision Support for Drug Dosing and Therapeutic Monitoring

PubMed Central

Lenert, Leslie; Lurie, Jon; Coleman, Robert; Klosterman, Heidrun; Blaschke, Terrence

1990-01-01

In this paper, we will describe an advanced drug dosing program, Aminoglycoside Therapy Manager that reasons using Bayesian pharmacokinetic modeling and symbolic modeling of patient status and drug response. Our design is similar to the design of the Digitalis Therapy Advisor program, but extends previous work by incorporating a Bayesian pharmacokinetic model, a “meta-level” analysis of drug concentrations to identify sampling errors and changes in pharmacokinetics, and including the results of the “meta-level” analysis in reasoning for dosing and therapeutic monitoring recommendations. The program is user friendly and runs on low cost general-purpose hardware. Validation studies show that the program is as accurate in predicting future drug concentrations as an expert using commercial Bayesian forecasting software.

Risk analysis with a fuzzy-logic approach of a complex installation

NASA Astrophysics Data System (ADS)

Peikert, Tim; Garbe, Heyno; Potthast, Stefan

2016-09-01

This paper introduces a procedural method based on fuzzy logic to analyze systematic the risk of an electronic system in an intentional electromagnetic environment (IEME). The method analyzes the susceptibility of a complex electronic installation with respect to intentional electromagnetic interference (IEMI). It combines the advantages of well-known techniques as fault tree analysis (FTA), electromagnetic topology (EMT) and Bayesian networks (BN) and extends the techniques with an approach to handle uncertainty. This approach uses fuzzy sets, membership functions and fuzzy logic to handle the uncertainty with probability functions and linguistic terms. The linguistic terms add to the risk analysis the knowledge from experts of the investigated system or environment.

Performance Evaluation of Expert Team Members

DTIC Science & Technology

2007-06-18

perspectives on organ donation: The reason for the shortage. Colloquium presented at the Department of Nursing and Midwifery , University of Stirling...November 2005. Weiss, J. W., Weiss, D. J., & Edwards, W. Sex, drugs , and gluttony: Everyday options. Paper presented at the Bayesian Research Conference

Prevalence of Neuropathic Pain in Cancer Patients: Pooled Estimates From a Systematic Review of Published Literature and Results From a Survey Conducted in 50 Italian Palliative Care Centers.

PubMed

Roberto, Anna; Deandrea, Silvia; Greco, Maria Teresa; Corli, Oscar; Negri, Eva; Pizzuto, Massimo; Ruggeri, Fabrizio

2016-06-01

Because of the increasing body of literature on neuropathic cancer pain (NCP), an accurate estimate of its prevalence requires recurring updates. To provide this estimate using information from a systematic review and a survey. Using MEDLINE, Embase, and a previous review, we searched for studies published up to 2014 reporting data on NCP prevalence in adult cancer populations. Pooled prevalence rates from observational prospective studies were computed. The association between NCP prevalence and possible predictors was investigated for oncology and palliative settings. Prevalence rates were extracted from a questionnaire answered by 137 physicians working in 50 Italian centers of palliative care. Estimates from studies conducted in palliative settings and from the experts were analyzed separately and eventually pooled with an informative Bayesian random-effect model. Twenty-nine observational studies were identified. The overall pooled prevalence was 31.2%, with high heterogeneity; similar figures were observed when oncology and palliative settings were individually considered. A slightly higher prevalence of NCP was detected for hospice/inpatients as compared to outpatients, in both settings. The mean NCP prevalence reported by the survey experts was 44.2%; the pooled Bayesian estimate for the palliative setting corresponded to 43.0% (95% CI: 40.0-46.0). The subgroup with the lowest heterogeneity and where the literature and experts' estimates were closest is hospice/inpatients, with a pooled Bayesian prevalence rate of 34.9% (95% CI: 29.9-41.0). The systematic review and the survey suggest that more than one in three patients with cancer pain also experiences NCP. Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Basics of Bayesian methods.

PubMed

Ghosh, Sujit K

2010-01-01

Bayesian methods are rapidly becoming popular tools for making statistical inference in various fields of science including biology, engineering, finance, and genetics. One of the key aspects of Bayesian inferential method is its logical foundation that provides a coherent framework to utilize not only empirical but also scientific information available to a researcher. Prior knowledge arising from scientific background, expert judgment, or previously collected data is used to build a prior distribution which is then combined with current data via the likelihood function to characterize the current state of knowledge using the so-called posterior distribution. Bayesian methods allow the use of models of complex physical phenomena that were previously too difficult to estimate (e.g., using asymptotic approximations). Bayesian methods offer a means of more fully understanding issues that are central to many practical problems by allowing researchers to build integrated models based on hierarchical conditional distributions that can be estimated even with limited amounts of data. Furthermore, advances in numerical integration methods, particularly those based on Monte Carlo methods, have made it possible to compute the optimal Bayes estimators. However, there is a reasonably wide gap between the background of the empirically trained scientists and the full weight of Bayesian statistical inference. Hence, one of the goals of this chapter is to bridge the gap by offering elementary to advanced concepts that emphasize linkages between standard approaches and full probability modeling via Bayesian methods.

Discussion of “Bayesian design of experiments for industrial and scientific applications via gaussian processes”

DOE PAGES

Anderson-Cook, Christine M.; Burke, Sarah E.

2016-10-18

First, we would like to commend Dr. Woods on his thought-provoking paper and insightful presentation at the 4th Annual Stu Hunter conference. We think that the material presented highlights some important needs in the area of design of experiments for generalized linear models (GLMs). In addition, we agree with Dr. Woods that design of experiements of GLMs does implicitly require expert judgement about model parameters, and hence using a Bayesian approach to capture this knowledge is a natural strategy to summarize what is known with the opportunity to incorporate associated uncertainty about that information.

Discussion of “Bayesian design of experiments for industrial and scientific applications via gaussian processes”

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson-Cook, Christine M.; Burke, Sarah E.

First, we would like to commend Dr. Woods on his thought-provoking paper and insightful presentation at the 4th Annual Stu Hunter conference. We think that the material presented highlights some important needs in the area of design of experiments for generalized linear models (GLMs). In addition, we agree with Dr. Woods that design of experiements of GLMs does implicitly require expert judgement about model parameters, and hence using a Bayesian approach to capture this knowledge is a natural strategy to summarize what is known with the opportunity to incorporate associated uncertainty about that information.

Diagnosis of combined faults in Rotary Machinery by Non-Naive Bayesian approach

NASA Astrophysics Data System (ADS)

Asr, Mahsa Yazdanian; Ettefagh, Mir Mohammad; Hassannejad, Reza; Razavi, Seyed Naser

2017-02-01

When combined faults happen in different parts of the rotating machines, their features are profoundly dependent. Experts are completely familiar with individuals faults characteristics and enough data are available from single faults but the problem arises, when the faults combined and the separation of characteristics becomes complex. Therefore, the experts cannot declare exact information about the symptoms of combined fault and its quality. In this paper to overcome this drawback, a novel method is proposed. The core idea of the method is about declaring combined fault without using combined fault features as training data set and just individual fault features are applied in training step. For this purpose, after data acquisition and resampling the obtained vibration signals, Empirical Mode Decomposition (EMD) is utilized to decompose multi component signals to Intrinsic Mode Functions (IMFs). With the use of correlation coefficient, proper IMFs for feature extraction are selected. In feature extraction step, Shannon energy entropy of IMFs was extracted as well as statistical features. It is obvious that most of extracted features are strongly dependent. To consider this matter, Non-Naive Bayesian Classifier (NNBC) is appointed, which release the fundamental assumption of Naive Bayesian, i.e., the independence among features. To demonstrate the superiority of NNBC, other counterpart methods, include Normal Naive Bayesian classifier, Kernel Naive Bayesian classifier and Back Propagation Neural Networks were applied and the classification results are compared. An experimental vibration signals, collected from automobile gearbox, were used to verify the effectiveness of the proposed method. During the classification process, only the features, related individually to healthy state, bearing failure and gear failures, were assigned for training the classifier. But, combined fault features (combined gear and bearing failures) were examined as test data. The achieved probabilities for the test data show that the combined fault can be identified with high success rate.

Objectified quantification of uncertainties in Bayesian atmospheric inversions

NASA Astrophysics Data System (ADS)

Berchet, A.; Pison, I.; Chevallier, F.; Bousquet, P.; Bonne, J.-L.; Paris, J.-D.

2015-05-01

Classical Bayesian atmospheric inversions process atmospheric observations and prior emissions, the two being connected by an observation operator picturing mainly the atmospheric transport. These inversions rely on prescribed errors in the observations, the prior emissions and the observation operator. When data pieces are sparse, inversion results are very sensitive to the prescribed error distributions, which are not accurately known. The classical Bayesian framework experiences difficulties in quantifying the impact of mis-specified error distributions on the optimized fluxes. In order to cope with this issue, we rely on recent research results to enhance the classical Bayesian inversion framework through a marginalization on a large set of plausible errors that can be prescribed in the system. The marginalization consists in computing inversions for all possible error distributions weighted by the probability of occurrence of the error distributions. The posterior distribution of the fluxes calculated by the marginalization is not explicitly describable. As a consequence, we carry out a Monte Carlo sampling based on an approximation of the probability of occurrence of the error distributions. This approximation is deduced from the well-tested method of the maximum likelihood estimation. Thus, the marginalized inversion relies on an automatic objectified diagnosis of the error statistics, without any prior knowledge about the matrices. It robustly accounts for the uncertainties on the error distributions, contrary to what is classically done with frozen expert-knowledge error statistics. Some expert knowledge is still used in the method for the choice of an emission aggregation pattern and of a sampling protocol in order to reduce the computation cost. The relevance and the robustness of the method is tested on a case study: the inversion of methane surface fluxes at the mesoscale with virtual observations on a realistic network in Eurasia. Observing system simulation experiments are carried out with different transport patterns, flux distributions and total prior amounts of emitted methane. The method proves to consistently reproduce the known "truth" in most cases, with satisfactory tolerance intervals. Additionally, the method explicitly provides influence scores and posterior correlation matrices. An in-depth interpretation of the inversion results is then possible. The more objective quantification of the influence of the observations on the fluxes proposed here allows us to evaluate the impact of the observation network on the characterization of the surface fluxes. The explicit correlations between emission aggregates reveal the mis-separated regions, hence the typical temporal and spatial scales the inversion can analyse. These scales are consistent with the chosen aggregation patterns.

Bayesian Peptide Peak Detection for High Resolution TOF Mass Spectrometry.

PubMed

Zhang, Jianqiu; Zhou, Xiaobo; Wang, Honghui; Suffredini, Anthony; Zhang, Lin; Huang, Yufei; Wong, Stephen

2010-11-01

In this paper, we address the issue of peptide ion peak detection for high resolution time-of-flight (TOF) mass spectrometry (MS) data. A novel Bayesian peptide ion peak detection method is proposed for TOF data with resolution of 10 000-15 000 full width at half-maximum (FWHW). MS spectra exhibit distinct characteristics at this resolution, which are captured in a novel parametric model. Based on the proposed parametric model, a Bayesian peak detection algorithm based on Markov chain Monte Carlo (MCMC) sampling is developed. The proposed algorithm is tested on both simulated and real datasets. The results show a significant improvement in detection performance over a commonly employed method. The results also agree with expert's visual inspection. Moreover, better detection consistency is achieved across MS datasets from patients with identical pathological condition.

Development of a Bayesian Belief Network Runway Incursion and Excursion Model

NASA Technical Reports Server (NTRS)

Green, Lawrence L.

2014-01-01

In a previous work, a statistical analysis of runway incursion (RI) event data was conducted to ascertain the relevance of this data to the top ten Technical Challenges (TC) of the National Aeronautics and Space Administration (NASA) Aviation Safety Program (AvSP). The study revealed connections to several of the AvSP top ten TC and identified numerous primary causes and contributing factors of RI events. The statistical analysis served as the basis for developing a system-level Bayesian Belief Network (BBN) model for RI events, also previously reported. Through literature searches and data analysis, this RI event network has now been extended to also model runway excursion (RE) events. These RI and RE event networks have been further modified and vetted by a Subject Matter Expert (SME) panel. The combined system-level BBN model will allow NASA to generically model the causes of RI and RE events and to assess the effectiveness of technology products being developed under NASA funding. These products are intended to reduce the frequency of runway safety incidents/accidents, and to improve runway safety in general. The development and structure of the BBN for both RI and RE events are documented in this paper.

BAYESIAN META-ANALYSIS ON MEDICAL DEVICES: APPLICATION TO IMPLANTABLE CARDIOVERTER DEFIBRILLATORS

PubMed Central

Youn, Ji-Hee; Lord, Joanne; Hemming, Karla; Girling, Alan; Buxton, Martin

2012-01-01

Objectives: The aim of this study is to describe and illustrate a method to obtain early estimates of the effectiveness of a new version of a medical device. Methods: In the absence of empirical data, expert opinion may be elicited on the expected difference between the conventional and modified devices. Bayesian Mixed Treatment Comparison (MTC) meta-analysis can then be used to combine this expert opinion with existing trial data on earlier versions of the device. We illustrate this approach for a new four-pole implantable cardioverter defibrillator (ICD) compared with conventional ICDs, Class III anti-arrhythmic drugs, and conventional drug therapy for the prevention of sudden cardiac death in high risk patients. Existing RCTs were identified from a published systematic review, and we elicited opinion on the difference between four-pole and conventional ICDs from experts recruited at a cardiology conference. Results: Twelve randomized controlled trials were identified. Seven experts provided valid probability distributions for the new ICDs compared with current devices. The MTC model resulted in estimated relative risks of mortality of 0.74 (0.60–0.89) (predictive relative risk [RR] = 0.77 [0.41–1.26]) and 0.83 (0.70–0.97) (predictive RR = 0.84 [0.55–1.22]) with the new ICD therapy compared to Class III anti-arrhythmic drug therapy and conventional drug therapy, respectively. These results showed negligible differences from the preliminary results for the existing ICDs. Conclusions: The proposed method incorporating expert opinion to adjust for a modification made to an existing device may play a useful role in assisting decision makers to make early informed judgments on the effectiveness of frequently modified healthcare technologies. PMID:22559753

A population-based tissue probability map-driven level set method for fully automated mammographic density estimations.

PubMed

Kim, Youngwoo; Hong, Byung Woo; Kim, Seung Ja; Kim, Jong Hyo

2014-07-01

A major challenge when distinguishing glandular tissues on mammograms, especially for area-based estimations, lies in determining a boundary on a hazy transition zone from adipose to glandular tissues. This stems from the nature of mammography, which is a projection of superimposed tissues consisting of different structures. In this paper, the authors present a novel segmentation scheme which incorporates the learned prior knowledge of experts into a level set framework for fully automated mammographic density estimations. The authors modeled the learned knowledge as a population-based tissue probability map (PTPM) that was designed to capture the classification of experts' visual systems. The PTPM was constructed using an image database of a selected population consisting of 297 cases. Three mammogram experts extracted regions for dense and fatty tissues on digital mammograms, which was an independent subset used to create a tissue probability map for each ROI based on its local statistics. This tissue class probability was taken as a prior in the Bayesian formulation and was incorporated into a level set framework as an additional term to control the evolution and followed the energy surface designed to reflect experts' knowledge as well as the regional statistics inside and outside of the evolving contour. A subset of 100 digital mammograms, which was not used in constructing the PTPM, was used to validate the performance. The energy was minimized when the initial contour reached the boundary of the dense and fatty tissues, as defined by experts. The correlation coefficient between mammographic density measurements made by experts and measurements by the proposed method was 0.93, while that with the conventional level set was 0.47. The proposed method showed a marked improvement over the conventional level set method in terms of accuracy and reliability. This result suggests that the proposed method successfully incorporated the learned knowledge of the experts' visual systems and has potential to be used as an automated and quantitative tool for estimations of mammographic breast density levels.

Spatial Bayesian belief networks as a planning decision tool for mapping ecosystem services trade-offs on forested landscapes.

PubMed

Gonzalez-Redin, Julen; Luque, Sandra; Poggio, Laura; Smith, Ron; Gimona, Alessandro

2016-01-01

An integrated methodology, based on linking Bayesian belief networks (BBN) with GIS, is proposed for combining available evidence to help forest managers evaluate implications and trade-offs between forest production and conservation measures to preserve biodiversity in forested habitats. A Bayesian belief network is a probabilistic graphical model that represents variables and their dependencies through specifying probabilistic relationships. In spatially explicit decision problems where it is difficult to choose appropriate combinations of interventions, the proposed integration of a BBN with GIS helped to facilitate shared understanding of the human-landscape relationships, while fostering collective management that can be incorporated into landscape planning processes. Trades-offs become more and more relevant in these landscape contexts where the participation of many and varied stakeholder groups is indispensable. With these challenges in mind, our integrated approach incorporates GIS-based data with expert knowledge to consider two different land use interests - biodiversity value for conservation and timber production potential - with the focus on a complex mountain landscape in the French Alps. The spatial models produced provided different alternatives of suitable sites that can be used by policy makers in order to support conservation priorities while addressing management options. The approach provided provide a common reasoning language among different experts from different backgrounds while helped to identify spatially explicit conflictive areas. Copyright © 2015 Elsevier Inc. All rights reserved.

Constructing a Bayesian network model for improving safety behavior of employees at workplaces.

PubMed

Mohammadfam, Iraj; Ghasemi, Fakhradin; Kalatpour, Omid; Moghimbeigi, Abbas

2017-01-01

Unsafe behavior increases the risk of accident at workplaces and needs to be managed properly. The aim of the present study was to provide a model for managing and improving safety behavior of employees using the Bayesian networks approach. The study was conducted in several power plant construction projects in Iran. The data were collected using a questionnaire composed of nine factors, including management commitment, supporting environment, safety management system, employees' participation, safety knowledge, safety attitude, motivation, resource allocation, and work pressure. In order for measuring the score of each factor assigned by a responder, a measurement model was constructed for each of them. The Bayesian network was constructed using experts' opinions and Dempster-Shafer theory. Using belief updating, the best intervention strategies for improving safety behavior also were selected. The result of the present study demonstrated that the majority of employees do not tend to consider safety rules, regulation, procedures and norms in their behavior at the workplace. Safety attitude, safety knowledge, and supporting environment were the best predictor of safety behavior. Moreover, it was determined that instantaneous improvement of supporting environment and employee participation is the best strategy to reach a high proportion of safety behavior at the workplace. The lack of a comprehensive model that can be used for explaining safety behavior was one of the most problematic issues of the study. Furthermore, it can be concluded that belief updating is a unique feature of Bayesian networks that is very useful in comparing various intervention strategies and selecting the best one form them. Copyright © 2016 Elsevier Ltd. All rights reserved.

Specifying and Refining a Complex Measurement Model.

ERIC Educational Resources Information Center

Levy, Roy; Mislevy, Robert J.

This paper aims to describe a Bayesian approach to modeling and estimating cognitive models both in terms of statistical machinery and actual instrument development. Such a method taps the knowledge of experts to provide initial estimates for the probabilistic relationships among the variables in a multivariate latent variable model and refines…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stewart, Robert N.; Urban, Marie L.; Duchscherer, Samantha E.

Understanding building occupancy is critical to a wide array of applications including natural hazards loss analysis, green building technologies, and population distribution modeling. Due to the expense of directly monitoring buildings, scientists rely in addition on a wide and disparate array of ancillary and open source information including subject matter expertise, survey data, and remote sensing information. These data are fused using data harmonization methods which refer to a loose collection of formal and informal techniques for fusing data together to create viable content for building occupancy estimation. In this paper, we add to the current state of the artmore » by introducing the Population Data Tables (PDT), a Bayesian based informatics system for systematically arranging data and harmonization techniques into a consistent, transparent, knowledge learning framework that retains in the final estimation uncertainty emerging from data, expert judgment, and model parameterization. PDT probabilistically estimates ambient occupancy in units of people/1000ft2 for over 50 building types at the national and sub-national level with the goal of providing global coverage. The challenge of global coverage led to the development of an interdisciplinary geospatial informatics system tool that provides the framework for capturing, storing, and managing open source data, handling subject matter expertise, carrying out Bayesian analytics as well as visualizing and exporting occupancy estimation results. We present the PDT project, situate the work within the larger community, and report on the progress of this multi-year project.Understanding building occupancy is critical to a wide array of applications including natural hazards loss analysis, green building technologies, and population distribution modeling. Due to the expense of directly monitoring buildings, scientists rely in addition on a wide and disparate array of ancillary and open source information including subject matter expertise, survey data, and remote sensing information. These data are fused using data harmonization methods which refer to a loose collection of formal and informal techniques for fusing data together to create viable content for building occupancy estimation. In this paper, we add to the current state of the art by introducing the Population Data Tables (PDT), a Bayesian model and informatics system for systematically arranging data and harmonization techniques into a consistent, transparent, knowledge learning framework that retains in the final estimation uncertainty emerging from data, expert judgment, and model parameterization. PDT probabilistically estimates ambient occupancy in units of people/1000 ft 2 for over 50 building types at the national and sub-national level with the goal of providing global coverage. The challenge of global coverage led to the development of an interdisciplinary geospatial informatics system tool that provides the framework for capturing, storing, and managing open source data, handling subject matter expertise, carrying out Bayesian analytics as well as visualizing and exporting occupancy estimation results. We present the PDT project, situate the work within the larger community, and report on the progress of this multi-year project.« less

A distributed agent architecture for real-time knowledge-based systems: Real-time expert systems project, phase 1

NASA Technical Reports Server (NTRS)

Lee, S. Daniel

1990-01-01

We propose a distributed agent architecture (DAA) that can support a variety of paradigms based on both traditional real-time computing and artificial intelligence. DAA consists of distributed agents that are classified into two categories: reactive and cognitive. Reactive agents can be implemented directly in Ada to meet hard real-time requirements and be deployed on on-board embedded processors. A traditional real-time computing methodology under consideration is the rate monotonic theory that can guarantee schedulability based on analytical methods. AI techniques under consideration for reactive agents are approximate or anytime reasoning that can be implemented using Bayesian belief networks as in Guardian. Cognitive agents are traditional expert systems that can be implemented in ART-Ada to meet soft real-time requirements. During the initial design of cognitive agents, it is critical to consider the migration path that would allow initial deployment on ground-based workstations with eventual deployment on on-board processors. ART-Ada technology enables this migration while Lisp-based technologies make it difficult if not impossible. In addition to reactive and cognitive agents, a meta-level agent would be needed to coordinate multiple agents and to provide meta-level control.

A Validation of an Intelligent Decision-Making Support System for the Nutrition Diagnosis of Bariatric Surgery Patients

PubMed Central

Martins, Cristina; Dias, João; Pinto, José S

2014-01-01

Background Bariatric surgery is an important method for treatment of morbid obesity. It is known that significant nutritional deficiencies might occur after surgery, such as, calorie-protein malnutrition, iron deficiency anemia, and lack of vitamin B12, thiamine, and folic acid. Objective The objective of our study was to validate a computerized intelligent decision support system that suggests nutritional diagnoses of patients submitted to bariatric surgery. Methods There were fifteen clinical cases that were developed and sent to three dietitians in order to evaluate and define a nutritional diagnosis. After this step, the cases were sent to four bariatric surgery expert dietitians who were aiming to collaborate on a gold standard. The nutritional diagnosis was to be defined individually, and any disagreements were solved through a consensus. The final result was used as the gold standard. Bayesian networks were used to implement the system, and database training was done with Shell Netica. For the system validation, a similar answer rate was calculated, as well as the specificity and sensibility. Receiver operating characteristic (ROC) curves were projected to each nutritional diagnosis. Results Among the four experts, the rate of similar answers found was 80% (48/60) to 93% (56/60), depending on the nutritional diagnosis. The rate of similar answers of the system, compared to the gold standard, was 100% (60/60). The system sensibility and specificity were 95.0%. The ROC curves projection showed that the system was able to represent the expert knowledge (gold standard), and to help them in their daily tasks. Conclusions The system that was developed was validated to be used by health care professionals for decision-making support in their nutritional diagnosis of patients submitted to bariatric surgery. PMID:25601419

Bayesian versus politically motivated reasoning in human perception of climate anomalies

NASA Astrophysics Data System (ADS)

Ripberger, Joseph T.; Jenkins-Smith, Hank C.; Silva, Carol L.; Carlson, Deven E.; Gupta, Kuhika; Carlson, Nina; Dunlap, Riley E.

2017-11-01

In complex systems where humans and nature interact to produce joint outcomes, mitigation, adaptation, and resilience require that humans perceive feedback—signals of health and distress—from natural systems. In many instances, humans readily perceive feedback. In others, feedback is more difficult to perceive, so humans rely on experts, heuristics, biases, and/or identify confirming rationalities that may distort perceptions of feedback. This study explores human perception of feedback from natural systems by testing alternate conceptions about how individuals perceive climate anomalies, a form of feedback from the climate system. Results indicate that individuals generally perceive climate anomalies, especially when the anomalies are relatively extreme and persistent. Moreover, this finding is largely robust to political differences that generate predictable but small biases in feedback perception at extreme ends of the partisan spectrum. The subtlety of these biases bodes well for mitigation, adaptation, and resilience as human systems continue to interact with a changing climate system.

Estimating the hatchery fraction of a natural population: a Bayesian approach

USGS Publications Warehouse

Barber, Jarrett J.; Gerow, Kenneth G.; Connolly, Patrick J.; Singh, Sarabdeep

2011-01-01

There is strong and growing interest in estimating the proportion of hatchery fish that are in a natural population (the hatchery fraction). In a sample of fish from the relevant population, some are observed to be marked, indicating their origin as hatchery fish. The observed proportion of marked fish is usually less than the actual hatchery fraction, since the observed proportion is determined by the proportion originally marked, differential survival (usually lower) of marked fish relative to unmarked hatchery fish, and rates of mark retention and detection. Bayesian methods can work well in a setting such as this, in which empirical data are limited but for which there may be considerable expert judgment regarding these values. We explored a Bayesian estimation of the hatchery fraction using Monte Carlo–Markov chain methods. Based on our findings, we created an interactive Excel tool to implement the algorithm, which we have made available for free.

Maritime Transportation Risk Assessment of Tianjin Port with Bayesian Belief Networks.

PubMed

Zhang, Jinfen; Teixeira, Ângelo P; Guedes Soares, C; Yan, Xinping; Liu, Kezhong

2016-06-01

This article develops a Bayesian belief network model for the prediction of accident consequences in the Tianjin port. The study starts with a statistical analysis of historical accident data of six years from 2008 to 2013. Then a Bayesian belief network is constructed to express the dependencies between the indicator variables and accident consequences. The statistics and expert knowledge are synthesized in the Bayesian belief network model to obtain the probability distribution of the consequences. By a sensitivity analysis, several indicator variables that have influence on the consequences are identified, including navigational area, ship type and time of the day. The results indicate that the consequences are most sensitive to the position where the accidents occurred, followed by time of day and ship length. The results also reflect that the navigational risk of the Tianjin port is at the acceptable level, despite that there is more room of improvement. These results can be used by the Maritime Safety Administration to take effective measures to enhance maritime safety in the Tianjin port. © 2016 Society for Risk Analysis.

Bayesian network classifiers for categorizing cortical GABAergic interneurons.

PubMed

Mihaljević, Bojan; Benavides-Piccione, Ruth; Bielza, Concha; DeFelipe, Javier; Larrañaga, Pedro

2015-04-01

An accepted classification of GABAergic interneurons of the cerebral cortex is a major goal in neuroscience. A recently proposed taxonomy based on patterns of axonal arborization promises to be a pragmatic method for achieving this goal. It involves characterizing interneurons according to five axonal arborization features, called F1-F5, and classifying them into a set of predefined types, most of which are established in the literature. Unfortunately, there is little consensus among expert neuroscientists regarding the morphological definitions of some of the proposed types. While supervised classifiers were able to categorize the interneurons in accordance with experts' assignments, their accuracy was limited because they were trained with disputed labels. Thus, here we automatically classify interneuron subsets with different label reliability thresholds (i.e., such that every cell's label is backed by at least a certain (threshold) number of experts). We quantify the cells with parameters of axonal and dendritic morphologies and, in order to predict the type, also with axonal features F1-F4 provided by the experts. Using Bayesian network classifiers, we accurately characterize and classify the interneurons and identify useful predictor variables. In particular, we discriminate among reliable examples of common basket, horse-tail, large basket, and Martinotti cells with up to 89.52% accuracy, and single out the number of branches at 180 μm from the soma, the convex hull 2D area, and the axonal features F1-F4 as especially useful predictors for distinguishing among these types. These results open up new possibilities for an objective and pragmatic classification of interneurons.

Artificial Intelligence: Bayesian versus Heuristic Method for Diagnostic Decision Support.

PubMed

Elkin, Peter L; Schlegel, Daniel R; Anderson, Michael; Komm, Jordan; Ficheur, Gregoire; Bisson, Leslie

2018-04-01

Evoking strength is one of the important contributions of the field of Biomedical Informatics to the discipline of Artificial Intelligence. The University at Buffalo's Orthopedics Department wanted to create an expert system to assist patients with self-diagnosis of knee problems and to thereby facilitate referral to the right orthopedic subspecialist. They had two independent sports medicine physicians review 469 cases. A board-certified orthopedic sports medicine practitioner, L.B., reviewed any disagreements until a gold standard diagnosis was reached. For each case, the patients entered 126 potential answers to 26 questions into a Web interface. These were modeled by an expert sports medicine physician and the answers were reviewed by L.B. For each finding, the clinician specified the sensitivity (term frequency) and both specificity (Sp) and the heuristic evoking strength (ES). Heuristics are methods of reasoning with only partial evidence. An expert system was constructed that reflected the posttest odds of disease-ranked list for each case. We compare the accuracy of using Sp to that of using ES (original model, p  < 0.0008; term importance * disease importance [DItimesTI] model, p  < 0.0001: Wilcoxon ranked sum test). For patient referral assignment, Sp in the DItimesTI model was superior to the use of ES. By the fifth diagnosis, the advantage was lost and so there is no difference between the techniques when serving as a reminder system. Schattauer GmbH Stuttgart.

A Bayesian Approach to Model Selection in Hierarchical Mixtures-of-Experts Architectures.

PubMed

Tanner, Martin A.; Peng, Fengchun; Jacobs, Robert A.

1997-03-01

There does not exist a statistical model that shows good performance on all tasks. Consequently, the model selection problem is unavoidable; investigators must decide which model is best at summarizing the data for each task of interest. This article presents an approach to the model selection problem in hierarchical mixtures-of-experts architectures. These architectures combine aspects of generalized linear models with those of finite mixture models in order to perform tasks via a recursive "divide-and-conquer" strategy. Markov chain Monte Carlo methodology is used to estimate the distribution of the architectures' parameters. One part of our approach to model selection attempts to estimate the worth of each component of an architecture so that relatively unused components can be pruned from the architecture's structure. A second part of this approach uses a Bayesian hypothesis testing procedure in order to differentiate inputs that carry useful information from nuisance inputs. Simulation results suggest that the approach presented here adheres to the dictum of Occam's razor; simple architectures that are adequate for summarizing the data are favored over more complex structures. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.

Physician Bayesian updating from personal beliefs about the base rate and likelihood ratio.

PubMed

Rottman, Benjamin Margolin

2017-02-01

Whether humans can accurately make decisions in line with Bayes' rule has been one of the most important yet contentious topics in cognitive psychology. Though a number of paradigms have been used for studying Bayesian updating, rarely have subjects been allowed to use their own preexisting beliefs about the prior and the likelihood. A study is reported in which physicians judged the posttest probability of a diagnosis for a patient vignette after receiving a test result, and the physicians' posttest judgments were compared to the normative posttest calculated from their own beliefs in the sensitivity and false positive rate of the test (likelihood ratio) and prior probability of the diagnosis. On the one hand, the posttest judgments were strongly related to the physicians' beliefs about both the prior probability as well as the likelihood ratio, and the priors were used considerably more strongly than in previous research. On the other hand, both the prior and the likelihoods were still not used quite as much as they should have been, and there was evidence of other nonnormative aspects to the updating, such as updating independent of the likelihood beliefs. By focusing on how physicians use their own prior beliefs for Bayesian updating, this study provides insight into how well experts perform probabilistic inference in settings in which they rely upon their own prior beliefs rather than experimenter-provided cues. It suggests that there is reason to be optimistic about experts' abilities, but that there is still considerable need for improvement.

Blind image deconvolution using the Fields of Experts prior

NASA Astrophysics Data System (ADS)

Dong, Wende; Feng, Huajun; Xu, Zhihai; Li, Qi

2012-11-01

In this paper, we present a method for single image blind deconvolution. To improve its ill-posedness, we formulate the problem under Bayesian probabilistic framework and use a prior named Fields of Experts (FoE) which is learnt from natural images to regularize the latent image. Furthermore, due to the sparse distribution of the point spread function (PSF), we adopt a Student-t prior to regularize it. An improved alternating minimization (AM) approach is proposed to solve the resulted optimization problem. Experiments on both synthetic and real world blurred images show that the proposed method can achieve results of high quality.

Calculating system reliability with SRFYDO

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morzinski, Jerome; Anderson - Cook, Christine M; Klamann, Richard M

2010-01-01

SRFYDO is a process for estimating reliability of complex systems. Using information from all applicable sources, including full-system (flight) data, component test data, and expert (engineering) judgment, SRFYDO produces reliability estimates and predictions. It is appropriate for series systems with possibly several versions of the system which share some common components. It models reliability as a function of age and up to 2 other lifecycle (usage) covariates. Initial output from its Exploratory Data Analysis mode consists of plots and numerical summaries so that the user can check data entry and model assumptions, and help determine a final form for themore » system model. The System Reliability mode runs a complete reliability calculation using Bayesian methodology. This mode produces results that estimate reliability at the component, sub-system, and system level. The results include estimates of uncertainty, and can predict reliability at some not-too-distant time in the future. This paper presents an overview of the underlying statistical model for the analysis, discusses model assumptions, and demonstrates usage of SRFYDO.« less

New insights into the classification and nomenclature of cortical GABAergic interneurons

PubMed Central

DeFelipe, Javier; López-Cruz, Pedro L.; Benavides-Piccione, Ruth; Bielza, Concha; Larrañaga, Pedro; Anderson, Stewart; Burkhalter, Andreas; Cauli, Bruno; Fairén, Alfonso; Feldmeyer, Dirk; Fishell, Gord; Fitzpatrick, David; Freund, Tamás F.; González-Burgos, Guillermo; Hestrin, Shaul; Hill, Sean; Hof, Patrick R.; Huang, Josh; Jones, Edward G.; Kawaguchi, Yasuo; Kisvárday, Zoltán; Kubota, Yoshiyuki; Lewis, David A.; Marín, Oscar; Markram, Henry; McBain, Chris J.; Meyer, Hanno S.; Monyer, Hannah; Nelson, Sacha B.; Rockland, Kathleen; Rossier, Jean; Rubenstein, John L. R.; Rudy, Bernardo; Scanziani, Massimo; Shepherd, Gordon M.; Sherwood, Chet C.; Staiger, Jochen F.; Tamás, Gábor; Thomson, Alex; Wang, Yun; Yuste, Rafael; Ascoli, Giorgio A.

2013-01-01

A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria. Using Bayesian analysis and clustering algorithms on the resulting data, we investigated the suitability of several anatomical terms and neuron names for cortical GABAergic interneurons. Moreover, we show that supervised classification models could automatically categorize interneurons in agreement with experts’ assignments. These results demonstrate a practical and objective approach to the naming, characterization and classification of neurons based on community consensus. PMID:23385869

Robotic astrobiology - prospects for enhancing scientific productivity of mars rover missions

NASA Astrophysics Data System (ADS)

Ellery, A. A.

2018-07-01

Robotic astrobiology involves the remote projection of intelligent capabilities to planetary missions in the search for life, preferably with human-level intelligence. Planetary rovers would be true human surrogates capable of sophisticated decision-making to enhance their scientific productivity. We explore several key aspects of this capability: (i) visual texture analysis of rocks to enable their geological classification and so, astrobiological potential; (ii) serendipitous target acquisition whilst on the move; (iii) continuous extraction of regolith properties, including water ice whilst on the move; and (iv) deep learning-capable Bayesian net expert systems. Individually, these capabilities will provide enhanced scientific return for astrobiology missions, but together, they will provide full autonomous science capability.

SUVI Thematic Maps: A new tool for space weather forecasting

NASA Astrophysics Data System (ADS)

Hughes, J. M.; Seaton, D. B.; Darnel, J.

2017-12-01

The new Solar Ultraviolet Imager (SUVI) instruments aboard NOAA's GOES-R series satellites collect continuous, high-quality imagery of the Sun in six wavelengths. SUVI imagers produce at least one image every 10 seconds, or 8,640 images per day, considerably more data than observers can digest in real time. Over the projected 20-year lifetime of the four GOES-R series spacecraft, SUVI will provide critical imagery for space weather forecasters and produce an extensive but unwieldy archive. In order to condense the database into a dynamic and searchable form we have developed solar thematic maps, maps of the Sun with key features, such as coronal holes, flares, bright regions, quiet corona, and filaments, identified. Thematic maps will be used in NOAA's Space Weather Prediction Center to improve forecaster response time to solar events and generate several derivative products. Likewise, scientists use thematic maps to find observations of interest more easily. Using an expert-trained, naive Bayesian classifier to label each pixel, we create thematic maps in real-time. We created software to collect expert classifications of solar features based on SUVI images. Using this software, we compiled a database of expert classifications, from which we could characterize the distribution of pixels associated with each theme. Given new images, the classifier assigns each pixel the most appropriate label according to the trained distribution. Here we describe the software to collect expert training and the successes and limitations of the classifier. The algorithm excellently identifies coronal holes but fails to consistently detect filaments and prominences. We compare the Bayesian classifier to an artificial neural network, one of our attempts to overcome the aforementioned limitations. These results are very promising and encourage future research into an ensemble classification approach.

Using a Java Dynamic Tree to manage the terminology in a suite of medical applications.

PubMed

Yang, K; Evens, M W; Trace, D A

2008-01-01

Now that the National Library of Medicine has made SNOMED-CT widely available, we are trying to manage the terminology of a whole suite of medical applications and map our terminology into that in SNOMED. This paper describes the design and implementation of the Java Dynamic Tree that provides structure to our medical terminology and explains how it functions as the core of our system. The tree was designed to reflect the stages in a patient interview, so it contains components for identifying the patient and the provider, a large set of chief complaints, review of systems, physical examination, several history modules, medications, laboratory tests, imaging, and special procedures. The tree is mirrored in a commercial DBMS, which also stores multi-encounter patient data, disorder patterns for our Bayesian diagnostic system, and the data and rules for other expert systems. The DBMS facilitates the import and export of large terminology files. Our Java Dynamic Tree allows the health care provider to view the entire terminology along with the structure that supports it, as well as the mechanism for the generation of progress notes and other documents, in terms of a single hierarchical structure. Changes in terminology can be propagated through the system under the control of the expert. The import/ export facility has been a major help by replacing our original terminology by the terminology in SNOMED-CT.

Bayesian Dose-Response Modeling in Sparse Data

NASA Astrophysics Data System (ADS)

Kim, Steven B.

This book discusses Bayesian dose-response modeling in small samples applied to two different settings. The first setting is early phase clinical trials, and the second setting is toxicology studies in cancer risk assessment. In early phase clinical trials, experimental units are humans who are actual patients. Prior to a clinical trial, opinions from multiple subject area experts are generally more informative than the opinion of a single expert, but we may face a dilemma when they have disagreeing prior opinions. In this regard, we consider compromising the disagreement and compare two different approaches for making a decision. In addition to combining multiple opinions, we also address balancing two levels of ethics in early phase clinical trials. The first level is individual-level ethics which reflects the perspective of trial participants. The second level is population-level ethics which reflects the perspective of future patients. We extensively compare two existing statistical methods which focus on each perspective and propose a new method which balances the two conflicting perspectives. In toxicology studies, experimental units are living animals. Here we focus on a potential non-monotonic dose-response relationship which is known as hormesis. Briefly, hormesis is a phenomenon which can be characterized by a beneficial effect at low doses and a harmful effect at high doses. In cancer risk assessments, the estimation of a parameter, which is known as a benchmark dose, can be highly sensitive to a class of assumptions, monotonicity or hormesis. In this regard, we propose a robust approach which considers both monotonicity and hormesis as a possibility. In addition, We discuss statistical hypothesis testing for hormesis and consider various experimental designs for detecting hormesis based on Bayesian decision theory. Past experiments have not been optimally designed for testing for hormesis, and some Bayesian optimal designs may not be optimal under a wrong parametric assumption. In this regard, we consider a robust experimental design which does not require any parametric assumption.

Integrating clinicians, knowledge and data: expert-based cooperative analysis in healthcare decision support

PubMed Central

2010-01-01

Background Decision support in health systems is a highly difficult task, due to the inherent complexity of the process and structures involved. Method This paper introduces a new hybrid methodology Expert-based Cooperative Analysis (EbCA), which incorporates explicit prior expert knowledge in data analysis methods, and elicits implicit or tacit expert knowledge (IK) to improve decision support in healthcare systems. EbCA has been applied to two different case studies, showing its usability and versatility: 1) Bench-marking of small mental health areas based on technical efficiency estimated by EbCA-Data Envelopment Analysis (EbCA-DEA), and 2) Case-mix of schizophrenia based on functional dependency using Clustering Based on Rules (ClBR). In both cases comparisons towards classical procedures using qualitative explicit prior knowledge were made. Bayesian predictive validity measures were used for comparison with expert panels results. Overall agreement was tested by Intraclass Correlation Coefficient in case "1" and kappa in both cases. Results EbCA is a new methodology composed by 6 steps:. 1) Data collection and data preparation; 2) acquisition of "Prior Expert Knowledge" (PEK) and design of the "Prior Knowledge Base" (PKB); 3) PKB-guided analysis; 4) support-interpretation tools to evaluate results and detect inconsistencies (here Implicit Knowledg -IK- might be elicited); 5) incorporation of elicited IK in PKB and repeat till a satisfactory solution; 6) post-processing results for decision support. EbCA has been useful for incorporating PEK in two different analysis methods (DEA and Clustering), applied respectively to assess technical efficiency of small mental health areas and for case-mix of schizophrenia based on functional dependency. Differences in results obtained with classical approaches were mainly related to the IK which could be elicited by using EbCA and had major implications for the decision making in both cases. Discussion This paper presents EbCA and shows the convenience of completing classical data analysis with PEK as a mean to extract relevant knowledge in complex health domains. One of the major benefits of EbCA is iterative elicitation of IK.. Both explicit and tacit or implicit expert knowledge are critical to guide the scientific analysis of very complex decisional problems as those found in health system research. PMID:20920289

Integrating clinicians, knowledge and data: expert-based cooperative analysis in healthcare decision support.

PubMed

Gibert, Karina; García-Alonso, Carlos; Salvador-Carulla, Luis

2010-09-30

Decision support in health systems is a highly difficult task, due to the inherent complexity of the process and structures involved. This paper introduces a new hybrid methodology Expert-based Cooperative Analysis (EbCA), which incorporates explicit prior expert knowledge in data analysis methods, and elicits implicit or tacit expert knowledge (IK) to improve decision support in healthcare systems. EbCA has been applied to two different case studies, showing its usability and versatility: 1) Bench-marking of small mental health areas based on technical efficiency estimated by EbCA-Data Envelopment Analysis (EbCA-DEA), and 2) Case-mix of schizophrenia based on functional dependency using Clustering Based on Rules (ClBR). In both cases comparisons towards classical procedures using qualitative explicit prior knowledge were made. Bayesian predictive validity measures were used for comparison with expert panels results. Overall agreement was tested by Intraclass Correlation Coefficient in case "1" and kappa in both cases. EbCA is a new methodology composed by 6 steps:. 1) Data collection and data preparation; 2) acquisition of "Prior Expert Knowledge" (PEK) and design of the "Prior Knowledge Base" (PKB); 3) PKB-guided analysis; 4) support-interpretation tools to evaluate results and detect inconsistencies (here Implicit Knowledg -IK- might be elicited); 5) incorporation of elicited IK in PKB and repeat till a satisfactory solution; 6) post-processing results for decision support. EbCA has been useful for incorporating PEK in two different analysis methods (DEA and Clustering), applied respectively to assess technical efficiency of small mental health areas and for case-mix of schizophrenia based on functional dependency. Differences in results obtained with classical approaches were mainly related to the IK which could be elicited by using EbCA and had major implications for the decision making in both cases. This paper presents EbCA and shows the convenience of completing classical data analysis with PEK as a mean to extract relevant knowledge in complex health domains. One of the major benefits of EbCA is iterative elicitation of IK.. Both explicit and tacit or implicit expert knowledge are critical to guide the scientific analysis of very complex decisional problems as those found in health system research.

Selected aspects of prior and likelihood information for a Bayesian classifier in a road safety analysis.

PubMed

Nowakowska, Marzena

2017-04-01

The development of the Bayesian logistic regression model classifying the road accident severity is discussed. The already exploited informative priors (method of moments, maximum likelihood estimation, and two-stage Bayesian updating), along with the original idea of a Boot prior proposal, are investigated when no expert opinion has been available. In addition, two possible approaches to updating the priors, in the form of unbalanced and balanced training data sets, are presented. The obtained logistic Bayesian models are assessed on the basis of a deviance information criterion (DIC), highest probability density (HPD) intervals, and coefficients of variation estimated for the model parameters. The verification of the model accuracy has been based on sensitivity, specificity and the harmonic mean of sensitivity and specificity, all calculated from a test data set. The models obtained from the balanced training data set have a better classification quality than the ones obtained from the unbalanced training data set. The two-stage Bayesian updating prior model and the Boot prior model, both identified with the use of the balanced training data set, outperform the non-informative, method of moments, and maximum likelihood estimation prior models. It is important to note that one should be careful when interpreting the parameters since different priors can lead to different models. Copyright © 2017 Elsevier Ltd. All rights reserved.

Revealing the ISO/IEC 9126-1 Clique Tree for COTS Software Evaluation

NASA Technical Reports Server (NTRS)

Morris, A. Terry

2007-01-01

Previous research has shown that acyclic dependency models, if they exist, can be extracted from software quality standards and that these models can be used to assess software safety and product quality. In the case of commercial off-the-shelf (COTS) software, the extracted dependency model can be used in a probabilistic Bayesian network context for COTS software evaluation. Furthermore, while experts typically employ Bayesian networks to encode domain knowledge, secondary structures (clique trees) from Bayesian network graphs can be used to determine the probabilistic distribution of any software variable (attribute) using any clique that contains that variable. Secondary structures, therefore, provide insight into the fundamental nature of graphical networks. This paper will apply secondary structure calculations to reveal the clique tree of the acyclic dependency model extracted from the ISO/IEC 9126-1 software quality standard. Suggestions will be provided to describe how the clique tree may be exploited to aid efficient transformation of an evaluation model.

A Monte Carlo–Based Bayesian Approach for Measuring Agreement in a Qualitative Scale

PubMed Central

Pérez Sánchez, Carlos Javier

2014-01-01

Agreement analysis has been an active research area whose techniques have been widely applied in psychology and other fields. However, statistical agreement among raters has been mainly considered from a classical statistics point of view. Bayesian methodology is a viable alternative that allows the inclusion of subjective initial information coming from expert opinions, personal judgments, or historical data. A Bayesian approach is proposed by providing a unified Monte Carlo–based framework to estimate all types of measures of agreement in a qualitative scale of response. The approach is conceptually simple and it has a low computational cost. Both informative and non-informative scenarios are considered. In case no initial information is available, the results are in line with the classical methodology, but providing more information on the measures of agreement. For the informative case, some guidelines are presented to elicitate the prior distribution. The approach has been applied to two applications related to schizophrenia diagnosis and sensory analysis. PMID:29881002

Towards a Bayesian evaluation of features in questioned handwritten signatures.

PubMed

Gaborini, Lorenzo; Biedermann, Alex; Taroni, Franco

2017-05-01

In this work, we propose the construction of a evaluative framework for supporting experts in questioned signature examinations. Through the use of Bayesian networks, we envision to quantify the probative value of well defined measurements performed on questioned signatures, in a way that is both formalised and part of a coherent approach to evaluation. At the current stage, our project is explorative, focusing on the broad range of aspects that relate to comparative signature examinations. The goal is to identify writing features which are both highly discriminant, and easy for forensic examiners to detect. We also seek for a balance between case-specific features and characteristics which can be measured in the vast majority of signatures. Care is also taken at preserving the interpretability at every step of the reasoning process. This paves the way for future work, which will aim at merging the different contributions to a single probabilistic measure of strength of evidence using Bayesian networks. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

A Bayesian network modeling approach to forecasting the 21st century worldwide status of polar bears

NASA Astrophysics Data System (ADS)

Amstrup, Steven C.; Marcot, Bruce G.; Douglas, David C.

To inform the U.S. Fish and Wildlife Service decision, whether or not to list polar bears as threatened under the Endangered Species Act (ESA), we projected the status of the world's polar bears (Ursus maritimus) for decades centered on future years 2025, 2050, 2075, and 2095. We defined four ecoregions based on current and projected sea ice conditions: seasonal ice, Canadian Archipelago, polar basin divergent, and polar basin convergent ecoregions. We incorporated general circulation model projections of future sea ice into a Bayesian network (BN) model structured around the factors considered in ESA decisions. This first-generation BN model combined empirical data, interpretations of data, and professional judgments of one polar bear expert into a probabilistic framework that identifies causal links between environmental stressors and polar bear responses. We provide guidance regarding steps necessary to refine the model, including adding inputs from other experts. The BN model projected extirpation of polar bears from the seasonal ice and polar basin divergent ecoregions, where ≈2/3 of the world's polar bears currently occur, by mid century. Projections were less dire in other ecoregions. Decline in ice habitat was the overriding factor driving the model outcomes. Although this is a first-generation model, the dependence of polar bears on sea ice is universally accepted, and the observed sea ice decline is faster than models suggest. Therefore, incorporating judgments of multiple experts in a final model is not expected to fundamentally alter the outlook for polar bears described here.

Clearing Unexploded Ordnance: Bayesian Methodology for Assessing Success

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson, K K.

2005-10-30

The Department of Defense has many Formerly Used Defense Sites (FUDS) that are slated for transfer for public use. Some sites have unexploded ordnance (UXO) that must be cleared prior to any land transfers. Sites are characterized using geophysical sensing devices and locations are identified where possible UXO may be located. In practice, based on the analysis of the geophysical surveys, a dig list of N suspect locations is created for a site that is possibly contaminated with UXO. The suspect locations on the dig list are often assigned into K bins ranging from ``most likely to contain UXO" tomore » ``least likely to be UXO" based on signal discrimination techniques and expert judgment. Usually all dig list locations are sampled to determine if UXO is present before the site is determined to be free of UXO. While this method is 100% certain to insure no UXO remains in the locations identified by the signal discrimination and expert judgment, it is very costly. This paper proposes a statistical Bayesian methodology that may result in digging less than 100% of the suspect locations to reach a pre-defined tolerable risk, where risk is defined in terms of a low probability that any UXO remains in the unsampled dig list locations. Two important features of a Bayesian approach are that it can account for uncertainties in model parameters and that it can handle data that becomes available in stages. The results from each stage of data can be used to direct the subsequent digs.« less

A Risk Assessment of Antibiotic Pan-Drug-Resistance in the UK: Bayesian Analysis of an Expert Elicitation Study

PubMed Central

Carter, Daniel; Charlett, André; Conti, Stefano; Robotham, Julie V.; Johnson, Alan P.; Livermore, David M.; Fowler, Tom; Sharland, Mike; Hopkins, Susan; Woodford, Neil; Burgess, Philip; Dobra, Stephen

2017-01-01

To inform the UK antimicrobial resistance strategy, a risk assessment was undertaken of the likelihood, over a five-year time-frame, of the emergence and widespread dissemination of pan-drug-resistant (PDR) Gram-negative bacteria that would pose a major public health threat by compromising effective healthcare delivery. Subsequent impact over five- and 20-year time-frames was assessed in terms of morbidity and mortality attributable to PDR Gram-negative bacteraemia. A Bayesian approach, combining available data with expert prior opinion, was used to determine the probability of the emergence, persistence and spread of PDR bacteria. Overall probability was modelled using Monte Carlo simulation. Estimates of impact were also obtained using Bayesian methods. The estimated probability of widespread occurrence of PDR pathogens within five years was 0.2 (95% credibility interval (CrI): 0.07–0.37). Estimated annual numbers of PDR Gram-negative bacteraemias at five and 20 years were 6800 (95% CrI: 400–58,600) and 22,800 (95% CrI: 1500–160,000), respectively; corresponding estimates of excess deaths were 1900 (95% CrI: 0–23,000) and 6400 (95% CrI: 0–64,000). Over 20 years, cumulative estimates indicate 284,000 (95% CrI: 17,000–1,990,000) cases of PDR Gram-negative bacteraemia, leading to an estimated 79,000 (95% CrI: 0–821,000) deaths. This risk assessment reinforces the need for urgent national and international action to tackle antibiotic resistance. PMID:28272350

A spatial Bayesian network model to assess the benefits of early warning for urban flood risk to people

NASA Astrophysics Data System (ADS)

Balbi, Stefano; Villa, Ferdinando; Mojtahed, Vahid; Hegetschweiler, Karin Tessa; Giupponi, Carlo

2016-06-01

This article presents a novel methodology to assess flood risk to people by integrating people's vulnerability and ability to cushion hazards through coping and adapting. The proposed approach extends traditional risk assessments beyond material damages; complements quantitative and semi-quantitative data with subjective and local knowledge, improving the use of commonly available information; and produces estimates of model uncertainty by providing probability distributions for all of its outputs. Flood risk to people is modeled using a spatially explicit Bayesian network model calibrated on expert opinion. Risk is assessed in terms of (1) likelihood of non-fatal physical injury, (2) likelihood of post-traumatic stress disorder and (3) likelihood of death. The study area covers the lower part of the Sihl valley (Switzerland) including the city of Zurich. The model is used to estimate the effect of improving an existing early warning system, taking into account the reliability, lead time and scope (i.e., coverage of people reached by the warning). Model results indicate that the potential benefits of an improved early warning in terms of avoided human impacts are particularly relevant in case of a major flood event.

Protocol for evaluation of the cost-effectiveness of ePrescribing systems and candidate prototype for other related health information technologies

PubMed Central

2014-01-01

Background This protocol concerns the assessment of cost-effectiveness of hospital health information technology (HIT) in four hospitals. Two of these hospitals are acquiring ePrescribing systems incorporating extensive decision support, while the other two will implement systems incorporating more basic clinical algorithms. Implementation of an ePrescribing system will have diffuse effects over myriad clinical processes, so the protocol has to deal with a large amount of information collected at various ‘levels’ across the system. Methods/Design The method we propose is use of Bayesian ideas as a philosophical guide. Assessment of cost-effectiveness requires a number of parameters in order to measure incremental cost utility or benefit – the effectiveness of the intervention in reducing frequency of preventable adverse events; utilities for these adverse events; costs of HIT systems; and cost consequences of adverse events averted. There is no single end-point that adequately and unproblematically captures the effectiveness of the intervention; we therefore plan to observe changes in error rates and adverse events in four error categories (death, permanent disability, moderate disability, minimal effect). For each category we will elicit and pool subjective probability densities from experts for reductions in adverse events, resulting from deployment of the intervention in a hospital with extensive decision support. The experts will have been briefed with quantitative and qualitative data from the study and external data sources prior to elicitation. Following this, there will be a process of deliberative dialogues so that experts can “re-calibrate” their subjective probability estimates. The consolidated densities assembled from the repeat elicitation exercise will then be used to populate a health economic model, along with salient utilities. The credible limits from these densities can define thresholds for sensitivity analyses. Discussion The protocol we present here was designed for evaluation of ePrescribing systems. However, the methodology we propose could be used whenever research cannot provide a direct and unbiased measure of comparative effectiveness. PMID:25038609

Bayesian network learning for natural hazard assessments

NASA Astrophysics Data System (ADS)

Vogel, Kristin

2016-04-01

Even though quite different in occurrence and consequences, from a modelling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding. On top of the uncertainty about the modelling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Thus, for reliable natural hazard assessments it is crucial not only to capture and quantify involved uncertainties, but also to express and communicate uncertainties in an intuitive way. Decision-makers, who often find it difficult to deal with uncertainties, might otherwise return to familiar (mostly deterministic) proceedings. In the scope of the DFG research training group „NatRiskChange" we apply the probabilistic framework of Bayesian networks for diverse natural hazard and vulnerability studies. The great potential of Bayesian networks was already shown in previous natural hazard assessments. Treating each model component as random variable, Bayesian networks aim at capturing the joint distribution of all considered variables. Hence, each conditional distribution of interest (e.g. the effect of precautionary measures on damage reduction) can be inferred. The (in-)dependencies between the considered variables can be learned purely data driven or be given by experts. Even a combination of both is possible. By translating the (in-)dependences into a graph structure, Bayesian networks provide direct insights into the workings of the system and allow to learn about the underlying processes. Besides numerous studies on the topic, learning Bayesian networks from real-world data remains challenging. In previous studies, e.g. on earthquake induced ground motion and flood damage assessments, we tackled the problems arising with continuous variables and incomplete observations. Further studies rise the challenge of relying on very small data sets. Since parameter estimates for complex models based on few observations are unreliable, it is necessary to focus on simplified, yet still meaningful models. A so called Markov Blanket approach is developed to identify the most relevant model components and to construct a simple Bayesian network based on those findings. Since the proceeding is completely data driven, it can easily be transferred to various applications in natural hazard domains. This study is funded by the Deutsche Forschungsgemeinschaft (DFG) within the research training programme GRK 2043/1 "NatRiskChange - Natural hazards and risks in a changing world" at Potsdam University.

Bayesian methodology incorporating expert judgment for ranking countermeasure effectiveness under uncertainty: example applied to at grade railroad crossings in Korea.

PubMed

Washington, Simon; Oh, Jutaek

2006-03-01

Transportation professionals are sometimes required to make difficult transportation safety investment decisions in the face of uncertainty. In particular, an engineer may be expected to choose among an array of technologies and/or countermeasures to remediate perceived safety problems when: (1) little information is known about the countermeasure effects on safety; (2) information is known but from different regions, states, or countries where a direct generalization may not be appropriate; (3) where the technologies and/or countermeasures are relatively untested, or (4) where costs prohibit the full and careful testing of each of the candidate countermeasures via before-after studies. The importance of an informed and well-considered decision based on the best possible engineering knowledge and information is imperative due to the potential impact on the numbers of human injuries and deaths that may result from these investments. This paper describes the formalization and application of a methodology to evaluate the safety benefit of countermeasures in the face of uncertainty. To illustrate the methodology, 18 countermeasures for improving safety of at grade railroad crossings (AGRXs) in the Republic of Korea are considered. Akin to "stated preference" methods in travel survey research, the methodology applies random selection and laws of large numbers to derive accident modification factor (AMF) densities from expert opinions. In a full Bayesian analysis framework, the collective opinions in the form of AMF densities (data likelihood) are combined with prior knowledge (AMF density priors) for the 18 countermeasures to obtain 'best' estimates of AMFs (AMF posterior credible intervals). The countermeasures are then compared and recommended based on the largest safety returns with minimum risk (uncertainty). To the author's knowledge the complete methodology is new and has not previously been applied or reported in the literature. The results demonstrate that the methodology is able to discern anticipated safety benefit differences across candidate countermeasures. For the 18 at grade railroad crossings considered in this analysis, it was found that the top three performing countermeasures for reducing crashes are in-vehicle warning systems, obstacle detection systems, and constant warning time systems.

Research and development of LANDSAT-based crop inventory techniques

NASA Technical Reports Server (NTRS)

Horvath, R.; Cicone, R. C.; Malila, W. A. (Principal Investigator)

1982-01-01

A wide spectrum of technology pertaining to the inventory of crops using LANDSAT without in situ training data is addressed. Methods considered include Bayesian based through-the-season methods, estimation technology based on analytical profile fitting methods, and expert-based computer aided methods. Although the research was conducted using U.S. data, the adaptation of the technology to the Southern Hemisphere, especially Argentina was considered.

Recognizing Uncertainty in the Q-Matrix via a Bayesian Extension of the DINA Model

ERIC Educational Resources Information Center

DeCarlo, Lawrence T.

2012-01-01

In the typical application of a cognitive diagnosis model, the Q-matrix, which reflects the theory with respect to the skills indicated by the items, is assumed to be known. However, the Q-matrix is usually determined by expert judgment, and so there can be uncertainty about some of its elements. Here it is shown that this uncertainty can be…

Simultaneous Force Regression and Movement Classification of Fingers via Surface EMG within a Unified Bayesian Framework.

PubMed

Baldacchino, Tara; Jacobs, William R; Anderson, Sean R; Worden, Keith; Rowson, Jennifer

2018-01-01

This contribution presents a novel methodology for myolectric-based control using surface electromyographic (sEMG) signals recorded during finger movements. A multivariate Bayesian mixture of experts (MoE) model is introduced which provides a powerful method for modeling force regression at the fingertips, while also performing finger movement classification as a by-product of the modeling algorithm. Bayesian inference of the model allows uncertainties to be naturally incorporated into the model structure. This method is tested using data from the publicly released NinaPro database which consists of sEMG recordings for 6 degree-of-freedom force activations for 40 intact subjects. The results demonstrate that the MoE model achieves similar performance compared to the benchmark set by the authors of NinaPro for finger force regression. Additionally, inherent to the Bayesian framework is the inclusion of uncertainty in the model parameters, naturally providing confidence bounds on the force regression predictions. Furthermore, the integrated clustering step allows a detailed investigation into classification of the finger movements, without incurring any extra computational effort. Subsequently, a systematic approach to assessing the importance of the number of electrodes needed for accurate control is performed via sensitivity analysis techniques. A slight degradation in regression performance is observed for a reduced number of electrodes, while classification performance is unaffected.

Planetary micro-rover operations on Mars using a Bayesian framework for inference and control

NASA Astrophysics Data System (ADS)

Post, Mark A.; Li, Junquan; Quine, Brendan M.

2016-03-01

With the recent progress toward the application of commercially-available hardware to small-scale space missions, it is now becoming feasible for groups of small, efficient robots based on low-power embedded hardware to perform simple tasks on other planets in the place of large-scale, heavy and expensive robots. In this paper, we describe design and programming of the Beaver micro-rover developed for Northern Light, a Canadian initiative to send a small lander and rover to Mars to study the Martian surface and subsurface. For a small, hardware-limited rover to handle an uncertain and mostly unknown environment without constant management by human operators, we use a Bayesian network of discrete random variables as an abstraction of expert knowledge about the rover and its environment, and inference operations for control. A framework for efficient construction and inference into a Bayesian network using only the C language and fixed-point mathematics on embedded hardware has been developed for the Beaver to make intelligent decisions with minimal sensor data. We study the performance of the Beaver as it probabilistically maps a simple outdoor environment with sensor models that include uncertainty. Results indicate that the Beaver and other small and simple robotic platforms can make use of a Bayesian network to make intelligent decisions in uncertain planetary environments.

Investigating different approaches to develop informative priors in hierarchical Bayesian safety performance functions.

PubMed

Yu, Rongjie; Abdel-Aty, Mohamed

2013-07-01

The Bayesian inference method has been frequently adopted to develop safety performance functions. One advantage of the Bayesian inference is that prior information for the independent variables can be included in the inference procedures. However, there are few studies that discussed how to formulate informative priors for the independent variables and evaluated the effects of incorporating informative priors in developing safety performance functions. This paper addresses this deficiency by introducing four approaches of developing informative priors for the independent variables based on historical data and expert experience. Merits of these informative priors have been tested along with two types of Bayesian hierarchical models (Poisson-gamma and Poisson-lognormal models). Deviance information criterion (DIC), R-square values, and coefficients of variance for the estimations were utilized as evaluation measures to select the best model(s). Comparison across the models indicated that the Poisson-gamma model is superior with a better model fit and it is much more robust with the informative priors. Moreover, the two-stage Bayesian updating informative priors provided the best goodness-of-fit and coefficient estimation accuracies. Furthermore, informative priors for the inverse dispersion parameter have also been introduced and tested. Different types of informative priors' effects on the model estimations and goodness-of-fit have been compared and concluded. Finally, based on the results, recommendations for future research topics and study applications have been made. Copyright © 2013 Elsevier Ltd. All rights reserved.

Interactive classification and content-based retrieval of tissue images

NASA Astrophysics Data System (ADS)

Aksoy, Selim; Marchisio, Giovanni B.; Tusk, Carsten; Koperski, Krzysztof

2002-11-01

We describe a system for interactive classification and retrieval of microscopic tissue images. Our system models tissues in pixel, region and image levels. Pixel level features are generated using unsupervised clustering of color and texture values. Region level features include shape information and statistics of pixel level feature values. Image level features include statistics and spatial relationships of regions. To reduce the gap between low-level features and high-level expert knowledge, we define the concept of prototype regions. The system learns the prototype regions in an image collection using model-based clustering and density estimation. Different tissue types are modeled using spatial relationships of these regions. Spatial relationships are represented by fuzzy membership functions. The system automatically selects significant relationships from training data and builds models which can also be updated using user relevance feedback. A Bayesian framework is used to classify tissues based on these models. Preliminary experiments show that the spatial relationship models we developed provide a flexible and powerful framework for classification and retrieval of tissue images.

Fuselets: an agent based architecture for fusion of heterogeneous information and data

NASA Astrophysics Data System (ADS)

Beyerer, Jürgen; Heizmann, Michael; Sander, Jennifer

2006-04-01

A new architecture for fusing information and data from heterogeneous sources is proposed. The approach takes criminalistics as a model. In analogy to the work of detectives, who attempt to investigate crimes, software agents are initiated that pursue clues and try to consolidate or to dismiss hypotheses. Like their human pendants, they can, if questions beyond their competences arise, consult expert agents. Within the context of a certain task, region, and time interval, specialized operations are applied to each relevant information source, e.g. IMINT, SIGINT, ACINT,..., HUMINT, data bases etc. in order to establish hit lists of first clues. Each clue is described by its pertaining facts, uncertainties, and dependencies in form of a local degree-of-belief (DoB) distribution in a Bayesian sense. For each clue an agent is initiated which cooperates with other agents and experts. Expert agents support to make use of different information sources. Consultations of experts, capable to access certain information sources, result in changes of the DoB of the pertaining clue. According to the significance of concentration of their DoB distribution clues are abandoned or pursued further to formulate task specific hypotheses. Communications between the agents serve to find out whether different clues belong to the same cause and thus can be put together. At the end of the investigation process, the different hypotheses are evaluated by a jury and a final report is created that constitutes the fusion result. The approach proposed avoids calculating global DoB distributions by adopting a local Bayesian approximation and thus reduces the complexity of the exact problem essentially. Different information sources are transformed into DoB distributions using the maximum entropy paradigm and considering known facts as constraints. Nominal, ordinal and cardinal quantities can be treated within this framework equally. The architecture is scalable by tailoring the number of agents according to the available computer resources, to the priority of tasks, and to the maximum duration of the fusion process. Furthermore, the architecture allows cooperative work of human and automated agents and experts, as long as not all subtasks can be accomplished automatically.

[Development and Application of a Performance Prediction Model for Home Care Nursing Based on a Balanced Scorecard using the Bayesian Belief Network].

PubMed

Noh, Wonjung; Seomun, Gyeongae

2015-06-01

This study was conducted to develop key performance indicators (KPIs) for home care nursing (HCN) based on a balanced scorecard, and to construct a performance prediction model of strategic objectives using the Bayesian Belief Network (BBN). This methodological study included four steps: establishment of KPIs, performance prediction modeling, development of a performance prediction model using BBN, and simulation of a suggested nursing management strategy. An HCN expert group and a staff group participated. The content validity index was analyzed using STATA 13.0, and BBN was analyzed using HUGIN 8.0. We generated a list of KPIs composed of 4 perspectives, 10 strategic objectives, and 31 KPIs. In the validity test of the performance prediction model, the factor with the greatest variance for increasing profit was maximum cost reduction of HCN services. The factor with the smallest variance for increasing profit was a minimum image improvement for HCN. During sensitivity analysis, the probability of the expert group did not affect the sensitivity. Furthermore, simulation of a 10% image improvement predicted the most effective way to increase profit. KPIs of HCN can estimate financial and non-financial performance. The performance prediction model for HCN will be useful to improve performance.

Bayesian sensitivity analysis methods to evaluate bias due to misclassification and missing data using informative priors and external validation data.

PubMed

Luta, George; Ford, Melissa B; Bondy, Melissa; Shields, Peter G; Stamey, James D

2013-04-01

Recent research suggests that the Bayesian paradigm may be useful for modeling biases in epidemiological studies, such as those due to misclassification and missing data. We used Bayesian methods to perform sensitivity analyses for assessing the robustness of study findings to the potential effect of these two important sources of bias. We used data from a study of the joint associations of radiotherapy and smoking with primary lung cancer among breast cancer survivors. We used Bayesian methods to provide an operational way to combine both validation data and expert opinion to account for misclassification of the two risk factors and missing data. For comparative purposes we considered a "full model" that allowed for both misclassification and missing data, along with alternative models that considered only misclassification or missing data, and the naïve model that ignored both sources of bias. We identified noticeable differences between the four models with respect to the posterior distributions of the odds ratios that described the joint associations of radiotherapy and smoking with primary lung cancer. Despite those differences we found that the general conclusions regarding the pattern of associations were the same regardless of the model used. Overall our results indicate a nonsignificantly decreased lung cancer risk due to radiotherapy among nonsmokers, and a mildly increased risk among smokers. We described easy to implement Bayesian methods to perform sensitivity analyses for assessing the robustness of study findings to misclassification and missing data. Copyright © 2012 Elsevier Ltd. All rights reserved.

Multifaceted Modelling of Complex Business Enterprises

PubMed Central

2015-01-01

We formalise and present a new generic multifaceted complex system approach for modelling complex business enterprises. Our method has a strong focus on integrating the various data types available in an enterprise which represent the diverse perspectives of various stakeholders. We explain the challenges faced and define a novel approach to converting diverse data types into usable Bayesian probability forms. The data types that can be integrated include historic data, survey data, and management planning data, expert knowledge and incomplete data. The structural complexities of the complex system modelling process, based on various decision contexts, are also explained along with a solution. This new application of complex system models as a management tool for decision making is demonstrated using a railway transport case study. The case study demonstrates how the new approach can be utilised to develop a customised decision support model for a specific enterprise. Various decision scenarios are also provided to illustrate the versatility of the decision model at different phases of enterprise operations such as planning and control. PMID:26247591

Multifaceted Modelling of Complex Business Enterprises.

PubMed

Chakraborty, Subrata; Mengersen, Kerrie; Fidge, Colin; Ma, Lin; Lassen, David

2015-01-01

We formalise and present a new generic multifaceted complex system approach for modelling complex business enterprises. Our method has a strong focus on integrating the various data types available in an enterprise which represent the diverse perspectives of various stakeholders. We explain the challenges faced and define a novel approach to converting diverse data types into usable Bayesian probability forms. The data types that can be integrated include historic data, survey data, and management planning data, expert knowledge and incomplete data. The structural complexities of the complex system modelling process, based on various decision contexts, are also explained along with a solution. This new application of complex system models as a management tool for decision making is demonstrated using a railway transport case study. The case study demonstrates how the new approach can be utilised to develop a customised decision support model for a specific enterprise. Various decision scenarios are also provided to illustrate the versatility of the decision model at different phases of enterprise operations such as planning and control.

Bayesian network interface for assisting radiology interpretation and education

NASA Astrophysics Data System (ADS)

Duda, Jeffrey; Botzolakis, Emmanuel; Chen, Po-Hao; Mohan, Suyash; Nasrallah, Ilya; Rauschecker, Andreas; Rudie, Jeffrey; Bryan, R. Nick; Gee, James; Cook, Tessa

2018-03-01

In this work, we present the use of Bayesian networks for radiologist decision support during clinical interpretation. This computational approach has the advantage of avoiding incorrect diagnoses that result from known human cognitive biases such as anchoring bias, framing effect, availability bias, and premature closure. To integrate Bayesian networks into clinical practice, we developed an open-source web application that provides diagnostic support for a variety of radiology disease entities (e.g., basal ganglia diseases, bone lesions). The Clinical tool presents the user with a set of buttons representing clinical and imaging features of interest. These buttons are used to set the value for each observed feature. As features are identified, the conditional probabilities for each possible diagnosis are updated in real time. Additionally, using sensitivity analysis, the interface may be set to inform the user which remaining imaging features provide maximum discriminatory information to choose the most likely diagnosis. The Case Submission tools allow the user to submit a validated case and the associated imaging features to a database, which can then be used for future tuning/testing of the Bayesian networks. These submitted cases are then reviewed by an assigned expert using the provided QC tool. The Research tool presents users with cases with previously labeled features and a chosen diagnosis, for the purpose of performance evaluation. Similarly, the Education page presents cases with known features, but provides real time feedback on feature selection.

Collaborative autonomous sensing with Bayesians in the loop

NASA Astrophysics Data System (ADS)

Ahmed, Nisar

2016-10-01

There is a strong push to develop intelligent unmanned autonomy that complements human reasoning for applications as diverse as wilderness search and rescue, military surveillance, and robotic space exploration. More than just replacing humans for `dull, dirty and dangerous' work, autonomous agents are expected to cope with a whole host of uncertainties while working closely together with humans in new situations. The robotics revolution firmly established the primacy of Bayesian algorithms for tackling challenging perception, learning and decision-making problems. Since the next frontier of autonomy demands the ability to gather information across stretches of time and space that are beyond the reach of a single autonomous agent, the next generation of Bayesian algorithms must capitalize on opportunities to draw upon the sensing and perception abilities of humans-in/on-the-loop. This work summarizes our recent research toward harnessing `human sensors' for information gathering tasks. The basic idea behind is to allow human end users (i.e. non-experts in robotics, statistics, machine learning, etc.) to directly `talk to' the information fusion engine and perceptual processes aboard any autonomous agent. Our approach is grounded in rigorous Bayesian modeling and fusion of flexible semantic information derived from user-friendly interfaces, such as natural language chat and locative hand-drawn sketches. This naturally enables `plug and play' human sensing with existing probabilistic algorithms for planning and perception, and has been successfully demonstrated with human-robot teams in target localization applications.

Computer-aided diagnosis system: a Bayesian hybrid classification method.

PubMed

Calle-Alonso, F; Pérez, C J; Arias-Nicolás, J P; Martín, J

2013-10-01

A novel method to classify multi-class biomedical objects is presented. The method is based on a hybrid approach which combines pairwise comparison, Bayesian regression and the k-nearest neighbor technique. It can be applied in a fully automatic way or in a relevance feedback framework. In the latter case, the information obtained from both an expert and the automatic classification is iteratively used to improve the results until a certain accuracy level is achieved, then, the learning process is finished and new classifications can be automatically performed. The method has been applied in two biomedical contexts by following the same cross-validation schemes as in the original studies. The first one refers to cancer diagnosis, leading to an accuracy of 77.35% versus 66.37%, originally obtained. The second one considers the diagnosis of pathologies of the vertebral column. The original method achieves accuracies ranging from 76.5% to 96.7%, and from 82.3% to 97.1% in two different cross-validation schemes. Even with no supervision, the proposed method reaches 96.71% and 97.32% in these two cases. By using a supervised framework the achieved accuracy is 97.74%. Furthermore, all abnormal cases were correctly classified. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

A spatial Bayesian network model to assess the benefits of early warning for urban flood risk to people

NASA Astrophysics Data System (ADS)

Balbi, S.; Villa, F.; Mojtahed, V.; Hegetschweiler, K. T.; Giupponi, C.

2015-10-01

This article presents a novel methodology to assess flood risk to people by integrating people's vulnerability and ability to cushion hazards through coping and adapting. The proposed approach extends traditional risk assessments beyond material damages; complements quantitative and semi-quantitative data with subjective and local knowledge, improving the use of commonly available information; produces estimates of model uncertainty by providing probability distributions for all of its outputs. Flood risk to people is modeled using a spatially explicit Bayesian network model calibrated on expert opinion. Risk is assessed in terms of: (1) likelihood of non-fatal physical injury; (2) likelihood of post-traumatic stress disorder; (3) likelihood of death. The study area covers the lower part of the Sihl valley (Switzerland) including the city of Zurich. The model is used to estimate the benefits of improving an existing Early Warning System, taking into account the reliability, lead-time and scope (i.e. coverage of people reached by the warning). Model results indicate that the potential benefits of an improved early warning in terms of avoided human impacts are particularly relevant in case of a major flood event: about 75 % of fatalities, 25 % of injuries and 18 % of post-traumatic stress disorders could be avoided.

Linking Structural Equation Modelling with Bayesian Network and Coastal Phytoplankton Dynamics in Bohai Bay

NASA Astrophysics Data System (ADS)

Chu, Jiangtao; Yang, Yue

2018-06-01

Bayesian networks (BN) have many advantages over other methods in ecological modelling and have become an increasingly popular modelling tool. However, BN are flawed in regard to building models based on inadequate existing knowledge. To overcome this limitation, we propose a new method that links BN with structural equation modelling (SEM). In this method, SEM is used to improve the model structure for BN. This method was used to simulate coastal phytoplankton dynamics in Bohai Bay. We demonstrate that this hybrid approach minimizes the need for expert elicitation, generates more reasonable structures for BN models and increases the BN model's accuracy and reliability. These results suggest that the inclusion of SEM for testing and verifying the theoretical structure during the initial construction stage improves the effectiveness of BN models, especially for complex eco-environment systems. The results also demonstrate that in Bohai Bay, while phytoplankton biomass has the greatest influence on phytoplankton dynamics, the impact of nutrients on phytoplankton dynamics is larger than the influence of the physical environment in summer. Furthermore, despite the Redfield ratio indicating that phosphorus should be the primary nutrient limiting factor, our results indicate that silicate plays the most important role in regulating phytoplankton dynamics in Bohai Bay.

Are Security Experts Useful? Bayesian Nash Equilibria for Network Security Games with Limited Information

DTIC Science & Technology

2010-04-23

from the stock market. Journal of Computer Security, 11(3):431–448, 2003. [7] D . Chaum . Untraceable electronic mail, return addresses, and digital...to cross several other administrative boundaries (e.g., ISPs C, D , . . .), causing potential congestion at all of these intermediaries. A very...Quarterly Journal of Economics, 108(1):259–271, February 1993. [9] P. Ferguson and D . Senie. Network ingress filtering: Defeating denial of service

Systems Modelling of the Socio-Technical Aspects of Residential Electricity Use and Network Peak Demand.

PubMed

Lewis, Jim; Mengersen, Kerrie; Buys, Laurie; Vine, Desley; Bell, John; Morris, Peter; Ledwich, Gerard

2015-01-01

Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers' peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers' location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price, managed supply, etc., in a conceptual 'map' of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tickbox interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments.

Systems Modelling of the Socio-Technical Aspects of Residential Electricity Use and Network Peak Demand

PubMed Central

Lewis, Jim; Mengersen, Kerrie; Buys, Laurie; Vine, Desley; Bell, John; Morris, Peter; Ledwich, Gerard

2015-01-01

Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers’ peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers’ location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price, managed supply, etc., in a conceptual ‘map’ of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tickbox interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments. PMID:26226511

Under which conditions, additional monitoring data are worth gathering for improving decision making? Application of the VOI theory in the Bayesian Event Tree eruption forecasting framework

NASA Astrophysics Data System (ADS)

Loschetter, Annick; Rohmer, Jérémy

2016-04-01

Standard and new generation of monitoring observations provide in almost real-time important information about the evolution of the volcanic system. These observations are used to update the model and contribute to a better hazard assessment and to support decision making concerning potential evacuation. The framework BET_EF (based on Bayesian Event Tree) developed by INGV enables dealing with the integration of information from monitoring with the prospect of decision making. Using this framework, the objectives of the present work are i. to propose a method to assess the added value of information (within the Value Of Information (VOI) theory) from monitoring; ii. to perform sensitivity analysis on the different parameters that influence the VOI from monitoring. VOI consists in assessing the possible increase in expected value provided by gathering information, for instance through monitoring. Basically, the VOI is the difference between the value with information and the value without additional information in a Cost-Benefit approach. This theory is well suited to deal with situations that can be represented in the form of a decision tree such as the BET_EF tool. Reference values and ranges of variation (for sensitivity analysis) were defined for input parameters, based on data from the MESIMEX exercise (performed at Vesuvio volcano in 2006). Complementary methods for sensitivity analyses were implemented: local, global using Sobol' indices and regional using Contribution to Sample Mean and Variance plots. The results (specific to the case considered) obtained with the different techniques are in good agreement and enable answering the following questions: i. Which characteristics of monitoring are important for early warning (reliability)? ii. How do experts' opinions influence the hazard assessment and thus the decision? Concerning the characteristics of monitoring, the more influent parameters are the means rather than the variances for the case considered. For the parameters that concern expert setting, the weight attributed to monitoring measurement ω, the mean of thresholds, the economic context and the setting of the decision threshold are very influential. The interest of applying the VOI theory (more precisely the value of imperfect information) in the BET framework was demonstrated as support for helping experts in the setting of the monitoring system or for helping managers to decide the installation of additional monitoring systems. Acknowledgments: This work was carried out in the framework of the project MEDSUV. This project is funded under the call FP7 ENV.2012.6.4-2: Long-term monitoring experiment in geologically active regions of Europe prone to natural hazards: the Supersite concept. Grant agreement n°308665.

Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft

NASA Technical Reports Server (NTRS)

Mengshoel, Ole Jakob; Poll, Scott; Kurtoglu, Tolga

2009-01-01

In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability of the compositional approach, we briefly report on experimental results from the diagnostic competition DXC, where the ProADAPT team, using techniques discussed here, obtained the highest scores in both Tier 1 (among 9 international competitors) and Tier 2 (among 6 international competitors) of the industrial track. While we consider diagnosis of power systems specifically, we believe this work is relevant to other system health management problems, in particular in dependable systems such as aircraft and spacecraft. (See CASI ID 20100021910 for supplemental data disk.)

Safety Risk Knowledge Elicitation in Support of Aeronautical R and D Portfolio Management: A Case Study

NASA Technical Reports Server (NTRS)

Shih, Ann T.; Ancel, Ersin; Jones, Sharon Monica; Reveley, Mary S.; Luxhoj, James T.

2012-01-01

Aviation is a problem domain characterized by a high level of system complexity and uncertainty. Safety risk analysis in such a domain is especially challenging given the multitude of operations and diverse stakeholders. The Federal Aviation Administration (FAA) projects that by 2025 air traffic will increase by more than 50 percent with 1.1 billion passengers a year and more than 85,000 flights every 24 hours contributing to further delays and congestion in the sky (Circelli, 2011). This increased system complexity necessitates the application of structured safety risk analysis methods to understand and eliminate where possible, reduce, and/or mitigate risk factors. The use of expert judgments for probabilistic safety analysis in such a complex domain is necessary especially when evaluating the projected impact of future technologies, capabilities, and procedures for which current operational data may be scarce. Management of an R&D product portfolio in such a dynamic domain needs a systematic process to elicit these expert judgments, process modeling results, perform sensitivity analyses, and efficiently communicate the modeling results to decision makers. In this paper a case study focusing on the application of an R&D portfolio of aeronautical products intended to mitigate aircraft Loss of Control (LOC) accidents is presented. In particular, the knowledge elicitation process with three subject matter experts who contributed to the safety risk model is emphasized. The application and refinement of a verbal-numerical scale for conditional probability elicitation in a Bayesian Belief Network (BBN) is discussed. The preliminary findings from this initial step of a three-part elicitation are important to project management practitioners as they illustrate the vital contribution of systematic knowledge elicitation in complex domains.

BATMAN--an R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a Bayesian model.

PubMed

Hao, Jie; Astle, William; De Iorio, Maria; Ebbels, Timothy M D

2012-08-01

Nuclear Magnetic Resonance (NMR) spectra are widely used in metabolomics to obtain metabolite profiles in complex biological mixtures. Common methods used to assign and estimate concentrations of metabolites involve either an expert manual peak fitting or extra pre-processing steps, such as peak alignment and binning. Peak fitting is very time consuming and is subject to human error. Conversely, alignment and binning can introduce artefacts and limit immediate biological interpretation of models. We present the Bayesian automated metabolite analyser for NMR spectra (BATMAN), an R package that deconvolutes peaks from one-dimensional NMR spectra, automatically assigns them to specific metabolites from a target list and obtains concentration estimates. The Bayesian model incorporates information on characteristic peak patterns of metabolites and is able to account for shifts in the position of peaks commonly seen in NMR spectra of biological samples. It applies a Markov chain Monte Carlo algorithm to sample from a joint posterior distribution of the model parameters and obtains concentration estimates with reduced error compared with conventional numerical integration and comparable to manual deconvolution by experienced spectroscopists. http://www1.imperial.ac.uk/medicine/people/t.ebbels/ t.ebbels@imperial.ac.uk.

Quantum Bayesian perspective for intelligence reservoir characterization, monitoring and management

NASA Astrophysics Data System (ADS)

Lozada Aguilar, Miguel Ángel; Khrennikov, Andrei; Oleschko, Klaudia; de Jesús Correa, María

2017-10-01

The paper starts with a brief review of the literature about uncertainty in geological, geophysical and petrophysical data. In particular, we present the viewpoints of experts in geophysics on the application of Bayesian inference and subjective probability. Then we present arguments that the use of classical probability theory (CP) does not match completely the structure of geophysical data. We emphasize that such data are characterized by contextuality and non-Kolmogorovness (the impossibility to use the CP model), incompleteness as well as incompatibility of some geophysical measurements. These characteristics of geophysical data are similar to the characteristics of quantum physical data. Notwithstanding all this, contextuality can be seen as a major deviation of quantum theory from classical physics. In particular, the contextual probability viewpoint is the essence of the Växjö interpretation of quantum mechanics. We propose to use quantum probability (QP) for decision-making during the characterization, modelling, exploring and management of the intelligent hydrocarbon reservoir. Quantum Bayesianism (QBism), one of the recently developed information interpretations of quantum theory, can be used as the interpretational basis for such QP decision-making in geology, geophysics and petroleum projects design and management. This article is part of the themed issue `Second quantum revolution: foundational questions'.

Bayes-LQAS: classifying the prevalence of global acute malnutrition

PubMed Central

2010-01-01

Lot Quality Assurance Sampling (LQAS) applications in health have generally relied on frequentist interpretations for statistical validity. Yet health professionals often seek statements about the probability distribution of unknown parameters to answer questions of interest. The frequentist paradigm does not pretend to yield such information, although a Bayesian formulation might. This is the source of an error made in a recent paper published in this journal. Many applications lend themselves to a Bayesian treatment, and would benefit from such considerations in their design. We discuss Bayes-LQAS (B-LQAS), which allows for incorporation of prior information into the LQAS classification procedure, and thus shows how to correct the aforementioned error. Further, we pay special attention to the formulation of Bayes Operating Characteristic Curves and the use of prior information to improve survey designs. As a motivating example, we discuss the classification of Global Acute Malnutrition prevalence and draw parallels between the Bayes and classical classifications schemes. We also illustrate the impact of informative and non-informative priors on the survey design. Results indicate that using a Bayesian approach allows the incorporation of expert information and/or historical data and is thus potentially a valuable tool for making accurate and precise classifications. PMID:20534159

Bayes-LQAS: classifying the prevalence of global acute malnutrition.

PubMed

Olives, Casey; Pagano, Marcello

2010-06-09

Lot Quality Assurance Sampling (LQAS) applications in health have generally relied on frequentist interpretations for statistical validity. Yet health professionals often seek statements about the probability distribution of unknown parameters to answer questions of interest. The frequentist paradigm does not pretend to yield such information, although a Bayesian formulation might. This is the source of an error made in a recent paper published in this journal. Many applications lend themselves to a Bayesian treatment, and would benefit from such considerations in their design. We discuss Bayes-LQAS (B-LQAS), which allows for incorporation of prior information into the LQAS classification procedure, and thus shows how to correct the aforementioned error. Further, we pay special attention to the formulation of Bayes Operating Characteristic Curves and the use of prior information to improve survey designs. As a motivating example, we discuss the classification of Global Acute Malnutrition prevalence and draw parallels between the Bayes and classical classifications schemes. We also illustrate the impact of informative and non-informative priors on the survey design. Results indicate that using a Bayesian approach allows the incorporation of expert information and/or historical data and is thus potentially a valuable tool for making accurate and precise classifications.

The DNA database search controversy revisited: bridging the Bayesian-frequentist gap.

PubMed

Storvik, Geir; Egeland, Thore

2007-09-01

Two different quantities have been suggested for quantification of evidence in cases where a suspect is found by a search through a database of DNA profiles. The likelihood ratio, typically motivated from a Bayesian setting, is preferred by most experts in the field. The so-called np rule has been suggested through frequentist arguments and has been suggested by the American National Research Council and Stockmarr (1999, Biometrics55, 671-677). The two quantities differ substantially and have given rise to the DNA database search controversy. Although several authors have criticized the different approaches, a full explanation of why these differences appear is still lacking. In this article we show that a P-value in a frequentist hypothesis setting is approximately equal to the result of the np rule. We argue, however, that a more reasonable procedure in this case is to use conditional testing, in which case a P-value directly related to posterior probabilities and the likelihood ratio is obtained. This way of viewing the problem bridges the gap between the Bayesian and frequentist approaches. At the same time it indicates that the np rule should not be used to quantify evidence.

Diagnosis and Reconfiguration using Bayesian Networks: An Electrical Power System Case Study

NASA Technical Reports Server (NTRS)

Knox, W. Bradley; Mengshoel, Ole

2009-01-01

Automated diagnosis and reconfiguration are important computational techniques that aim to minimize human intervention in autonomous systems. In this paper, we develop novel techniques and models in the context of diagnosis and reconfiguration reasoning using causal Bayesian networks (BNs). We take as starting point a successful diagnostic approach, using a static BN developed for a real-world electrical power system. We discuss in this paper the extension of this diagnostic approach along two dimensions, namely: (i) from a static BN to a dynamic BN; and (ii) from a diagnostic task to a reconfiguration task. More specifically, we discuss the auto-generation of a dynamic Bayesian network from a static Bayesian network. In addition, we discuss subtle, but important, differences between Bayesian networks when used for diagnosis versus reconfiguration. We discuss a novel reconfiguration agent, which models a system causally, including effects of actions through time, using a dynamic Bayesian network. Though the techniques we discuss are general, we demonstrate them in the context of electrical power systems (EPSs) for aircraft and spacecraft. EPSs are vital subsystems on-board aircraft and spacecraft, and many incidents and accidents of these vehicles have been attributed to EPS failures. We discuss a case study that provides initial but promising results for our approach in the setting of electrical power systems.

Using a web-based application to define the accuracy of diagnostic tests when the gold standard is imperfect.

PubMed

Lim, Cherry; Wannapinij, Prapass; White, Lisa; Day, Nicholas P J; Cooper, Ben S; Peacock, Sharon J; Limmathurotsakul, Direk

2013-01-01

Estimates of the sensitivity and specificity for new diagnostic tests based on evaluation against a known gold standard are imprecise when the accuracy of the gold standard is imperfect. Bayesian latent class models (LCMs) can be helpful under these circumstances, but the necessary analysis requires expertise in computational programming. Here, we describe open-access web-based applications that allow non-experts to apply Bayesian LCMs to their own data sets via a user-friendly interface. Applications for Bayesian LCMs were constructed on a web server using R and WinBUGS programs. The models provided (http://mice.tropmedres.ac) include two Bayesian LCMs: the two-tests in two-population model (Hui and Walter model) and the three-tests in one-population model (Walter and Irwig model). Both models are available with simplified and advanced interfaces. In the former, all settings for Bayesian statistics are fixed as defaults. Users input their data set into a table provided on the webpage. Disease prevalence and accuracy of diagnostic tests are then estimated using the Bayesian LCM, and provided on the web page within a few minutes. With the advanced interfaces, experienced researchers can modify all settings in the models as needed. These settings include correlation among diagnostic test results and prior distributions for all unknown parameters. The web pages provide worked examples with both models using the original data sets presented by Hui and Walter in 1980, and by Walter and Irwig in 1988. We also illustrate the utility of the advanced interface using the Walter and Irwig model on a data set from a recent melioidosis study. The results obtained from the web-based applications were comparable to those published previously. The newly developed web-based applications are open-access and provide an important new resource for researchers worldwide to evaluate new diagnostic tests.

A combined Fuzzy and Naive Bayesian strategy can be used to assign event codes to injury narratives.

PubMed

Marucci-Wellman, H; Lehto, M; Corns, H

2011-12-01

Bayesian methods show promise for classifying injury narratives from large administrative datasets into cause groups. This study examined a combined approach where two Bayesian models (Fuzzy and Naïve) were used to either classify a narrative or select it for manual review. Injury narratives were extracted from claims filed with a worker's compensation insurance provider between January 2002 and December 2004. Narratives were separated into a training set (n=11,000) and prediction set (n=3,000). Expert coders assigned two-digit Bureau of Labor Statistics Occupational Injury and Illness Classification event codes to each narrative. Fuzzy and Naïve Bayesian models were developed using manually classified cases in the training set. Two semi-automatic machine coding strategies were evaluated. The first strategy assigned cases for manual review if the Fuzzy and Naïve models disagreed on the classification. The second strategy selected additional cases for manual review from the Agree dataset using prediction strength to reach a level of 50% computer coding and 50% manual coding. When agreement alone was used as the filtering strategy, the majority were coded by the computer (n=1,928, 64%) leaving 36% for manual review. The overall combined (human plus computer) sensitivity was 0.90 and positive predictive value (PPV) was >0.90 for 11 of 18 2-digit event categories. Implementing the 2nd strategy improved results with an overall sensitivity of 0.95 and PPV >0.90 for 17 of 18 categories. A combined Naïve-Fuzzy Bayesian approach can classify some narratives with high accuracy and identify others most beneficial for manual review, reducing the burden on human coders.

Imprecise Probability Methods for Weapons UQ

DOE Office of Scientific and Technical Information (OSTI.GOV)

Picard, Richard Roy; Vander Wiel, Scott Alan

Building on recent work in uncertainty quanti cation, we examine the use of imprecise probability methods to better characterize expert knowledge and to improve on misleading aspects of Bayesian analysis with informative prior distributions. Quantitative approaches to incorporate uncertainties in weapons certi cation are subject to rigorous external peer review, and in this regard, certain imprecise probability methods are well established in the literature and attractive. These methods are illustrated using experimental data from LANL detonator impact testing.

Instruction in information structuring improves Bayesian judgment in intelligence analysts.

PubMed

Mandel, David R

2015-01-01

An experiment was conducted to test the effectiveness of brief instruction in information structuring (i.e., representing and integrating information) for improving the coherence of probability judgments and binary choices among intelligence analysts. Forty-three analysts were presented with comparable sets of Bayesian judgment problems before and immediately after instruction. After instruction, analysts' probability judgments were more coherent (i.e., more additive and compliant with Bayes theorem). Instruction also improved the coherence of binary choices regarding category membership: after instruction, subjects were more likely to invariably choose the category to which they assigned the higher probability of a target's membership. The research provides a rare example of evidence-based validation of effectiveness in instruction to improve the statistical assessment skills of intelligence analysts. Such instruction could also be used to improve the assessment quality of other types of experts who are required to integrate statistical information or make probabilistic assessments.

Comparative Analysis of Automatic Exudate Detection between Machine Learning and Traditional Approaches

NASA Astrophysics Data System (ADS)

Sopharak, Akara; Uyyanonvara, Bunyarit; Barman, Sarah; Williamson, Thomas

To prevent blindness from diabetic retinopathy, periodic screening and early diagnosis are neccessary. Due to lack of expert ophthalmologists in rural area, automated early exudate (one of visible sign of diabetic retinopathy) detection could help to reduce the number of blindness in diabetic patients. Traditional automatic exudate detection methods are based on specific parameter configuration, while the machine learning approaches which seems more flexible may be computationally high cost. A comparative analysis of traditional and machine learning of exudates detection, namely, mathematical morphology, fuzzy c-means clustering, naive Bayesian classifier, Support Vector Machine and Nearest Neighbor classifier are presented. Detected exudates are validated with expert ophthalmologists' hand-drawn ground-truths. The sensitivity, specificity, precision, accuracy and time complexity of each method are also compared.

A mathematical proof and example that Bayes's Theorem is fundamental to actuarial estimates of sexual recidivism risk.

PubMed

Donaldson, Theodore; Wollert, Richard

2008-06-01

Expert witnesses in sexually violent predator (SVP) cases often rely on actuarial instruments to make risk determinations. Many questions surround their use, however. Bayes's Theorem holds much promise for addressing these questions. Some experts nonetheless claim that Bayesian analyses are inadmissible in SVP cases because they are not accepted by the relevant scientific community. This position is illogical because Bayes's Theorem is simply a probabilistic restatement of the way that frequency data are combined to arrive at whatever recidivism rates are paired with each test score in an actuarial table. This article presents a mathematical proof and example validating this assertion. The advantages and implications of a logic model that combines Bayes's Theorem and the null hypothesis are also discussed.

Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft

NASA Technical Reports Server (NTRS)

Mengshoel, Ole Jakob; Poll, Scott; Kurtoglu, Tolga

2009-01-01

This CD contains files that support the talk (see CASI ID 20100021404). There are 24 models that relate to the ADAPT system and 1 Excel worksheet. In the paper an investigation into the use of Bayesian networks to construct large-scale diagnostic systems is described. The high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems are described in the talk. The data in the CD are the models of the 24 different power systems.

Bayesian networks for evaluation of evidence from forensic entomology.

PubMed

Andersson, M Gunnar; Sundström, Anders; Lindström, Anders

2013-09-01

In the aftermath of a CBRN incident, there is an urgent need to reconstruct events in order to bring the perpetrators to court and to take preventive actions for the future. The challenge is to discriminate, based on available information, between alternative scenarios. Forensic interpretation is used to evaluate to what extent results from the forensic investigation favor the prosecutors' or the defendants' arguments, using the framework of Bayesian hypothesis testing. Recently, several new scientific disciplines have been used in a forensic context. In the AniBioThreat project, the framework was applied to veterinary forensic pathology, tracing of pathogenic microorganisms, and forensic entomology. Forensic entomology is an important tool for estimating the postmortem interval in, for example, homicide investigations as a complement to more traditional methods. In this article we demonstrate the applicability of the Bayesian framework for evaluating entomological evidence in a forensic investigation through the analysis of a hypothetical scenario involving suspect movement of carcasses from a clandestine laboratory. Probabilities of different findings under the alternative hypotheses were estimated using a combination of statistical analysis of data, expert knowledge, and simulation, and entomological findings are used to update the beliefs about the prosecutors' and defendants' hypotheses and to calculate the value of evidence. The Bayesian framework proved useful for evaluating complex hypotheses using findings from several insect species, accounting for uncertainty about development rate, temperature, and precolonization. The applicability of the forensic statistic approach to evaluating forensic results from a CBRN incident is discussed.

The Psychology of Bayesian Reasoning

DTIC Science & Technology

2014-10-21

The psychology of Bayesian reasoning David R. Mandel* Socio-Cognitive Systems Section, Defence Research and Development Canada and Department...belief revision, subjective probability, human judgment, psychological methods. Most psychological research on Bayesian reasoning since the 1970s has...attention to some important problems with the conventional approach to studying Bayesian reasoning in psychology that has been dominant since the

Probabilistic techniques for obtaining accurate patient counts in Clinical Data Warehouses

PubMed Central

Myers, Risa B.; Herskovic, Jorge R.

2011-01-01

Proposal and execution of clinical trials, computation of quality measures and discovery of correlation between medical phenomena are all applications where an accurate count of patients is needed. However, existing sources of this type of patient information, including Clinical Data Warehouses (CDW) may be incomplete or inaccurate. This research explores applying probabilistic techniques, supported by the MayBMS probabilistic database, to obtain accurate patient counts from a clinical data warehouse containing synthetic patient data. We present a synthetic clinical data warehouse (CDW), and populate it with simulated data using a custom patient data generation engine. We then implement, evaluate and compare different techniques for obtaining patients counts. We model billing as a test for the presence of a condition. We compute billing’s sensitivity and specificity both by conducting a “Simulated Expert Review” where a representative sample of records are reviewed and labeled by experts, and by obtaining the ground truth for every record. We compute the posterior probability of a patient having a condition through a “Bayesian Chain”, using Bayes’ Theorem to calculate the probability of a patient having a condition after each visit. The second method is a “one-shot” approach that computes the probability of a patient having a condition based on whether the patient is ever billed for the condition Our results demonstrate the utility of probabilistic approaches, which improve on the accuracy of raw counts. In particular, the simulated review paired with a single application of Bayes’ Theorem produces the best results, with an average error rate of 2.1% compared to 43.7% for the straightforward billing counts. Overall, this research demonstrates that Bayesian probabilistic approaches improve patient counts on simulated patient populations. We believe that total patient counts based on billing data are one of the many possible applications of our Bayesian framework. Use of these probabilistic techniques will enable more accurate patient counts and better results for applications requiring this metric. PMID:21986292

A Bayesian approach to multivariate measurement system assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamada, Michael Scott

This article considers system assessment for multivariate measurements and presents a Bayesian approach to analyzing gauge R&R study data. The evaluation of variances for univariate measurement becomes the evaluation of covariance matrices for multivariate measurements. The Bayesian approach ensures positive definite estimates of the covariance matrices and easily provides their uncertainty. Furthermore, various measurement system assessment criteria are easily evaluated. The approach is illustrated with data from a real gauge R&R study as well as simulated data.

A Bayesian approach to multivariate measurement system assessment

DOE PAGES

Hamada, Michael Scott

2016-07-01

This article considers system assessment for multivariate measurements and presents a Bayesian approach to analyzing gauge R&R study data. The evaluation of variances for univariate measurement becomes the evaluation of covariance matrices for multivariate measurements. The Bayesian approach ensures positive definite estimates of the covariance matrices and easily provides their uncertainty. Furthermore, various measurement system assessment criteria are easily evaluated. The approach is illustrated with data from a real gauge R&R study as well as simulated data.

Pragmatic precision oncology: the secondary uses of clinical tumor molecular profiling

PubMed Central

Thota, Ramya; Staggs, David B; Johnson, Douglas B; Warner, Jeremy L

2016-01-01

Background Precision oncology increasingly utilizes molecular profiling of tumors to determine treatment decisions with targeted therapeutics. The molecular profiling data is valuable in the treatment of individual patients as well as for multiple secondary uses. Objective To automatically parse, categorize, and aggregate clinical molecular profile data generated during cancer care as well as use this data to address multiple secondary use cases. Methods A system to parse, categorize and aggregate molecular profile data was created. A naÿve Bayesian classifier categorized results according to clinical groups. The accuracy of these systems were validated against a published expertly-curated subset of molecular profiling data. Results Following one year of operation, 819 samples have been accurately parsed and categorized to generate a data repository of 10,620 genetic variants. The database has been used for operational, clinical trial, and discovery science research. Conclusions A real-time database of molecular profiling data is a pragmatic solution to several knowledge management problems in the practice and science of precision oncology. PMID:27026612

Assessing Vermont's stream health and biological integrity using artificial neural networks and Bayesian methods

NASA Astrophysics Data System (ADS)

Rizzo, D. M.; Fytilis, N.; Stevens, L.

2012-12-01

Environmental managers are increasingly required to monitor and forecast long-term effects and vulnerability of biophysical systems to human-generated stresses. Ideally, a study involving both physical and biological assessments conducted concurrently (in space and time) could provide a better understanding of the mechanisms and complex relationships. However, costs and resources associated with monitoring the complex linkages between the physical, geomorphic and habitat conditions and the biological integrity of stream reaches are prohibitive. Researchers have used classification techniques to place individual streams and rivers into a broader spatial context (hydrologic or health condition). Such efforts require environmental managers to gather multiple forms of information - quantitative, qualitative and subjective. We research and develop a novel classification tool that combines self-organizing maps with a Naïve Bayesian classifier to direct resources to stream reaches most in need. The Vermont Agency of Natural Resources has developed and adopted protocols for physical stream geomorphic and habitat assessments throughout the state of Vermont. Separate from these assessments, the Vermont Department of Environmental Conservation monitors the biological communities and the water quality in streams. Our initial hypothesis is that the geomorphic reach assessments and water quality data may be leveraged to reduce error and uncertainty associated with predictions of biological integrity and stream health. We test our hypothesis using over 2500 Vermont stream reaches (~1371 stream miles) assessed by the two agencies. In the development of this work, we combine a Naïve Bayesian classifier with a modified Kohonen Self-Organizing Map (SOM). The SOM is an unsupervised artificial neural network that autonomously analyzes inherent dataset properties using input data only. It is typically used to cluster data into similar categories when a priori classes do not exist. The incorporation of a Bayesian classifier allows one to explicitly incorporate existing knowledge and expert opinion into the data analysis. Since classification plays a leading role in the future development of data-enabled science and engineering, such a computational tool is applicable to a variety of proactive adaptive watershed management applications.

Interactive Inverse Groundwater Modeling - Addressing User Fatigue

NASA Astrophysics Data System (ADS)

Singh, A.; Minsker, B. S.

2006-12-01

This paper builds on ongoing research on developing an interactive and multi-objective framework to solve the groundwater inverse problem. In this work we solve the classic groundwater inverse problem of estimating a spatially continuous conductivity field, given field measurements of hydraulic heads. The proposed framework is based on an interactive multi-objective genetic algorithm (IMOGA) that not only considers quantitative measures such as calibration error and degree of regularization, but also takes into account expert knowledge about the structure of the underlying conductivity field expressed as subjective rankings of potential conductivity fields by the expert. The IMOGA converges to the optimal Pareto front representing the best trade- off among the qualitative as well as quantitative objectives. However, since the IMOGA is a population-based iterative search it requires the user to evaluate hundreds of solutions. This leads to the problem of 'user fatigue'. We propose a two step methodology to combat user fatigue in such interactive systems. The first step is choosing only a few highly representative solutions to be shown to the expert for ranking. Spatial clustering is used to group the search space based on the similarity of the conductivity fields. Sampling is then carried out from different clusters to improve the diversity of solutions shown to the user. Once the expert has ranked representative solutions from each cluster a machine learning model is used to 'learn user preference' and extrapolate these for the solutions not ranked by the expert. We investigate different machine learning models such as Decision Trees, Bayesian learning model, and instance based weighting to model user preference. In addition, we also investigate ways to improve the performance of these models by providing information about the spatial structure of the conductivity fields (which is what the expert bases his or her rank on). Results are shown for each of these machine learning models and the advantages and disadvantages for each approach are discussed. These results indicate that using the proposed two-step methodology leads to significant reduction in user-fatigue without deteriorating the solution quality of the IMOGA.

Fossil Signatures Using Elemental Abundance Distributions and Bayesian Probabilistic Classification

NASA Technical Reports Server (NTRS)

Hoover, Richard B.; Storrie-Lombardi, Michael C.

2004-01-01

Elemental abundances (C6, N7, O8, Na11, Mg12, Al3, P15, S16, Cl17, K19, Ca20, Ti22, Mn25, Fe26, and Ni28) were obtained for a set of terrestrial fossils and the rock matrix surrounding them. Principal Component Analysis extracted five factors accounting for the 92.5% of the data variance, i.e. information content, of the elemental abundance data. Hierarchical Cluster Analysis provided unsupervised sample classification distinguishing fossil from matrix samples on the basis of either raw abundances or PCA input that agreed strongly with visual classification. A stochastic, non-linear Artificial Neural Network produced a Bayesian probability of correct sample classification. The results provide a quantitative probabilistic methodology for discriminating terrestrial fossils from the surrounding rock matrix using chemical information. To demonstrate the applicability of these techniques to the assessment of meteoritic samples or in situ extraterrestrial exploration, we present preliminary data on samples of the Orgueil meteorite. In both systems an elemental signature produces target classification decisions remarkably consistent with morphological classification by a human expert using only structural (visual) information. We discuss the possibility of implementing a complexity analysis metric capable of automating certain image analysis and pattern recognition abilities of the human eye using low magnification optical microscopy images and discuss the extension of this technique across multiple scales.

Linking urbanization to the Biological Condition Gradient (BCG) for stream ecosystems in the Northeastern United States using a Bayesian network approach

USGS Publications Warehouse

Kashuba, Roxolana; McMahon, Gerard; Cuffney, Thomas F.; Qian, Song; Reckhow, Kenneth; Gerritsen, Jeroen; Davies, Susan

2012-01-01

In realization of the aforementioned advantages, a Bayesian network model was constructed to characterize the effect of urban development on aquatic macroinvertebrate stream communities through three simultaneous, interacting ecological pathways affecting stream hydrology, habitat, and water quality across watersheds in the Northeastern United States. This model incorporates both empirical data and expert knowledge to calculate the probabilities of attaining desired aquatic ecosystem conditions under different urban stress levels, environmental conditions, and management options. Ecosystem conditions are characterized in terms of standardized Biological Condition Gradient (BCG) management endpoints. This approach to evaluating urban development-induced perturbations in watersheds integrates statistical and mechanistic perspectives, different information sources, and several ecological processes into a comprehensive description of the system that can be used to support decision making. The completed model can be used to infer which management actions would lead to the highest likelihood of desired BCG tier achievement. For example, if best management practices (BMP) were implemented in a highly urbanized watershed to reduce flashiness to medium levels and specific conductance to low levels, the stream would have a 70-percent chance of achieving BCG Tier 3 or better, relative to a 24-percent achievement likelihood for unmanaged high urban land cover. Results are reported probabilistically to account for modeling uncertainty that is inherent in sources such as natural variability and model simplification error.

Using Highlighting to Train Attentional Expertise

PubMed Central

Roads, Brett; Mozer, Michael C.; Busey, Thomas A.

2016-01-01

Acquiring expertise in complex visual tasks is time consuming. To facilitate the efficient training of novices on where to look in these tasks, we propose an attentional highlighting paradigm. Highlighting involves dynamically modulating the saliency of a visual image to guide attention along the fixation path of a domain expert who had previously viewed the same image. In Experiment 1, we trained naive subjects via attentional highlighting on a fingerprint-matching task. Before and after training, we asked subjects to freely inspect images containing pairs of prints and determine whether the prints matched. Fixation sequences were automatically scored for the degree of expertise exhibited using a Bayesian discriminative model of novice and expert gaze behavior. Highlighted training causes gaze behavior to become more expert-like not only on the trained images but also on transfer images, indicating generalization of learning. In Experiment 2, to control for the possibility that the increase in expertise is due to mere exposure, we trained subjects via highlighting of fixation sequences from novices, not experts, and observed no transition toward expertise. In Experiment 3, to determine the specificity of the training effect, we trained subjects with expert fixation sequences from images other than the one being viewed, which preserves coarse-scale statistics of expert gaze but provides no information about fine-grain features. Observing at least a partial transition toward expertise, we obtain only weak evidence that the highlighting procedure facilitates the learning of critical local features. We discuss possible improvements to the highlighting procedure. PMID:26744839

Using Highlighting to Train Attentional Expertise.

PubMed

Roads, Brett; Mozer, Michael C; Busey, Thomas A

2016-01-01

Acquiring expertise in complex visual tasks is time consuming. To facilitate the efficient training of novices on where to look in these tasks, we propose an attentional highlighting paradigm. Highlighting involves dynamically modulating the saliency of a visual image to guide attention along the fixation path of a domain expert who had previously viewed the same image. In Experiment 1, we trained naive subjects via attentional highlighting on a fingerprint-matching task. Before and after training, we asked subjects to freely inspect images containing pairs of prints and determine whether the prints matched. Fixation sequences were automatically scored for the degree of expertise exhibited using a Bayesian discriminative model of novice and expert gaze behavior. Highlighted training causes gaze behavior to become more expert-like not only on the trained images but also on transfer images, indicating generalization of learning. In Experiment 2, to control for the possibility that the increase in expertise is due to mere exposure, we trained subjects via highlighting of fixation sequences from novices, not experts, and observed no transition toward expertise. In Experiment 3, to determine the specificity of the training effect, we trained subjects with expert fixation sequences from images other than the one being viewed, which preserves coarse-scale statistics of expert gaze but provides no information about fine-grain features. Observing at least a partial transition toward expertise, we obtain only weak evidence that the highlighting procedure facilitates the learning of critical local features. We discuss possible improvements to the highlighting procedure.

Bayesian Parameter Inference and Model Selection by Population Annealing in Systems Biology

PubMed Central

Murakami, Yohei

2014-01-01

Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population annealing. Although population annealing is usually used in statistical mechanics, we showed that population annealing can be used to compute Bayesian posterior distributions in the approximate Bayesian computation framework. To deal with un-identifiability of the representative values of parameters, we proposed to run the simulations with the parameter ensemble sampled from the posterior distribution, named “posterior parameter ensemble”. We showed that population annealing is an efficient and convenient algorithm to generate posterior parameter ensemble. We also showed that the simulations with the posterior parameter ensemble can, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference. Lastly, we introduced the marginal likelihood in the approximate Bayesian computation framework for Bayesian model selection. We showed that population annealing enables us to compute the marginal likelihood in the approximate Bayesian computation framework and conduct model selection depending on the Bayes factor. PMID:25089832

Quantification of biophysical adaptation benefits from Climate-Smart Agriculture using a Bayesian Belief Network.

PubMed

de Nijs, Patrick J; Berry, Nicholas J; Wells, Geoff J; Reay, Dave S

2014-10-20

The need for smallholder farmers to adapt their practices to a changing climate is well recognised, particularly in Africa. The cost of adapting to climate change in Africa is estimated to be $20 to $30 billion per year, but the total amount pledged to finance adaptation falls significantly short of this requirement. The difficulty of assessing and monitoring when adaptation is achieved is one of the key barriers to the disbursement of performance-based adaptation finance. To demonstrate the potential of Bayesian Belief Networks for describing the impacts of specific activities on climate change resilience, we developed a simple model that incorporates climate projections, local environmental data, information from peer-reviewed literature and expert opinion to account for the adaptation benefits derived from Climate-Smart Agriculture activities in Malawi. This novel approach allows assessment of vulnerability to climate change under different land use activities and can be used to identify appropriate adaptation strategies and to quantify biophysical adaptation benefits from activities that are implemented. We suggest that multiple-indicator Bayesian Belief Network approaches can provide insights into adaptation planning for a wide range of applications and, if further explored, could be part of a set of important catalysts for the expansion of adaptation finance.

Quantification of biophysical adaptation benefits from Climate-Smart Agriculture using a Bayesian Belief Network

NASA Astrophysics Data System (ADS)

de Nijs, Patrick J.; Berry, Nicholas J.; Wells, Geoff J.; Reay, Dave S.

2014-10-01

The need for smallholder farmers to adapt their practices to a changing climate is well recognised, particularly in Africa. The cost of adapting to climate change in Africa is estimated to be $20 to $30 billion per year, but the total amount pledged to finance adaptation falls significantly short of this requirement. The difficulty of assessing and monitoring when adaptation is achieved is one of the key barriers to the disbursement of performance-based adaptation finance. To demonstrate the potential of Bayesian Belief Networks for describing the impacts of specific activities on climate change resilience, we developed a simple model that incorporates climate projections, local environmental data, information from peer-reviewed literature and expert opinion to account for the adaptation benefits derived from Climate-Smart Agriculture activities in Malawi. This novel approach allows assessment of vulnerability to climate change under different land use activities and can be used to identify appropriate adaptation strategies and to quantify biophysical adaptation benefits from activities that are implemented. We suggest that multiple-indicator Bayesian Belief Network approaches can provide insights into adaptation planning for a wide range of applications and, if further explored, could be part of a set of important catalysts for the expansion of adaptation finance.

Quantum Bayesian perspective for intelligence reservoir characterization, monitoring and management.

PubMed

Lozada Aguilar, Miguel Ángel; Khrennikov, Andrei; Oleschko, Klaudia; de Jesús Correa, María

2017-11-13

The paper starts with a brief review of the literature about uncertainty in geological, geophysical and petrophysical data. In particular, we present the viewpoints of experts in geophysics on the application of Bayesian inference and subjective probability. Then we present arguments that the use of classical probability theory (CP) does not match completely the structure of geophysical data. We emphasize that such data are characterized by contextuality and non-Kolmogorovness (the impossibility to use the CP model), incompleteness as well as incompatibility of some geophysical measurements. These characteristics of geophysical data are similar to the characteristics of quantum physical data. Notwithstanding all this, contextuality can be seen as a major deviation of quantum theory from classical physics. In particular, the contextual probability viewpoint is the essence of the Växjö interpretation of quantum mechanics. We propose to use quantum probability (QP) for decision-making during the characterization, modelling, exploring and management of the intelligent hydrocarbon reservoir Quantum Bayesianism (QBism), one of the recently developed information interpretations of quantum theory, can be used as the interpretational basis for such QP decision-making in geology, geophysics and petroleum projects design and management.This article is part of the themed issue 'Second quantum revolution: foundational questions'. © 2017 The Author(s).

Tree Classification Software

NASA Technical Reports Server (NTRS)

Buntine, Wray

1993-01-01

This paper introduces the IND Tree Package to prospective users. IND does supervised learning using classification trees. This learning task is a basic tool used in the development of diagnosis, monitoring and expert systems. The IND Tree Package was developed as part of a NASA project to semi-automate the development of data analysis and modelling algorithms using artificial intelligence techniques. The IND Tree Package integrates features from CART and C4 with newer Bayesian and minimum encoding methods for growing classification trees and graphs. The IND Tree Package also provides an experimental control suite on top. The newer features give improved probability estimates often required in diagnostic and screening tasks. The package comes with a manual, Unix 'man' entries, and a guide to tree methods and research. The IND Tree Package is implemented in C under Unix and was beta-tested at university and commercial research laboratories in the United States.

Bayesian performance metrics and small system integration in recent homeland security and defense applications

NASA Astrophysics Data System (ADS)

Jannson, Tomasz; Kostrzewski, Andrew; Patton, Edward; Pradhan, Ranjit; Shih, Min-Yi; Walter, Kevin; Savant, Gajendra; Shie, Rick; Forrester, Thomas

2010-04-01

In this paper, Bayesian inference is applied to performance metrics definition of the important class of recent Homeland Security and defense systems called binary sensors, including both (internal) system performance and (external) CONOPS. The medical analogy is used to define the PPV (Positive Predictive Value), the basic Bayesian metrics parameter of the binary sensors. Also, Small System Integration (SSI) is discussed in the context of recent Homeland Security and defense applications, emphasizing a highly multi-technological approach, within the broad range of clusters ("nexus") of electronics, optics, X-ray physics, γ-ray physics, and other disciplines.

A nanomaterial release model for waste shredding using a Bayesian belief network

NASA Astrophysics Data System (ADS)

Shandilya, Neeraj; Ligthart, Tom; van Voorde, Imelda; Stahlmecke, Burkhard; Clavaguera, Simon; Philippot, Cecile; Ding, Yaobo; Goede, Henk

2018-02-01

The shredding of waste of electrical and electronic equipment (WEEE) and other products, incorporated with nanomaterials, can lead to a substantial release of nanomaterials. Considering the uncertainty, complexity, and scarcity of experimental data on release, we present the development of a Bayesian belief network (BBN) model. This baseline model aims to give a first prediction of the release of nanomaterials (excluding nanofibers) during their mechanical shredding. With a focus on the description of the model development methodology, we characterize nanomaterial release in terms of number, size, mass, and composition of released particles. Through a sensitivity analysis of the model, we find the material-specific parameters like affinity of nanomaterials to the matrix of the composite and their state of dispersion inside the matrix to reduce the nanomaterial release up to 50%. The shredder-specific parameters like number of shafts in a shredder and input and output size of the material for shredding could minimize it up to 98%. The comparison with two experimental test cases shows promising outcome on the prediction capacity of the model. As additional experimental data on nanomaterial release becomes available, the model is able to further adapt and update risk forecasts. When adapting the model with additional expert beliefs, experts should be selected using criteria, e.g., substantial contribution to nanomaterial and/or particulate matter release-related scientific literature, the capacity and willingness to contribute to further development of the BBN model, and openness to accepting deviating opinions. [Figure not available: see fulltext.

A Bayesian spawning habitat suitability model for American shad in southeastern United States rivers

USGS Publications Warehouse

Hightower, Joseph E.; Harris, Julianne E.; Raabe, Joshua K.; Brownell, Prescott; Drew, C. Ashton

2012-01-01

Habitat suitability index models for American shad Alosa sapidissima were developed by Stier and Crance in 1985. These models, which were based on a combination of published information and expert opinion, are often used to make decisions about hydropower dam operations and fish passage. The purpose of this study was to develop updated habitat suitability index models for spawning American shad in the southeastern United States, building on the many field and laboratory studies completed since 1985. We surveyed biologists who had knowledge about American shad spawning grounds, assembled a panel of experts to discuss important habitat variables, and used raw data from published and unpublished studies to develop new habitat suitability curves. The updated curves are based on resource selection functions, which can model habitat selectivity based on use and availability of particular habitats. Using field data collected in eight rivers from Virginia to Florida (Mattaponi, Pamunkey, Roanoke, Tar, Neuse, Cape Fear, Pee Dee, St. Johns), we obtained new curves for temperature, current velocity, and depth that were generally similar to the original models. Our new suitability function for substrate was also similar to the original pattern, except that sand (optimal in the original model) has a very low estimated suitability. The Bayesian approach that we used to develop habitat suitability curves provides an objective framework for updating the model as new studies are completed and for testing the model's applicability in other parts of the species' range.

The Integral Theory System Questionnaire: an anatomically directed questionnaire to determine pelvic floor dysfunctions in women.

PubMed

Wagenlehner, Florian Martin Erich; Fröhlich, Oliver; Bschleipfer, Thomas; Weidner, Wolfgang; Perletti, Gianpaolo

2014-06-01

Anatomical damage to pelvic floor structures may cause multiple symptoms. The Integral Theory System Questionnaire (ITSQ) is a holistic questionnaire that uses symptoms to help locate damage in specific connective tissue structures as a guide to reconstructive surgery. It is based on the integral theory, which states that pelvic floor symptoms and prolapse are both caused by lax suspensory ligaments. The aim of the present study was to psychometrically validate the ITSQ. Established psychometric properties including validity, reliability, and responsiveness were considered for evaluation. Criterion validity was assessed in a cohort of 110 women with pelvic floor dysfunctions by analyzing the correlation of questionnaire responses with objective clinical data. Test-retest was performed with questionnaires from 47 patients. Cronbach's alpha and "split-half" reliability coefficients were calculated for inner consistency analysis. Psychometric properties of ITSQ were comparable to the ones of previously validated Pelvic Floor Questionnaires. Face validity and content validity were approved by an expert group of the International Collaboration of Pelvic Floor surgeons. Convergent validity assessed using Bayesian method was at least as accurate as the expert assessment of anatomical defects. Objective data measurement in patients demonstrated significant correlations with ITSQ domains fulfilling criterion validity. Internal consistency values ranked from 0.85 to 0.89 in different scenarios. The ITSQ proofed accurate and is able to serve as a holistic Pelvic Floor Questionnaire directing symptoms to site-specific pelvic floor reconstructive surgery.

A Measure of Systems Engineering Effectiveness in Government Acquisition of Complex Information Systems: A Bayesian Belief Network-Based Approach

ERIC Educational Resources Information Center

Doskey, Steven Craig

2014-01-01

This research presents an innovative means of gauging Systems Engineering effectiveness through a Systems Engineering Relative Effectiveness Index (SE REI) model. The SE REI model uses a Bayesian Belief Network to map causal relationships in government acquisitions of Complex Information Systems (CIS), enabling practitioners to identify and…

A Bayesian belief network approach for assessing uncertainty in conceptual site models at contaminated sites

NASA Astrophysics Data System (ADS)

Thomsen, Nanna I.; Binning, Philip J.; McKnight, Ursula S.; Tuxen, Nina; Bjerg, Poul L.; Troldborg, Mads

2016-05-01

A key component in risk assessment of contaminated sites is in the formulation of a conceptual site model (CSM). A CSM is a simplified representation of reality and forms the basis for the mathematical modeling of contaminant fate and transport at the site. The CSM should therefore identify the most important site-specific features and processes that may affect the contaminant transport behavior at the site. However, the development of a CSM will always be associated with uncertainties due to limited data and lack of understanding of the site conditions. CSM uncertainty is often found to be a major source of model error and it should therefore be accounted for when evaluating uncertainties in risk assessments. We present a Bayesian belief network (BBN) approach for constructing CSMs and assessing their uncertainty at contaminated sites. BBNs are graphical probabilistic models that are effective for integrating quantitative and qualitative information, and thus can strengthen decisions when empirical data are lacking. The proposed BBN approach facilitates a systematic construction of multiple CSMs, and then determines the belief in each CSM using a variety of data types and/or expert opinion at different knowledge levels. The developed BBNs combine data from desktop studies and initial site investigations with expert opinion to assess which of the CSMs are more likely to reflect the actual site conditions. The method is demonstrated on a Danish field site, contaminated with chlorinated ethenes. Four different CSMs are developed by combining two contaminant source zone interpretations (presence or absence of a separate phase contamination) and two geological interpretations (fractured or unfractured clay till). The beliefs in each of the CSMs are assessed sequentially based on data from three investigation stages (a screening investigation, a more detailed investigation, and an expert consultation) to demonstrate that the belief can be updated as more information becomes available.

Estimating virus occurrence using Bayesian modeling in multiple drinking water systems of the United States

USGS Publications Warehouse

Varughese, Eunice A.; Brinkman, Nichole E; Anneken, Emily M; Cashdollar, Jennifer S; Fout, G. Shay; Furlong, Edward T.; Kolpin, Dana W.; Glassmeyer, Susan T.; Keely, Scott P

2017-01-01

incorporated into a Bayesian model to more accurately determine viral load in both source and treated water. Results of the Bayesian model indicated that viruses are present in source water and treated water. By using a Bayesian framework that incorporates inhibition, as well as many other parameters that affect viral detection, this study offers an approach for more accurately estimating the occurrence of viral pathogens in environmental waters.

A Bayesian Hybrid Adaptive Randomisation Design for Clinical Trials with Survival Outcomes.

PubMed

Moatti, M; Chevret, S; Zohar, S; Rosenberger, W F

2016-01-01

Response-adaptive randomisation designs have been proposed to improve the efficiency of phase III randomised clinical trials and improve the outcomes of the clinical trial population. In the setting of failure time outcomes, Zhang and Rosenberger (2007) developed a response-adaptive randomisation approach that targets an optimal allocation, based on a fixed sample size. The aim of this research is to propose a response-adaptive randomisation procedure for survival trials with an interim monitoring plan, based on the following optimal criterion: for fixed variance of the estimated log hazard ratio, what allocation minimizes the expected hazard of failure? We demonstrate the utility of the design by redesigning a clinical trial on multiple myeloma. To handle continuous monitoring of data, we propose a Bayesian response-adaptive randomisation procedure, where the log hazard ratio is the effect measure of interest. Combining the prior with the normal likelihood, the mean posterior estimate of the log hazard ratio allows derivation of the optimal target allocation. We perform a simulation study to assess and compare the performance of this proposed Bayesian hybrid adaptive design to those of fixed, sequential or adaptive - either frequentist or fully Bayesian - designs. Non informative normal priors of the log hazard ratio were used, as well as mixture of enthusiastic and skeptical priors. Stopping rules based on the posterior distribution of the log hazard ratio were computed. The method is then illustrated by redesigning a phase III randomised clinical trial of chemotherapy in patients with multiple myeloma, with mixture of normal priors elicited from experts. As expected, there was a reduction in the proportion of observed deaths in the adaptive vs. non-adaptive designs; this reduction was maximized using a Bayes mixture prior, with no clear-cut improvement by using a fully Bayesian procedure. The use of stopping rules allows a slight decrease in the observed proportion of deaths under the alternate hypothesis compared with the adaptive designs with no stopping rules. Such Bayesian hybrid adaptive survival trials may be promising alternatives to traditional designs, reducing the duration of survival trials, as well as optimizing the ethical concerns for patients enrolled in the trial.

Using Bayesian Networks to Improve Knowledge Assessment

ERIC Educational Resources Information Center

Millan, Eva; Descalco, Luis; Castillo, Gladys; Oliveira, Paula; Diogo, Sandra

2013-01-01

In this paper, we describe the integration and evaluation of an existing generic Bayesian student model (GBSM) into an existing computerized testing system within the Mathematics Education Project (PmatE--Projecto Matematica Ensino) of the University of Aveiro. This generic Bayesian student model had been previously evaluated with simulated…

Using Bayesian belief networks in adaptive management.

Treesearch

J.B. Nyberg; B.G. Marcot; R. Sulyma

2006-01-01

Bayesian belief and decision networks are relatively new modeling methods that are especially well suited to adaptive-management applications, but they appear not to have been widely used in adaptive management to date. Bayesian belief networks (BBNs) can serve many purposes for practioners of adaptive management, from illustrating system relations conceptually to...

A study of anthropogenic and climatic disturbance of the New River Estuary using a Bayesian belief network.

PubMed

Nojavan A, Farnaz; Qian, Song S; Paerl, Hans W; Reckhow, Kenneth H; Albright, Elizabeth A

2014-06-15

The present paper utilizes a Bayesian Belief Network (BBN) approach to intuitively present and quantify our current understanding of the complex physical, chemical, and biological processes that lead to eutrophication in an estuarine ecosystem (New River Estuary, North Carolina, USA). The model is further used to explore the effects of plausible future climatic and nutrient pollution management scenarios on water quality indicators. The BBN, through visualizing the structure of the network, facilitates knowledge communication with managers/stakeholders who might not be experts in the underlying scientific disciplines. Moreover, the developed structure of the BBN is transferable to other comparable estuaries. The BBN nodes are discretized exploring a new approach called moment matching method. The conditional probability tables of the variables are driven by a large dataset (four years). Our results show interaction among various predictors and their impact on water quality indicators. The synergistic effects caution future management actions. Copyright © 2014 Elsevier Ltd. All rights reserved.

BEASTling: A software tool for linguistic phylogenetics using BEAST 2

PubMed Central

Forkel, Robert; Kaiping, Gereon A.; Atkinson, Quentin D.

2017-01-01

We present a new open source software tool called BEASTling, designed to simplify the preparation of Bayesian phylogenetic analyses of linguistic data using the BEAST 2 platform. BEASTling transforms comparatively short and human-readable configuration files into the XML files used by BEAST to specify analyses. By taking advantage of Creative Commons-licensed data from the Glottolog language catalog, BEASTling allows the user to conveniently filter datasets using names for recognised language families, to impose monophyly constraints so that inferred language trees are backward compatible with Glottolog classifications, or to assign geographic location data to languages for phylogeographic analyses. Support for the emerging cross-linguistic linked data format (CLDF) permits easy incorporation of data published in cross-linguistic linked databases into analyses. BEASTling is intended to make the power of Bayesian analysis more accessible to historical linguists without strong programming backgrounds, in the hopes of encouraging communication and collaboration between those developing computational models of language evolution (who are typically not linguists) and relevant domain experts. PMID:28796784

BEASTling: A software tool for linguistic phylogenetics using BEAST 2.

PubMed

Maurits, Luke; Forkel, Robert; Kaiping, Gereon A; Atkinson, Quentin D

2017-01-01

We present a new open source software tool called BEASTling, designed to simplify the preparation of Bayesian phylogenetic analyses of linguistic data using the BEAST 2 platform. BEASTling transforms comparatively short and human-readable configuration files into the XML files used by BEAST to specify analyses. By taking advantage of Creative Commons-licensed data from the Glottolog language catalog, BEASTling allows the user to conveniently filter datasets using names for recognised language families, to impose monophyly constraints so that inferred language trees are backward compatible with Glottolog classifications, or to assign geographic location data to languages for phylogeographic analyses. Support for the emerging cross-linguistic linked data format (CLDF) permits easy incorporation of data published in cross-linguistic linked databases into analyses. BEASTling is intended to make the power of Bayesian analysis more accessible to historical linguists without strong programming backgrounds, in the hopes of encouraging communication and collaboration between those developing computational models of language evolution (who are typically not linguists) and relevant domain experts.

Computational Prediction and Validation of an Expert's Evaluation of Chemical Probes

PubMed Central

Litterman, Nadia K.; Lipinski, Christopher A.; Bunin, Barry A.; Ekins, Sean

2016-01-01

In a decade with over half a billion dollars of investment, more than 300 chemical probes have been identified to have biological activity through NIH funded screening efforts. We have collected the evaluations of an experienced medicinal chemist on the likely chemistry quality of these probes based on a number of criteria including literature related to the probe and potential chemical reactivity. Over 20% of these probes were found to be undesirable. Analysis of the molecular properties of these compounds scored as desirable suggested higher pKa, molecular weight, heavy atom count and rotatable bond number. We were particularly interested whether the human evaluation aspect of medicinal chemistry due diligence could be computationally predicted. We used a process of sequential Bayesian model building and iterative testing as we included additional probes. Following external validation of these methods and comparing different machine learning methods we identified Bayesian models with accuracy comparable to other measures of drug-likeness and filtering rules created to date. PMID:25244007

Quantitative analysis of factors that affect oil pipeline network accident based on Bayesian networks: A case study in China

NASA Astrophysics Data System (ADS)

Zhang, Chao; Qin, Ting Xin; Huang, Shuai; Wu, Jian Song; Meng, Xin Yan

2018-06-01

Some factors can affect the consequences of oil pipeline accident and their effects should be analyzed to improve emergency preparation and emergency response. Although there are some qualitative analysis models of risk factors' effects, the quantitative analysis model still should be researched. In this study, we introduce a Bayesian network (BN) model of risk factors' effects analysis in an oil pipeline accident case that happened in China. The incident evolution diagram is built to identify the risk factors. And the BN model is built based on the deployment rule for factor nodes in BN and the expert knowledge by Dempster-Shafer evidence theory. Then the probabilities of incident consequences and risk factors' effects can be calculated. The most likely consequences given by this model are consilient with the case. Meanwhile, the quantitative estimations of risk factors' effects may provide a theoretical basis to take optimal risk treatment measures for oil pipeline management, which can be used in emergency preparation and emergency response.

Bayesian Estimation of Pneumonia Etiology: Epidemiologic Considerations and Applications to the Pneumonia Etiology Research for Child Health Study

PubMed Central

Fu, Wei; Shi, Qiyuan; Prosperi, Christine; Wu, Zhenke; Hammitt, Laura L.; Feikin, Daniel R.; Baggett, Henry C.; Howie, Stephen R.C.; Scott, J. Anthony G.; Murdoch, David R.; Madhi, Shabir A.; Thea, Donald M.; Brooks, W. Abdullah; Kotloff, Karen L.; Li, Mengying; Park, Daniel E.; Lin, Wenyi; Levine, Orin S.; O’Brien, Katherine L.; Zeger, Scott L.

2017-01-01

Abstract In pneumonia, specimens are rarely obtained directly from the infection site, the lung, so the pathogen causing infection is determined indirectly from multiple tests on peripheral clinical specimens, which may have imperfect and uncertain sensitivity and specificity, so inference about the cause is complex. Analytic approaches have included expert review of case-only results, case–control logistic regression, latent class analysis, and attributable fraction, but each has serious limitations and none naturally integrate multiple test results. The Pneumonia Etiology Research for Child Health (PERCH) study required an analytic solution appropriate for a case–control design that could incorporate evidence from multiple specimens from cases and controls and that accounted for measurement error. We describe a Bayesian integrated approach we developed that combined and extended elements of attributable fraction and latent class analyses to meet some of these challenges and illustrate the advantage it confers regarding the challenges identified for other methods. PMID:28575370

Bayesian flood forecasting methods: A review

NASA Astrophysics Data System (ADS)

Han, Shasha; Coulibaly, Paulin

2017-08-01

Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been developed and widely applied, but there is still room for improvements. Future research in the context of Bayesian flood forecasting should be on assimilation of various sources of newly available information and improvement of predictive performance assessment methods.

Risk Assessment for Mobile Systems Through a Multilayered Hierarchical Bayesian Network.

PubMed

Li, Shancang; Tryfonas, Theo; Russell, Gordon; Andriotis, Panagiotis

2016-08-01

Mobile systems are facing a number of application vulnerabilities that can be combined together and utilized to penetrate systems with devastating impact. When assessing the overall security of a mobile system, it is important to assess the security risks posed by each mobile applications (apps), thus gaining a stronger understanding of any vulnerabilities present. This paper aims at developing a three-layer framework that assesses the potential risks which apps introduce within the Android mobile systems. A Bayesian risk graphical model is proposed to evaluate risk propagation in a layered risk architecture. By integrating static analysis, dynamic analysis, and behavior analysis in a hierarchical framework, the risks and their propagation through each layer are well modeled by the Bayesian risk graph, which can quantitatively analyze risks faced to both apps and mobile systems. The proposed hierarchical Bayesian risk graph model offers a novel way to investigate the security risks in mobile environment and enables users and administrators to evaluate the potential risks. This strategy allows to strengthen both app security as well as the security of the entire system.

Application and Evaluation of an Expert Judgment Elicitation Procedure for Correlations.

PubMed

Zondervan-Zwijnenburg, Mariëlle; van de Schoot-Hubeek, Wenneke; Lek, Kimberley; Hoijtink, Herbert; van de Schoot, Rens

2017-01-01

The purpose of the current study was to apply and evaluate a procedure to elicit expert judgments about correlations, and to update this information with empirical data. The result is a face-to-face group elicitation procedure with as its central element a trial roulette question that elicits experts' judgments expressed as distributions. During the elicitation procedure, a concordance probability question was used to provide feedback to the experts on their judgments. We evaluated the elicitation procedure in terms of validity and reliability by means of an application with a small sample of experts. Validity means that the elicited distributions accurately represent the experts' judgments. Reliability concerns the consistency of the elicited judgments over time. Four behavioral scientists provided their judgments with respect to the correlation between cognitive potential and academic performance for two separate populations enrolled at a specific school in the Netherlands that provides special education to youth with severe behavioral problems: youth with autism spectrum disorder (ASD), and youth with diagnoses other than ASD. Measures of face-validity, feasibility, convergent validity, coherence, and intra-rater reliability showed promising results. Furthermore, the current study illustrates the use of the elicitation procedure and elicited distributions in a social science application. The elicited distributions were used as a prior for the correlation, and updated with data for both populations collected at the school of interest. The current study shows that the newly developed elicitation procedure combining the trial roulette method with the elicitation of correlations is a promising tool, and that the results of the procedure are useful as prior information in a Bayesian analysis.

Competing risk models in reliability systems, a weibull distribution model with bayesian analysis approach

NASA Astrophysics Data System (ADS)

Iskandar, Ismed; Satria Gondokaryono, Yudi

2016-02-01

In reliability theory, the most important problem is to determine the reliability of a complex system from the reliability of its components. The weakness of most reliability theories is that the systems are described and explained as simply functioning or failed. In many real situations, the failures may be from many causes depending upon the age and the environment of the system and its components. Another problem in reliability theory is one of estimating the parameters of the assumed failure models. The estimation may be based on data collected over censored or uncensored life tests. In many reliability problems, the failure data are simply quantitatively inadequate, especially in engineering design and maintenance system. The Bayesian analyses are more beneficial than the classical one in such cases. The Bayesian estimation analyses allow us to combine past knowledge or experience in the form of an apriori distribution with life test data to make inferences of the parameter of interest. In this paper, we have investigated the application of the Bayesian estimation analyses to competing risk systems. The cases are limited to the models with independent causes of failure by using the Weibull distribution as our model. A simulation is conducted for this distribution with the objectives of verifying the models and the estimators and investigating the performance of the estimators for varying sample size. The simulation data are analyzed by using Bayesian and the maximum likelihood analyses. The simulation results show that the change of the true of parameter relatively to another will change the value of standard deviation in an opposite direction. For a perfect information on the prior distribution, the estimation methods of the Bayesian analyses are better than those of the maximum likelihood. The sensitivity analyses show some amount of sensitivity over the shifts of the prior locations. They also show the robustness of the Bayesian analysis within the range between the true value and the maximum likelihood estimated value lines.

Statistical Modeling for Radiation Hardness Assurance

NASA Technical Reports Server (NTRS)

Ladbury, Raymond L.

2014-01-01

We cover the models and statistics associated with single event effects (and total ionizing dose), why we need them, and how to use them: What models are used, what errors exist in real test data, and what the model allows us to say about the DUT will be discussed. In addition, how to use other sources of data such as historical, heritage, and similar part and how to apply experience, physics, and expert opinion to the analysis will be covered. Also included will be concepts of Bayesian statistics, data fitting, and bounding rates.

Implementation of an Adaptive Learning System Using a Bayesian Network

ERIC Educational Resources Information Center

Yasuda, Keiji; Kawashima, Hiroyuki; Hata, Yoko; Kimura, Hiroaki

2015-01-01

An adaptive learning system is proposed that incorporates a Bayesian network to efficiently gauge learners' understanding at the course-unit level. Also, learners receive content that is adapted to their measured level of understanding. The system works on an iPad via the Edmodo platform. A field experiment using the system in an elementary school…

Fire risk in San Diego County, California: A weighted Bayesian model approach

USGS Publications Warehouse

Kolden, Crystal A.; Weigel, Timothy J.

2007-01-01

Fire risk models are widely utilized to mitigate wildfire hazards, but models are often based on expert opinions of less understood fire-ignition and spread processes. In this study, we used an empirically derived weights-of-evidence model to assess what factors produce fire ignitions east of San Diego, California. We created and validated a dynamic model of fire-ignition risk based on land characteristics and existing fire-ignition history data, and predicted ignition risk for a future urbanization scenario. We then combined our empirical ignition-risk model with a fuzzy fire behavior-risk model developed by wildfire experts to create a hybrid model of overall fire risk. We found that roads influence fire ignitions and that future growth will increase risk in new rural development areas. We conclude that empirically derived risk models and hybrid models offer an alternative method to assess current and future fire risk based on management actions.

A study of the transferability of influenza case detection systems between two large healthcare systems

PubMed Central

Wagner, Michael M.; Cooper, Gregory F.; Ferraro, Jeffrey P.; Su, Howard; Gesteland, Per H.; Haug, Peter J.; Millett, Nicholas E.; Aronis, John M.; Nowalk, Andrew J.; Ruiz, Victor M.; López Pineda, Arturo; Shi, Lingyun; Van Bree, Rudy; Ginter, Thomas; Tsui, Fuchiang

2017-01-01

Objectives This study evaluates the accuracy and transferability of Bayesian case detection systems (BCD) that use clinical notes from emergency department (ED) to detect influenza cases. Methods A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are fed into a Bayesain network classifier (BN) to infer patients’ diagnoses. We developed BCDs at the University of Pittsburgh Medical Center (BCDUPMC) and Intermountain Healthcare in Utah (BCDIH). At each site, we manually built a rule-based NLP and trained a Bayesain network classifier from over 40,000 ED encounters between Jan. 2008 and May. 2010 using feature selection, machine learning, and expert debiasing approach. Transferability of a BCD in this study may be impacted by seven factors: development (source) institution, development parser, application (target) institution, application parser, NLP transfer, BN transfer, and classification task. We employed an ANOVA analysis to study their impacts on BCD performance. Results Both BCDs discriminated well between influenza and non-influenza on local test cases (AUCs > 0.92). When tested for transferability using the other institution’s cases, BCDUPMC discriminations declined minimally (AUC decreased from 0.95 to 0.94, p<0.01), and BCDIH discriminations declined more (from 0.93 to 0.87, p<0.0001). We attributed the BCDIH decline to the lower recall of the IH parser on UPMC notes. The ANOVA analysis showed five significant factors: development parser, application institution, application parser, BN transfer, and classification task. Conclusion We demonstrated high influenza case detection performance in two large healthcare systems in two geographically separated regions, providing evidentiary support for the use of automated case detection from routinely collected electronic clinical notes in national influenza surveillance. The transferability could be improved by training Bayesian network classifier locally and increasing the accuracy of the NLP parser. PMID:28380048

A study of the transferability of influenza case detection systems between two large healthcare systems.

PubMed

Ye, Ye; Wagner, Michael M; Cooper, Gregory F; Ferraro, Jeffrey P; Su, Howard; Gesteland, Per H; Haug, Peter J; Millett, Nicholas E; Aronis, John M; Nowalk, Andrew J; Ruiz, Victor M; López Pineda, Arturo; Shi, Lingyun; Van Bree, Rudy; Ginter, Thomas; Tsui, Fuchiang

2017-01-01

This study evaluates the accuracy and transferability of Bayesian case detection systems (BCD) that use clinical notes from emergency department (ED) to detect influenza cases. A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are fed into a Bayesain network classifier (BN) to infer patients' diagnoses. We developed BCDs at the University of Pittsburgh Medical Center (BCDUPMC) and Intermountain Healthcare in Utah (BCDIH). At each site, we manually built a rule-based NLP and trained a Bayesain network classifier from over 40,000 ED encounters between Jan. 2008 and May. 2010 using feature selection, machine learning, and expert debiasing approach. Transferability of a BCD in this study may be impacted by seven factors: development (source) institution, development parser, application (target) institution, application parser, NLP transfer, BN transfer, and classification task. We employed an ANOVA analysis to study their impacts on BCD performance. Both BCDs discriminated well between influenza and non-influenza on local test cases (AUCs > 0.92). When tested for transferability using the other institution's cases, BCDUPMC discriminations declined minimally (AUC decreased from 0.95 to 0.94, p<0.01), and BCDIH discriminations declined more (from 0.93 to 0.87, p<0.0001). We attributed the BCDIH decline to the lower recall of the IH parser on UPMC notes. The ANOVA analysis showed five significant factors: development parser, application institution, application parser, BN transfer, and classification task. We demonstrated high influenza case detection performance in two large healthcare systems in two geographically separated regions, providing evidentiary support for the use of automated case detection from routinely collected electronic clinical notes in national influenza surveillance. The transferability could be improved by training Bayesian network classifier locally and increasing the accuracy of the NLP parser.

Prediction of Individual Serum Infliximab Concentrations in Inflammatory Bowel Disease by a Bayesian Dashboard System.

PubMed

Eser, Alexander; Primas, Christian; Reinisch, Sieglinde; Vogelsang, Harald; Novacek, Gottfried; Mould, Diane R; Reinisch, Walter

2018-01-30

Despite a robust exposure-response relationship of infliximab in inflammatory bowel disease (IBD), attempts to adjust dosing to individually predicted serum concentrations of infliximab (SICs) are lacking. Compared with labor-intensive conventional software for pharmacokinetic (PK) modeling (eg, NONMEM) dashboards are easy-to-use programs incorporating complex Bayesian statistics to determine individual pharmacokinetics. We evaluated various infliximab detection assays and the number of samples needed to precisely forecast individual SICs using a Bayesian dashboard. We assessed long-term infliximab retention in patients being dosed concordantly versus discordantly with Bayesian dashboard recommendations. Three hundred eighty-two serum samples from 117 adult IBD patients on infliximab maintenance therapy were analyzed by 3 commercially available assays. Data from each assay was modeled using NONMEM and a Bayesian dashboard. PK parameter precision and residual variability were assessed. Forecast concentrations from both systems were compared with observed concentrations. Infliximab retention was assessed by prediction for dose intensification via Bayesian dashboard versus real-life practice. Forecast precision of SICs varied between detection assays. At least 3 SICs from a reliable assay are needed for an accurate forecast. The Bayesian dashboard performed similarly to NONMEM to predict SICs. Patients dosed concordantly with Bayesian dashboard recommendations had a significantly longer median drug survival than those dosed discordantly (51.5 versus 4.6 months, P < .0001). The Bayesian dashboard helps to assess the diagnostic performance of infliximab detection assays. Three, not single, SICs provide sufficient information for individualized dose adjustment when incorporated into the Bayesian dashboard. Treatment adjusted to forecasted SICs is associated with longer drug retention of infliximab. © 2018, The American College of Clinical Pharmacology.

Efficient Probabilistic Diagnostics for Electrical Power Systems

NASA Technical Reports Server (NTRS)

Mengshoel, Ole J.; Chavira, Mark; Cascio, Keith; Poll, Scott; Darwiche, Adnan; Uckun, Serdar

2008-01-01

We consider in this work the probabilistic approach to model-based diagnosis when applied to electrical power systems (EPSs). Our probabilistic approach is formally well-founded, as it based on Bayesian networks and arithmetic circuits. We investigate the diagnostic task known as fault isolation, and pay special attention to meeting two of the main challenges . model development and real-time reasoning . often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-to-use speci.cation language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In essence, we introduce a high-level EPS speci.cation language from which Bayesian networks that can diagnose multiple simultaneous failures are auto-generated, and we illustrate the feasibility of using arithmetic circuits, compiled from Bayesian networks, for real-time diagnosis on real-world EPSs of interest to NASA. The experimental system is a real-world EPS, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. In experiments with the ADAPT Bayesian network, which currently contains 503 discrete nodes and 579 edges, we .nd high diagnostic accuracy in scenarios where one to three faults, both in components and sensors, were inserted. The time taken to compute the most probable explanation using arithmetic circuits has a small mean of 0.2625 milliseconds and standard deviation of 0.2028 milliseconds. In experiments with data from ADAPT we also show that arithmetic circuit evaluation substantially outperforms joint tree propagation and variable elimination, two alternative algorithms for diagnosis using Bayesian network inference.

Bayesian data analysis in population ecology: motivations, methods, and benefits

USGS Publications Warehouse

Dorazio, Robert

2016-01-01

During the 20th century ecologists largely relied on the frequentist system of inference for the analysis of their data. However, in the past few decades ecologists have become increasingly interested in the use of Bayesian methods of data analysis. In this article I provide guidance to ecologists who would like to decide whether Bayesian methods can be used to improve their conclusions and predictions. I begin by providing a concise summary of Bayesian methods of analysis, including a comparison of differences between Bayesian and frequentist approaches to inference when using hierarchical models. Next I provide a list of problems where Bayesian methods of analysis may arguably be preferred over frequentist methods. These problems are usually encountered in analyses based on hierarchical models of data. I describe the essentials required for applying modern methods of Bayesian computation, and I use real-world examples to illustrate these methods. I conclude by summarizing what I perceive to be the main strengths and weaknesses of using Bayesian methods to solve ecological inference problems.

Final Report, DOE Early Career Award: Predictive modeling of complex physical systems: new tools for statistical inference, uncertainty quantification, and experimental design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marzouk, Youssef

Predictive simulation of complex physical systems increasingly rests on the interplay of experimental observations with computational models. Key inputs, parameters, or structural aspects of models may be incomplete or unknown, and must be developed from indirect and limited observations. At the same time, quantified uncertainties are needed to qualify computational predictions in the support of design and decision-making. In this context, Bayesian statistics provides a foundation for inference from noisy and limited data, but at prohibitive computional expense. This project intends to make rigorous predictive modeling *feasible* in complex physical systems, via accelerated and scalable tools for uncertainty quantification, Bayesianmore » inference, and experimental design. Specific objectives are as follows: 1. Develop adaptive posterior approximations and dimensionality reduction approaches for Bayesian inference in high-dimensional nonlinear systems. 2. Extend accelerated Bayesian methodologies to large-scale {\\em sequential} data assimilation, fully treating nonlinear models and non-Gaussian state and parameter distributions. 3. Devise efficient surrogate-based methods for Bayesian model selection and the learning of model structure. 4. Develop scalable simulation/optimization approaches to nonlinear Bayesian experimental design, for both parameter inference and model selection. 5. Demonstrate these inferential tools on chemical kinetic models in reacting flow, constructing and refining thermochemical and electrochemical models from limited data. Demonstrate Bayesian filtering on canonical stochastic PDEs and in the dynamic estimation of inhomogeneous subsurface properties and flow fields.« less

BN-FLEMOps pluvial - A probabilistic multi-variable loss estimation model for pluvial floods

NASA Astrophysics Data System (ADS)

Roezer, V.; Kreibich, H.; Schroeter, K.; Doss-Gollin, J.; Lall, U.; Merz, B.

2017-12-01

Pluvial flood events, such as in Copenhagen (Denmark) in 2011, Beijing (China) in 2012 or Houston (USA) in 2016, have caused severe losses to urban dwellings in recent years. These floods are caused by storm events with high rainfall rates well above the design levels of urban drainage systems, which lead to inundation of streets and buildings. A projected increase in frequency and intensity of heavy rainfall events in many areas and an ongoing urbanization may increase pluvial flood losses in the future. For an efficient risk assessment and adaptation to pluvial floods, a quantification of the flood risk is needed. Few loss models have been developed particularly for pluvial floods. These models usually use simple waterlevel- or rainfall-loss functions and come with very high uncertainties. To account for these uncertainties and improve the loss estimation, we present a probabilistic multi-variable loss estimation model for pluvial floods based on empirical data. The model was developed in a two-step process using a machine learning approach and a comprehensive database comprising 783 records of direct building and content damage of private households. The data was gathered through surveys after four different pluvial flood events in Germany between 2005 and 2014. In a first step, linear and non-linear machine learning algorithms, such as tree-based and penalized regression models were used to identify the most important loss influencing factors among a set of 55 candidate variables. These variables comprise hydrological and hydraulic aspects, early warning, precaution, building characteristics and the socio-economic status of the household. In a second step, the most important loss influencing variables were used to derive a probabilistic multi-variable pluvial flood loss estimation model based on Bayesian Networks. Two different networks were tested: a score-based network learned from the data and a network based on expert knowledge. Loss predictions are made through Bayesian inference using Markov chain Monte Carlo (MCMC) sampling. With the ability to cope with incomplete information and use expert knowledge, as well as inherently providing quantitative uncertainty information, it is shown that loss models based on BNs are superior to deterministic approaches for pluvial flood risk assessment.

True versus Apparent Malaria Infection Prevalence: The Contribution of a Bayesian Approach

PubMed Central

Claes, Filip; Van Hong, Nguyen; Torres, Kathy; Mao, Sokny; Van den Eede, Peter; Thi Thinh, Ta; Gamboa, Dioni; Sochantha, Tho; Thang, Ngo Duc; Coosemans, Marc; Büscher, Philippe; D'Alessandro, Umberto; Berkvens, Dirk; Erhart, Annette

2011-01-01

Aims To present a new approach for estimating the “true prevalence” of malaria and apply it to datasets from Peru, Vietnam, and Cambodia. Methods Bayesian models were developed for estimating both the malaria prevalence using different diagnostic tests (microscopy, PCR & ELISA), without the need of a gold standard, and the tests' characteristics. Several sources of information, i.e. data, expert opinions and other sources of knowledge can be integrated into the model. This approach resulting in an optimal and harmonized estimate of malaria infection prevalence, with no conflict between the different sources of information, was tested on data from Peru, Vietnam and Cambodia. Results Malaria sero-prevalence was relatively low in all sites, with ELISA showing the highest estimates. The sensitivity of microscopy and ELISA were statistically lower in Vietnam than in the other sites. Similarly, the specificities of microscopy, ELISA and PCR were significantly lower in Vietnam than in the other sites. In Vietnam and Peru, microscopy was closer to the “true” estimate than the other 2 tests while as expected ELISA, with its lower specificity, usually overestimated the prevalence. Conclusions Bayesian methods are useful for analyzing prevalence results when no gold standard diagnostic test is available. Though some results are expected, e.g. PCR more sensitive than microscopy, a standardized and context-independent quantification of the diagnostic tests' characteristics (sensitivity and specificity) and the underlying malaria prevalence may be useful for comparing different sites. Indeed, the use of a single diagnostic technique could strongly bias the prevalence estimation. This limitation can be circumvented by using a Bayesian framework taking into account the imperfect characteristics of the currently available diagnostic tests. As discussed in the paper, this approach may further support global malaria burden estimation initiatives. PMID:21364745

A brief history and technical review of the expert system research

NASA Astrophysics Data System (ADS)

Tan, Haocheng

2017-09-01

The expert system is a computer system that emulates the decision-making ability of a human expert, which aims to solve complex problems by reasoning knowledge. It is an important branch of artificial intelligence. In this paper, firstly, we briefly introduce the development and basic structure of the expert system. Then, from the perspective of the enabling technology, we classify the current expert systems and elaborate four expert systems: The Rule-Based Expert System, the Framework-Based Expert System, the Fuzzy Logic-Based Expert System and the Expert System Based on Neural Network.

ANUBIS: artificial neuromodulation using a Bayesian inference system.

PubMed

Smith, Benjamin J H; Saaj, Chakravarthini M; Allouis, Elie

2013-01-01

Gain tuning is a crucial part of controller design and depends not only on an accurate understanding of the system in question, but also on the designer's ability to predict what disturbances and other perturbations the system will encounter throughout its operation. This letter presents ANUBIS (artificial neuromodulation using a Bayesian inference system), a novel biologically inspired technique for automatically tuning controller parameters in real time. ANUBIS is based on the Bayesian brain concept and modifies it by incorporating a model of the neuromodulatory system comprising four artificial neuromodulators. It has been applied to the controller of EchinoBot, a prototype walking rover for Martian exploration. ANUBIS has been implemented at three levels of the controller; gait generation, foot trajectory planning using Bézier curves, and foot trajectory tracking using a terminal sliding mode controller. We compare the results to a similar system that has been tuned using a multilayer perceptron. The use of Bayesian inference means that the system retains mathematical interpretability, unlike other intelligent tuning techniques, which use neural networks, fuzzy logic, or evolutionary algorithms. The simulation results show that ANUBIS provides significant improvements in efficiency and adaptability of the three controller components; it allows the robot to react to obstacles and uncertainties faster than the system tuned with the MLP, while maintaining stability and accuracy. As well as advancing rover autonomy, ANUBIS could also be applied to other situations where operating conditions are likely to change or cannot be accurately modeled in advance, such as process control. In addition, it demonstrates one way in which neuromodulation could fit into the Bayesian brain framework.

Expert Systems: An Overview for Teacher-Librarians.

ERIC Educational Resources Information Center

Orwig, Gary; Barron, Ann

1992-01-01

Provides an overview of expert systems for teacher librarians. Highlights include artificial intelligence and expert systems; the development of the MYCIN medical expert system; rule-based expert systems; the use of expert system shells to develop a specific system; and how to select an appropriate application for an expert system. (11 references)…

Bayesian multimodel inference for dose-response studies

USGS Publications Warehouse

Link, W.A.; Albers, P.H.

2007-01-01

Statistical inference in dose?response studies is model-based: The analyst posits a mathematical model of the relation between exposure and response, estimates parameters of the model, and reports conclusions conditional on the model. Such analyses rarely include any accounting for the uncertainties associated with model selection. The Bayesian inferential system provides a convenient framework for model selection and multimodel inference. In this paper we briefly describe the Bayesian paradigm and Bayesian multimodel inference. We then present a family of models for multinomial dose?response data and apply Bayesian multimodel inferential methods to the analysis of data on the reproductive success of American kestrels (Falco sparveriuss) exposed to various sublethal dietary concentrations of methylmercury.

Decision generation tools and Bayesian inference

NASA Astrophysics Data System (ADS)

Jannson, Tomasz; Wang, Wenjian; Forrester, Thomas; Kostrzewski, Andrew; Veeris, Christian; Nielsen, Thomas

2014-05-01

Digital Decision Generation (DDG) tools are important software sub-systems of Command and Control (C2) systems and technologies. In this paper, we present a special type of DDGs based on Bayesian Inference, related to adverse (hostile) networks, including such important applications as terrorism-related networks and organized crime ones.

Ockham's razor and Bayesian analysis. [statistical theory for systems evaluation

NASA Technical Reports Server (NTRS)

Jefferys, William H.; Berger, James O.

1992-01-01

'Ockham's razor', the ad hoc principle enjoining the greatest possible simplicity in theoretical explanations, is presently shown to be justifiable as a consequence of Bayesian inference; Bayesian analysis can, moreover, clarify the nature of the 'simplest' hypothesis consistent with the given data. By choosing the prior probabilities of hypotheses, it becomes possible to quantify the scientific judgment that simpler hypotheses are more likely to be correct. Bayesian analysis also shows that a hypothesis with fewer adjustable parameters intrinsically possesses an enhanced posterior probability, due to the clarity of its predictions.

Viral hemorrhagic septicemia virus (VHSV IVb) risk factors and association measures derived by expert panel

USGS Publications Warehouse

,

2010-01-01

Viral hemorrhagic septicemia virus (VHSV) is an OIE-listed pathogen of fish, recently expanding in known host and geographic range in North America. Through a group process designed for subjective probability assessment, an international panel of fish health experts identified and weighted risk factors perceived important to the emergence and spread of the viral genotype, VHSV IVb, within and from the Great Lakes region of the US and Canada. Identified factors included the presence of known VHSV-susceptible species, water temperatures conducive for disease, hydrologic connectivity and proximity to known VHSV-positive areas, untested shipments of live or frozen fish from known positive regions, insufficient regulatory infrastructure for fish health oversight, and uncontrolled exposure to fomites associated with boat and equipment or fish wastes from known VHSV-positive areas. Results provide qualitative insights for use in VHSV surveillance and risk-management planning, and quantitative estimates of contextual risk for use in a Bayesian model combining multiple evidence streams for joint probability assessment of disease freedom status. Consistency checks suggest that the compiled factors positively reflect expert judgment of watershed risk for acquiring VHSV IVb. External validation is recommended as the availability of empirical data permits.

Incorporating expert judgments in utility evaluation of bacteroidales qPCR assays for microbial source tracking in a drinking water source.

PubMed

Åström, Johan; Pettersson, Thomas J R; Reischer, Georg H; Norberg, Tommy; Hermansson, Malte

2015-02-03

Several assays for the detection of host-specific genetic markers of the order Bacteroidales have been developed and used for microbial source tracking (MST) in environmental waters. It is recognized that the source-sensitivity and source-specificity are unknown and variable when introducing these assays in new geographic regions, which reduces their reliability and use. A Bayesian approach was developed to incorporate expert judgments with regional assay sensitivity and specificity assessments in a utility evaluation of a human and a ruminant-specific qPCR assay for MST in a drinking water source. Water samples from Lake Rådasjön were analyzed for E. coli, intestinal enterococci and somatic coliphages through cultivation and for human (BacH) and ruminant-specific (BacR) markers through qPCR assays. Expert judgments were collected regarding the probability of human and ruminant fecal contamination based on fecal indicator organism data and subjective information. Using Bayes formula, the conditional probability of a true human or ruminant fecal contamination given the presence of BacH or BacR was determined stochastically from expert judgments and regional qPCR assay performance, using Beta distributions to represent uncertainties. A web-based computational tool was developed for the procedure, which provides a measure of confidence to findings of host-specific markers and demonstrates the information value from these assays.

Incorporating Expert Judgments in Utility Evaluation of Bacteroidales qPCR Assays for Microbial Source Tracking in a Drinking Water Source

PubMed Central

Åström, Johan; Pettersson, Thomas J. R.; Reischer, Georg H.; Norberg, Tommy; Hermansson, Malte

2017-01-01

Several assays for the detection of host-specific genetic markers of the order Bacteroidales have been developed and used for microbial source tracking (MST) in environmental waters. It is recognized that the source-sensitivity and source-specificity are unknown and variable when introducing these assays in new geographic regions, which reduces their reliability and use. A Bayesian approach was developed to incorporate expert judgments with regional assay sensitivity and specificity assessments in a utility evaluation of a human and a ruminant-specific qPCR assay for MST in a drinking water source. Water samples from Lake Rådasjön were analyzed for E. coli, intestinal enterococci and somatic coliphages through cultivation and for human (BacH) and ruminant-specific (BacR) markers through qPCR assays. Expert judgments were collected regarding the probability of human and ruminant fecal contamination based on fecal indicator organism data and subjective information. Using Bayes formula, the conditional probability of a true human or ruminant fecal contamination given the presence of BacH or BacR was determined stochastically from expert judgments and regional qPCR assay performance, using Beta distributions to represent uncertainties. A web-based computational tool was developed for the procedure, which provides a measure of confidence to findings of host-specific markers and demonstrates the information value from these assays. PMID:25545113

Construction of monitoring model and algorithm design on passenger security during shipping based on improved Bayesian network.

PubMed

Wang, Jiali; Zhang, Qingnian; Ji, Wenfeng

2014-01-01

A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers during shipping is affected by various factors, and it is hard to predict and control. The index system that has the impact on the passenger safety during shipping was established on basis of the multifield coupling theory in this paper. Meanwhile, the fuzzy-precise Bayesian network was applied to monitor the security of passengers in the shipping process. The model was applied to monitor the passenger safety during shipping of a shipping company in Hainan, and the effectiveness of this model was examined. This research work provides guidance for guaranteeing security of passengers during shipping.

Construction of Monitoring Model and Algorithm Design on Passenger Security during Shipping Based on Improved Bayesian Network

PubMed Central

Wang, Jiali; Zhang, Qingnian; Ji, Wenfeng

2014-01-01

A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers during shipping is affected by various factors, and it is hard to predict and control. The index system that has the impact on the passenger safety during shipping was established on basis of the multifield coupling theory in this paper. Meanwhile, the fuzzy-precise Bayesian network was applied to monitor the security of passengers in the shipping process. The model was applied to monitor the passenger safety during shipping of a shipping company in Hainan, and the effectiveness of this model was examined. This research work provides guidance for guaranteeing security of passengers during shipping. PMID:25254227

Bayesian Nonlinear Assimilation of Eulerian and Lagrangian Coastal Flow Data

DTIC Science & Technology

2015-09-30

Lagrangian Coastal Flow Data Dr. Pierre F.J. Lermusiaux Department of Mechanical Engineering Center for Ocean Science and Engineering Massachusetts...Develop and apply theory, schemes and computational systems for rigorous Bayesian nonlinear assimilation of Eulerian and Lagrangian coastal flow data...coastal ocean fields, both in Eulerian and Lagrangian forms. - Further develop and implement our GMM-DO schemes for robust Bayesian nonlinear estimation

Improved Accuracy Using Recursive Bayesian Estimation Based Language Model Fusion in ERP-Based BCI Typing Systems

PubMed Central

Orhan, U.; Erdogmus, D.; Roark, B.; Oken, B.; Purwar, S.; Hild, K. E.; Fowler, A.; Fried-Oken, M.

2013-01-01

RSVP Keyboard™ is an electroencephalography (EEG) based brain computer interface (BCI) typing system, designed as an assistive technology for the communication needs of people with locked-in syndrome (LIS). It relies on rapid serial visual presentation (RSVP) and does not require precise eye gaze control. Existing BCI typing systems which uses event related potentials (ERP) in EEG suffer from low accuracy due to low signal-to-noise ratio. Henceforth, RSVP Keyboard™ utilizes a context based decision making via incorporating a language model, to improve the accuracy of letter decisions. To further improve the contributions of the language model, we propose recursive Bayesian estimation, which relies on non-committing string decisions, and conduct an offline analysis, which compares it with the existing naïve Bayesian fusion approach. The results indicate the superiority of the recursive Bayesian fusion and in the next generation of RSVP Keyboard™ we plan to incorporate this new approach. PMID:23366432

Nonlinear and non-Gaussian Bayesian based handwriting beautification

NASA Astrophysics Data System (ADS)

Shi, Cao; Xiao, Jianguo; Xu, Canhui; Jia, Wenhua

2013-03-01

A framework is proposed in this paper to effectively and efficiently beautify handwriting by means of a novel nonlinear and non-Gaussian Bayesian algorithm. In the proposed framework, format and size of handwriting image are firstly normalized, and then typeface in computer system is applied to optimize vision effect of handwriting. The Bayesian statistics is exploited to characterize the handwriting beautification process as a Bayesian dynamic model. The model parameters to translate, rotate and scale typeface in computer system are controlled by state equation, and the matching optimization between handwriting and transformed typeface is employed by measurement equation. Finally, the new typeface, which is transformed from the original one and gains the best nonlinear and non-Gaussian optimization, is the beautification result of handwriting. Experimental results demonstrate the proposed framework provides a creative handwriting beautification methodology to improve visual acceptance.

Systemic antibiotics in the treatment of aggressive periodontitis. A systematic review and a Bayesian Network meta-analysis.

PubMed

Rabelo, Cleverton Correa; Feres, Magda; Gonçalves, Cristiane; Figueiredo, Luciene C; Faveri, Marcelo; Tu, Yu-Kang; Chambrone, Leandro

2015-07-01

The aim of this study was to assess the effect of systemic antibiotic therapy on the treatment of aggressive periodontitis (AgP). This study was conducted and reported in accordance with the PRISMA statement. The MEDLINE, EMBASE and CENTRAL databases were searched up to June 2014 for randomized clinical trials comparing the treatment of subjects with AgP with either scaling and root planing (SRP) alone or associated with systemic antibiotics. Bayesian network meta-analysis was prepared using the Bayesian random-effects hierarchical models and the outcomes reported at 6-month post-treatment. Out of 350 papers identified, 14 studies were eligible. Greater gain in clinical attachment (CA) (mean difference [MD]: 1.08 mm; p < 0.0001) and reduction in probing depth (PD) (MD: 1.05 mm; p < 0.00001) were observed for SRP + metronidazole (Mtz), and for SRP + Mtz + amoxicillin (Amx) (MD: 0.45 mm, MD: 0.53 mm, respectively; p < 0.00001) than SRP alone/placebo. Bayesian network meta-analysis showed additional benefits in CA gain and PD reduction when SRP was associated with systemic antibiotics. SRP plus systemic antibiotics led to an additional clinical effect compared with SRP alone in the treatment of AgP. Of the antibiotic protocols available for inclusion into the Bayesian network meta-analysis, Mtz and Mtz/Amx provided to the most beneficial outcomes. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

The Value of Information from a GRACE-Enhanced Drought Severity Index

NASA Astrophysics Data System (ADS)

Kuwayama, Y.; Bernknopf, R.; Brookshire, D.; Macauley, M.; Zaitchik, B. F.; Rodell, M.; Vail, P.; Thompson, A.

2015-12-01

In this project, we develop a framework to estimate the economic value of information from the Gravity and Climate Experiment (GRACE) for drought monitoring and to understand how the GRACE Data Assimilation (GRACE-DA) system can inform decision making to improve regional economic outcomes. Specifically, we consider the potential societal value of further incorporating GRACE-DA information into the U.S. Drought Monitor mapmaking process. Research activities include (a) a literature review, (b) a series of listening sessions with experts and stakeholders, (c) the development of a conceptual economic framework based on a Bayesian updating procedure, and (d) an econometric analysis and retrospective case study to understand the GRACE-DA contribution to agricultural policy and production decisions. Taken together, the results from these research activities support our conclusion that GRACE-DA has the potential to lower the variance associated with our understanding of drought and that this improved understanding has the potential to change policy decisions that lead to tangible societal benefits.

Adjusting Beliefs via Transformed Fuzzy Priors

NASA Astrophysics Data System (ADS)

Rattanadamrongaksorn, T.; Sirikanchanarak, D.; Sirisrisakulchai, J.; Sriboonchitta, S.

2018-02-01

Instead of leaving a decision to a pure data-driven system, intervention and collaboration by human would be preferred to fill the gap that machine cannot perform well. In financial applications, for instance, the inference and prediction during structural changes by critical factors; such as market conditions, administrative styles, political policies, etc.; have significant influences to investment strategies. With the conditions differing from the past, we believe that the decision should not be made by only the historical data but also with human estimation. In this study, the updating process by data fusion between expert opinions and statistical observations is thus proposed. The expert’s linguistic terms can be translated into mathematical expressions by the predefined fuzzy numbers and utilized as the initial knowledge for Bayesian statistical framework via the possibility-to-probability transformation. The artificial samples on five scenarios were tested in the univariate problem to demonstrate the methodology. The results showed the shifts and variations appeared on the parameters of the distributions and, as a consequence, adjust the degrees of belief accordingly.

Towards Measurement of Confidence in Safety Cases

NASA Technical Reports Server (NTRS)

Denney, Ewen; Paim Ganesh J.; Habli, Ibrahim

2011-01-01

Arguments in safety cases are predominantly qualitative. This is partly attributed to the lack of sufficient design and operational data necessary to measure the achievement of high-dependability targets, particularly for safety-critical functions implemented in software. The subjective nature of many forms of evidence, such as expert judgment and process maturity, also contributes to the overwhelming dependence on qualitative arguments. However, where data for quantitative measurements is systematically collected, quantitative arguments provide far more benefits over qualitative arguments, in assessing confidence in the safety case. In this paper, we propose a basis for developing and evaluating integrated qualitative and quantitative safety arguments based on the Goal Structuring Notation (GSN) and Bayesian Networks (BN). The approach we propose identifies structures within GSN-based arguments where uncertainties can be quantified. BN are then used to provide a means to reason about confidence in a probabilistic way. We illustrate our approach using a fragment of a safety case for an unmanned aerial system and conclude with some preliminary observations

Software Health Management with Bayesian Networks

NASA Technical Reports Server (NTRS)

Mengshoel, Ole; Schumann, JOhann

2011-01-01

Most modern aircraft as well as other complex machinery is equipped with diagnostics systems for its major subsystems. During operation, sensors provide important information about the subsystem (e.g., the engine) and that information is used to detect and diagnose faults. Most of these systems focus on the monitoring of a mechanical, hydraulic, or electromechanical subsystem of the vehicle or machinery. Only recently, health management systems that monitor software have been developed. In this paper, we will discuss our approach of using Bayesian networks for Software Health Management (SWHM). We will discuss SWHM requirements, which make advanced reasoning capabilities for the detection and diagnosis important. Then we will present our approach to using Bayesian networks for the construction of health models that dynamically monitor a software system and is capable of detecting and diagnosing faults.

A Bayesian Approach for Evaluation of Determinants of Health System Efficiency Using Stochastic Frontier Analysis and Beta Regression.

PubMed

Şenel, Talat; Cengiz, Mehmet Ali

2016-01-01

In today's world, Public expenditures on health are one of the most important issues for governments. These increased expenditures are putting pressure on public budgets. Therefore, health policy makers have focused on the performance of their health systems and many countries have introduced reforms to improve the performance of their health systems. This study investigates the most important determinants of healthcare efficiency for OECD countries using second stage approach for Bayesian Stochastic Frontier Analysis (BSFA). There are two steps in this study. First we measure 29 OECD countries' healthcare efficiency by BSFA using the data from the OECD Health Database. At second stage, we expose the multiple relationships between the healthcare efficiency and characteristics of healthcare systems across OECD countries using Bayesian beta regression.

A fuzzy Bayesian network approach to quantify the human behaviour during an evacuation

NASA Astrophysics Data System (ADS)

Ramli, Nurulhuda; Ghani, Noraida Abdul; Ahmad, Nazihah

2016-06-01

Bayesian Network (BN) has been regarded as a successful representation of inter-relationship of factors affecting human behavior during an emergency. This paper is an extension of earlier work of quantifying the variables involved in the BN model of human behavior during an evacuation using a well-known direct probability elicitation technique. To overcome judgment bias and reduce the expert's burden in providing precise probability values, a new approach for the elicitation technique is required. This study proposes a new fuzzy BN approach for quantifying human behavior during an evacuation. Three major phases of methodology are involved, namely 1) development of qualitative model representing human factors during an evacuation, 2) quantification of BN model using fuzzy probability and 3) inferencing and interpreting the BN result. A case study of three inter-dependencies of human evacuation factors such as danger assessment ability, information about the threat and stressful conditions are used to illustrate the application of the proposed method. This approach will serve as an alternative to the conventional probability elicitation technique in understanding the human behavior during an evacuation.

Bayesian Estimation of Pneumonia Etiology: Epidemiologic Considerations and Applications to the Pneumonia Etiology Research for Child Health Study.

PubMed

Deloria Knoll, Maria; Fu, Wei; Shi, Qiyuan; Prosperi, Christine; Wu, Zhenke; Hammitt, Laura L; Feikin, Daniel R; Baggett, Henry C; Howie, Stephen R C; Scott, J Anthony G; Murdoch, David R; Madhi, Shabir A; Thea, Donald M; Brooks, W Abdullah; Kotloff, Karen L; Li, Mengying; Park, Daniel E; Lin, Wenyi; Levine, Orin S; O'Brien, Katherine L; Zeger, Scott L

2017-06-15

In pneumonia, specimens are rarely obtained directly from the infection site, the lung, so the pathogen causing infection is determined indirectly from multiple tests on peripheral clinical specimens, which may have imperfect and uncertain sensitivity and specificity, so inference about the cause is complex. Analytic approaches have included expert review of case-only results, case-control logistic regression, latent class analysis, and attributable fraction, but each has serious limitations and none naturally integrate multiple test results. The Pneumonia Etiology Research for Child Health (PERCH) study required an analytic solution appropriate for a case-control design that could incorporate evidence from multiple specimens from cases and controls and that accounted for measurement error. We describe a Bayesian integrated approach we developed that combined and extended elements of attributable fraction and latent class analyses to meet some of these challenges and illustrate the advantage it confers regarding the challenges identified for other methods. © The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America.

A Bayesian approach to the characterization of electroencephalographic recordings in premature infants

NASA Astrophysics Data System (ADS)

Mitchell, Timothy J.

Preterm infants are particularly susceptible to cerebral injury, and electroencephalographic (EEG) recordings provide an important diagnostic tool for determining cerebral health. However, interpreting these EEG recordings is challenging and requires the skills of a trained electroencephalographer. Because these EEG specialists are rare, an automated interpretation of newborn EEG recordings would increase access to an important diagnostic tool for physicians. To automate this procedure, we employ a novel Bayesian approach to compute the probability of EEG features (waveforms) including suppression, delta brushes, and delta waves. The power of this approach lies not only in its ability to closely mimic the techniques used by EEG specialists, but also its ability to be generalized to identify other waveforms that may be of interest for future work. The results of these calculations are used in a program designed to output simple statistics related to the presence or absence of such features. Direct comparison of the software with expert human readers has indicated satisfactory performance, and the algorithm has shown promise in its ability to distinguish between infants with normal neurodevelopmental outcome and those with poor neurodevelopmental outcome.

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment.

PubMed

Marvin, Hans J P; Bouzembrak, Yamine; Janssen, Esmée M; van der Zande, Meike; Murphy, Finbarr; Sheehan, Barry; Mullins, Martin; Bouwmeester, Hans

2017-02-01

In this study, a Bayesian Network (BN) was developed for the prediction of the hazard potential and biological effects with the focus on metal- and metal-oxide nanomaterials to support human health risk assessment. The developed BN captures the (inter) relationships between the exposure route, the nanomaterials physicochemical properties and the ultimate biological effects in a holistic manner and was based on international expert consultation and the scientific literature (e.g., in vitro/in vivo data). The BN was validated with independent data extracted from published studies and the accuracy of the prediction of the nanomaterials hazard potential was 72% and for the biological effect 71%, respectively. The application of the BN is shown with scenario studies for TiO 2 , SiO 2 , Ag, CeO 2 , ZnO nanomaterials. It is demonstrated that the BN may be used by different stakeholders at several stages in the risk assessment to predict certain properties of a nanomaterials of which little information is available or to prioritize nanomaterials for further screening.

Bayesian model for fate and transport of polychlorinated biphenyl in upper Hudson River

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steinberg, L.J.; Reckhow, K.H.; Wolpert, R.L.

1996-05-01

Modelers of contaminant fate and transport in surface waters typically rely on literature values when selecting parameter values for mechanistic models. While the expert judgment with which these selections are made is valuable, the information contained in contaminant concentration measurements should not be ignored. In this full-scale Bayesian analysis of polychlorinated biphenyl (PCB) contamination in the upper Hudson River, these two sources of information are combined using Bayes` theorem. A simulation model for the fate and transport of the PCBs in the upper Hudson River forms the basis of the likelihood function while the prior density is developed from literaturemore » values. The method provides estimates for the anaerobic biodegradation half-life, aerobic biodegradation plus volatilization half-life, contaminated sediment depth, and resuspension velocity of 4,400 d, 3.2 d, 0.32 m, and 0.02 m/yr, respectively. These are significantly different than values obtained with more traditional methods, and are shown to produce better predictions than those methods when used in a cross-validation study.« less

Bayesian analyses of time-interval data for environmental radiation monitoring.

PubMed

Luo, Peng; Sharp, Julia L; DeVol, Timothy A

2013-01-01

Time-interval (time difference between two consecutive pulses) analysis based on the principles of Bayesian inference was investigated for online radiation monitoring. Using experimental and simulated data, Bayesian analysis of time-interval data [Bayesian (ti)] was compared with Bayesian and a conventional frequentist analysis of counts in a fixed count time [Bayesian (cnt) and single interval test (SIT), respectively]. The performances of the three methods were compared in terms of average run length (ARL) and detection probability for several simulated detection scenarios. Experimental data were acquired with a DGF-4C system in list mode. Simulated data were obtained using Monte Carlo techniques to obtain a random sampling of the Poisson distribution. All statistical algorithms were developed using the R Project for statistical computing. Bayesian analysis of time-interval information provided a similar detection probability as Bayesian analysis of count information, but the authors were able to make a decision with fewer pulses at relatively higher radiation levels. In addition, for the cases with very short presence of the source (< count time), time-interval information is more sensitive to detect a change than count information since the source data is averaged by the background data over the entire count time. The relationships of the source time, change points, and modifications to the Bayesian approach for increasing detection probability are presented.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rivera, W. Gary; Robinson, David Gerald; Wyss, Gregory Dane

The charter for adversarial delay is to hinder access to critical resources through the use of physical systems increasing an adversarys task time. The traditional method for characterizing access delay has been a simple model focused on accumulating times required to complete each task with little regard to uncertainty, complexity, or decreased efficiency associated with multiple sequential tasks or stress. The delay associated with any given barrier or path is further discounted to worst-case, and often unrealistic, times based on a high-level adversary, resulting in a highly conservative calculation of total delay. This leads to delay systems that require significantmore » funding and personnel resources in order to defend against the assumed threat, which for many sites and applications becomes cost prohibitive. A new methodology has been developed that considers the uncertainties inherent in the problem to develop a realistic timeline distribution for a given adversary path. This new methodology incorporates advanced Bayesian statistical theory and methodologies, taking into account small sample size, expert judgment, human factors and threat uncertainty. The result is an algorithm that can calculate a probability distribution function of delay times directly related to system risk. Through further analysis, the access delay analyst or end user can use the results in making informed decisions while weighing benefits against risks, ultimately resulting in greater system effectiveness with lower cost.« less

Expert Systems: An Overview.

ERIC Educational Resources Information Center

Adiga, Sadashiv

1984-01-01

Discusses: (1) the architecture of expert systems; (2) features that distinguish expert systems from conventional programs; (3) conditions necessary to select a particular application for the development of successful expert systems; (4) issues to be resolved when building expert systems; and (5) limitations. Examples of selected expert systems…

Bayesian approaches for Integrated Water Resources Management. A Mediterranean case study.

NASA Astrophysics Data System (ADS)

Gulliver, Zacarías; Herrero, Javier; José Polo, María

2013-04-01

This study presents the first steps of a short-term/mid-term analysis of the water resources in the Guadalfeo Basin, Spain. Within the basin the recent construction of the Rules dam has required the development of specific management tools and structures for this water system. The climate variability and the high water demand requirements for agriculture irrigation and tourism in this region may cause different controversies in the water management planning process. During the first stages of the study a rigorous analysis of the Water Framework Directive results was done in order to implement the legal requirements and the solutions for the gaps identified by the water authorities. In addition, the stakeholders and water experts identified the variables and geophysical processes for our specific water system case. These particularities need to be taken into account and are required to be reflected in the final computational tool. For decision making process purposes in a mid-term scale, a bayesian network has been used to quantify uncertainty which also provides a structure representation of probabilities, actions-decisions and utilities. On one hand by applying these techniques it is possible the inclusion of decision rules generating influence diagrams that provides clear and coherent semantics for the value of making an observation. On the other hand the utility nodes encode the stakeholders preferences which are measured on a numerical scale, choosing the action that maximizes the expected utility [MEU]. Also this graphical model allows us to identify gaps and project corrective measures, for example, formulating associated scenarios with different event hypotheses. In this sense conditional probability distributions of the seasonal water demand and waste water has been obtained between the established intervals. This fact will give to the regional water managers useful information for future decision making process. The final display is very visual and allows the user to understand quickly the model and the causal relationships between the existing nodes and variables. The input data were collected from the local monitoring networks and the unmonitored data has been generated with a physically based spatially distributed hydrological model WiMMed, which is validated and calibrated. For short-term purposes, pattern analysis has been applied for the management of extreme events scenarios, techniques as Bayesian Neural Networks (BNN) or Gaussian Processes (GP) giving accuracy on the predictions.

Probabilistic models in human sensorimotor control

PubMed Central

Wolpert, Daniel M.

2009-01-01

Sensory and motor uncertainty form a fundamental constraint on human sensorimotor control. Bayesian decision theory (BDT) has emerged as a unifying framework to understand how the central nervous system performs optimal estimation and control in the face of such uncertainty. BDT has two components: Bayesian statistics and decision theory. Here we review Bayesian statistics and show how it applies to estimating the state of the world and our own body. Recent results suggest that when learning novel tasks we are able to learn the statistical properties of both the world and our own sensory apparatus so as to perform estimation using Bayesian statistics. We review studies which suggest that humans can combine multiple sources of information to form maximum likelihood estimates, can incorporate prior beliefs about possible states of the world so as to generate maximum a posteriori estimates and can use Kalman filter-based processes to estimate time-varying states. Finally, we review Bayesian decision theory in motor control and how the central nervous system processes errors to determine loss functions and optimal actions. We review results that suggest we plan movements based on statistics of our actions that result from signal-dependent noise on our motor outputs. Taken together these studies provide a statistical framework for how the motor system performs in the presence of uncertainty. PMID:17628731

Effect of Bayesian Student Modeling on Academic Achievement in Foreign Language Teaching (University Level English Preparatory School Example)

ERIC Educational Resources Information Center

Aslan, Burak Galip; Öztürk, Özlem; Inceoglu, Mustafa Murat

2014-01-01

Considering the increasing importance of adaptive approaches in CALL systems, this study implemented a machine learning based student modeling middleware with Bayesian networks. The profiling approach of the student modeling system is based on Felder and Silverman's Learning Styles Model and Felder and Soloman's Index of Learning Styles…

Designing a Mobile Training System in Rural Areas with Bayesian Factor Models

ERIC Educational Resources Information Center

Omidi Najafabadi, Maryam; Mirdamadi, Seyed Mehdi; Payandeh Najafabadi, Amir Teimour

2014-01-01

The facts that the wireless technologies (1) are more convenient; and (2) need less skill than desktop computers, play a crucial role to decrease digital gap in rural areas. This study employed the Bayesian Confirmatory Factor Analysis (CFA) to design a mobile training system in rural areas of Iran. It categorized challenges, potential, and…

Bayesian bivariate meta-analysis of diagnostic test studies with interpretable priors.

PubMed

Guo, Jingyi; Riebler, Andrea; Rue, Håvard

2017-08-30

In a bivariate meta-analysis, the number of diagnostic studies involved is often very low so that frequentist methods may result in problems. Using Bayesian inference is particularly attractive as informative priors that add a small amount of information can stabilise the analysis without overwhelming the data. However, Bayesian analysis is often computationally demanding and the selection of the prior for the covariance matrix of the bivariate structure is crucial with little data. The integrated nested Laplace approximations method provides an efficient solution to the computational issues by avoiding any sampling, but the important question of priors remain. We explore the penalised complexity (PC) prior framework for specifying informative priors for the variance parameters and the correlation parameter. PC priors facilitate model interpretation and hyperparameter specification as expert knowledge can be incorporated intuitively. We conduct a simulation study to compare the properties and behaviour of differently defined PC priors to currently used priors in the field. The simulation study shows that the PC prior seems beneficial for the variance parameters. The use of PC priors for the correlation parameter results in more precise estimates when specified in a sensible neighbourhood around the truth. To investigate the usage of PC priors in practice, we reanalyse a meta-analysis using the telomerase marker for the diagnosis of bladder cancer and compare the results with those obtained by other commonly used modelling approaches. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Expert systems in civil engineering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kostem, C.N.; Maher, M.L.

1986-01-01

This book presents the papers given at a symposium on expert systems in civil engineering. Topics considered at the symposium included problem solving using expert system techniques, construction schedule analysis, decision making and risk analysis, seismic risk analysis systems, an expert system for inactive hazardous waste site characterization, an expert system for site selection, knowledge engineering, and knowledge-based expert systems in seismic analysis.

Quantum-Like Representation of Non-Bayesian Inference

NASA Astrophysics Data System (ADS)

Asano, M.; Basieva, I.; Khrennikov, A.; Ohya, M.; Tanaka, Y.

2013-01-01

This research is related to the problem of "irrational decision making or inference" that have been discussed in cognitive psychology. There are some experimental studies, and these statistical data cannot be described by classical probability theory. The process of decision making generating these data cannot be reduced to the classical Bayesian inference. For this problem, a number of quantum-like coginitive models of decision making was proposed. Our previous work represented in a natural way the classical Bayesian inference in the frame work of quantum mechanics. By using this representation, in this paper, we try to discuss the non-Bayesian (irrational) inference that is biased by effects like the quantum interference. Further, we describe "psychological factor" disturbing "rationality" as an "environment" correlating with the "main system" of usual Bayesian inference.

Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks.

PubMed

Aussem, Alex; de Morais, Sérgio Rodrigues; Corbex, Marilys

2012-01-01

We propose a new graphical framework for extracting the relevant dietary, social and environmental risk factors that are associated with an increased risk of nasopharyngeal carcinoma (NPC) on a case-control epidemiologic study that consists of 1289 subjects and 150 risk factors. This framework builds on the use of Bayesian networks (BNs) for representing statistical dependencies between the random variables. We discuss a novel constraint-based procedure, called Hybrid Parents and Children (HPC), that builds recursively a local graph that includes all the relevant features statistically associated to the NPC, without having to find the whole BN first. The local graph is afterwards directed by the domain expert according to his knowledge. It provides a statistical profile of the recruited population, and meanwhile helps identify the risk factors associated to NPC. Extensive experiments on synthetic data sampled from known BNs show that the HPC outperforms state-of-the-art algorithms that appeared in the recent literature. From a biological perspective, the present study confirms that chemical products, pesticides and domestic fume intake from incomplete combustion of coal and wood are significantly associated with NPC risk. These results suggest that industrial workers are often exposed to noxious chemicals and poisonous substances that are used in the course of manufacturing. This study also supports previous findings that the consumption of a number of preserved food items, like house made proteins and sheep fat, are a major risk factor for NPC. BNs are valuable data mining tools for the analysis of epidemiologic data. They can explicitly combine both expert knowledge from the field and information inferred from the data. These techniques therefore merit consideration as valuable alternatives to traditional multivariate regression techniques in epidemiologic studies. Copyright © 2011 Elsevier B.V. All rights reserved.

Towards a formal genealogical classification of the Lezgian languages (North Caucasus): testing various phylogenetic methods on lexical data.

PubMed

Kassian, Alexei

2015-01-01

A lexicostatistical classification is proposed for 20 languages and dialects of the Lezgian group of the North Caucasian family, based on meticulously compiled 110-item wordlists, published as part of the Global Lexicostatistical Database project. The lexical data have been subsequently analyzed with the aid of the principal phylogenetic methods, both distance-based and character-based: Starling neighbor joining (StarlingNJ), Neighbor joining (NJ), Unweighted pair group method with arithmetic mean (UPGMA), Bayesian Markov chain Monte Carlo (MCMC), Unweighted maximum parsimony (UMP). Cognation indexes within the input matrix were marked by two different algorithms: traditional etymological approach and phonetic similarity, i.e., the automatic method of consonant classes (Levenshtein distances). Due to certain reasons (first of all, high lexicographic quality of the wordlists and a consensus about the Lezgian phylogeny among Caucasologists), the Lezgian database is a perfect testing area for appraisal of phylogenetic methods. For the etymology-based input matrix, all the phylogenetic methods, with the possible exception of UMP, have yielded trees that are sufficiently compatible with each other to generate a consensus phylogenetic tree of the Lezgian lects. The obtained consensus tree agrees with the traditional expert classification as well as some of the previously proposed formal classifications of this linguistic group. Contrary to theoretical expectations, the UMP method has suggested the least plausible tree of all. In the case of the phonetic similarity-based input matrix, the distance-based methods (StarlingNJ, NJ, UPGMA) have produced the trees that are rather close to the consensus etymology-based tree and the traditional expert classification, whereas the character-based methods (Bayesian MCMC, UMP) have yielded less likely topologies.

Towards a Formal Genealogical Classification of the Lezgian Languages (North Caucasus): Testing Various Phylogenetic Methods on Lexical Data

PubMed Central

Kassian, Alexei

2015-01-01

A lexicostatistical classification is proposed for 20 languages and dialects of the Lezgian group of the North Caucasian family, based on meticulously compiled 110-item wordlists, published as part of the Global Lexicostatistical Database project. The lexical data have been subsequently analyzed with the aid of the principal phylogenetic methods, both distance-based and character-based: Starling neighbor joining (StarlingNJ), Neighbor joining (NJ), Unweighted pair group method with arithmetic mean (UPGMA), Bayesian Markov chain Monte Carlo (MCMC), Unweighted maximum parsimony (UMP). Cognation indexes within the input matrix were marked by two different algorithms: traditional etymological approach and phonetic similarity, i.e., the automatic method of consonant classes (Levenshtein distances). Due to certain reasons (first of all, high lexicographic quality of the wordlists and a consensus about the Lezgian phylogeny among Caucasologists), the Lezgian database is a perfect testing area for appraisal of phylogenetic methods. For the etymology-based input matrix, all the phylogenetic methods, with the possible exception of UMP, have yielded trees that are sufficiently compatible with each other to generate a consensus phylogenetic tree of the Lezgian lects. The obtained consensus tree agrees with the traditional expert classification as well as some of the previously proposed formal classifications of this linguistic group. Contrary to theoretical expectations, the UMP method has suggested the least plausible tree of all. In the case of the phonetic similarity-based input matrix, the distance-based methods (StarlingNJ, NJ, UPGMA) have produced the trees that are rather close to the consensus etymology-based tree and the traditional expert classification, whereas the character-based methods (Bayesian MCMC, UMP) have yielded less likely topologies. PMID:25719456

Assessing the Impact of Retreat Mechanisms in a Simple Antarctic Ice Sheet Model Using Bayesian Calibration.

PubMed

Ruckert, Kelsey L; Shaffer, Gary; Pollard, David; Guan, Yawen; Wong, Tony E; Forest, Chris E; Keller, Klaus

2017-01-01

The response of the Antarctic ice sheet (AIS) to changing climate forcings is an important driver of sea-level changes. Anthropogenic climate change may drive a sizeable AIS tipping point response with subsequent increases in coastal flooding risks. Many studies analyzing flood risks use simple models to project the future responses of AIS and its sea-level contributions. These analyses have provided important new insights, but they are often silent on the effects of potentially important processes such as Marine Ice Sheet Instability (MISI) or Marine Ice Cliff Instability (MICI). These approximations can be well justified and result in more parsimonious and transparent model structures. This raises the question of how this approximation impacts hindcasts and projections. Here, we calibrate a previously published and relatively simple AIS model, which neglects the effects of MICI and regional characteristics, using a combination of observational constraints and a Bayesian inversion method. Specifically, we approximate the effects of missing MICI by comparing our results to those from expert assessments with more realistic models and quantify the bias during the last interglacial when MICI may have been triggered. Our results suggest that the model can approximate the process of MISI and reproduce the projected median melt from some previous expert assessments in the year 2100. Yet, our mean hindcast is roughly 3/4 of the observed data during the last interglacial period and our mean projection is roughly 1/6 and 1/10 of the mean from a model accounting for MICI in the year 2100. These results suggest that missing MICI and/or regional characteristics can lead to a low-bias during warming period AIS melting and hence a potential low-bias in projected sea levels and flood risks.

Bayesian network modeling applied to coastal geomorphology: lessons learned from a decade of experimentation and application

NASA Astrophysics Data System (ADS)

Plant, N. G.; Thieler, E. R.; Gutierrez, B.; Lentz, E. E.; Zeigler, S. L.; Van Dongeren, A.; Fienen, M. N.

2016-12-01

We evaluate the strengths and weaknesses of Bayesian networks that have been used to address scientific and decision-support questions related to coastal geomorphology. We will provide an overview of coastal geomorphology research that has used Bayesian networks and describe what this approach can do and when it works (or fails to work). Over the past decade, Bayesian networks have been formulated to analyze the multi-variate structure and evolution of coastal morphology and associated human and ecological impacts. The approach relates observable system variables to each other by estimating discrete correlations. The resulting Bayesian-networks make predictions that propagate errors, conduct inference via Bayes rule, or both. In scientific applications, the model results are useful for hypothesis testing, using confidence estimates to gage the strength of tests while applications to coastal resource management are aimed at decision-support, where the probabilities of desired ecosystems outcomes are evaluated. The range of Bayesian-network applications to coastal morphology includes emulation of high-resolution wave transformation models to make oceanographic predictions, morphologic response to storms and/or sea-level rise, groundwater response to sea-level rise and morphologic variability, habitat suitability for endangered species, and assessment of monetary or human-life risk associated with storms. All of these examples are based on vast observational data sets, numerical model output, or both. We will discuss the progression of our experiments, which has included testing whether the Bayesian-network approach can be implemented and is appropriate for addressing basic and applied scientific problems and evaluating the hindcast and forecast skill of these implementations. We will present and discuss calibration/validation tests that are used to assess the robustness of Bayesian-network models and we will compare these results to tests of other models. This will demonstrate how Bayesian networks are used to extract new insights about coastal morphologic behavior, assess impacts to societal and ecological systems, and communicate probabilistic predictions to decision makers.

Expert system application for the loading capability assessment of transmission lines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Le, T.L.; Negnevitsky, M.; Piekutowski, M.

1995-11-01

This paper describes the application of an expert system for the evaluation of the short time thermal rating and temperature rise of overhead conductors. The expert system has been developed using a database and Leonardo expert system shell which is gaining popularity among commercial tools for developing expert system applications. The expert system has been found to compare well when evaluated against the site tests. A practical application is given to demonstrate the usefulness of the expert system developed.

Virtual Representation of IID Observations in Bayesian Belief Networks

DTIC Science & Technology

1994-04-01

programs for structuring and using Bayesian inference include ERGO ( Noetic Systems, Inc., 1991) and HUGIN (Andersen, Jensen, Olesen, & Jensen, 1989...Nichols, S.. Chipman, & R. Brennan (Eds.), Cognitively diagnostic assessment. Hillsdale, NJ: Erlbaum. Noetic Systems, Inc. (1991). ERGO [computer...Dr Geore Eageiard Jr Chicago IL 60612 US Naval Academy Division of Educational Studies Annapolis MD 21402-5002 Emory University Dr Janice Gifford 210

Bayesian Inference of Physics Parameters in the DIII-D Charge-Exchange Recombination Spectroscopy System

NASA Astrophysics Data System (ADS)

Bowman, C.; Gibson, K. J.; La Haye, R. J.; Groebner, R. J.; Taylor, N. Z.; Grierson, B. A.

2014-10-01

A Bayesian inference framework has been developed for the DIII-D charge-exchange recombination (CER) system, capable of computing probability distribution functions (PDFs) for desired parameters. CER is a key diagnostic system at DIII-D, measuring important physics parameters such as plasma rotation and impurity ion temperature. This work is motivated by a case in which the CER system was used to probe the plasma rotation radial profile around an m/n = 2/1 tearing mode island rotating at ~ 1 kHz. Due to limited resolution in the tearing mode phase and short integration time, it has proven challenging to observe the structure of the rotation profile across the island. We seek to solve this problem by using the Bayesian framework to improve the estimation accuracy of the plasma rotation, helping to reveal details of how it is perturbed in the magnetic island vicinity. Examples of the PDFs obtained through the Bayesian framework will be presented, and compared with results from a conventional least-squares analysis of the CER data. Work supported by the US DOE under DE-FC02-04ER54698 and DE-AC02-09CH11466.

Bayesian Scoring Systems for Military Pelvic and Perineal Blast Injuries: Is it Time to Take a New Approach?

PubMed

Mossadegh, Somayyeh; He, Shan; Parker, Paul

2016-05-01

Various injury severity scores exist for trauma; it is known that they do not correlate accurately to military injuries. A promising anatomical scoring system for blast pelvic and perineal injury led to the development of an improved scoring system using machine-learning techniques. An unbiased genetic algorithm selected optimal anatomical and physiological parameters from 118 military cases. A Naïve Bayesian model was built using the proposed parameters to predict the probability of survival. Ten-fold cross validation was employed to evaluate its performance. Our model significantly out-performed Injury Severity Score (ISS), Trauma ISS, New ISS, and the Revised Trauma Score in virtually all areas; positive predictive value 0.8941, specificity 0.9027, accuracy 0.9056, and area under curve 0.9059. A two-sample t test showed that the predictive performance of the proposed scoring system was significantly better than the other systems (p < 0.001). With limited resources and the simplest of Bayesian methodologies, we have demonstrated that the Naïve Bayesian model performed significantly better in virtually all areas assessed by current scoring systems used for trauma. This is encouraging and highlights that more can be done to improve trauma systems not only for our military injured, but also for civilian trauma victims. Reprint & Copyright © 2016 Association of Military Surgeons of the U.S.

Identifying the decision to be supported: a review of papers from environmental modelling and software

USGS Publications Warehouse

Sojda, Richard S.; Chen, Serena H.; El Sawah, Sondoss; Guillaume, Joseph H.A.; Jakeman, A.J.; Lautenbach, Sven; McIntosh, Brian S.; Rizzoli, A.E.; Seppelt, Ralf; Struss, Peter; Voinov, Alexey; Volk, Martin

2012-01-01

Two of the basic tenets of decision support system efforts are to help identify and structure the decisions to be supported, and to then provide analysis in how those decisions might be best made. One example from wetland management would be that wildlife biologists must decide when to draw down water levels to optimise aquatic invertebrates as food for breeding ducks. Once such a decision is identified, a system or tool to help them make that decision in the face of current and projected climate conditions could be developed. We examined a random sample of 100 papers published from 2001-2011 in Environmental Modelling and Software that used the phrase “decision support system” or “decision support tool”, and which are characteristic of different sectors. In our review, 41% of the systems and tools related to the water resources sector, 34% were related to agriculture, and 22% to the conservation of fish, wildlife, and protected area management. Only 60% of the papers were deemed to be reporting on DSS. This was based on the papers reviewed not having directly identified a specific decision to be supported. We also report on the techniques that were used to identify the decisions, such as formal survey, focus group, expert opinion, or sole judgment of the author(s). The primary underlying modelling system, e.g., expert system, agent based model, Bayesian belief network, geographical information system (GIS), and the like was categorised next. Finally, since decision support typically should target some aspect of unstructured decisions, we subjectively determined to what degree this was the case. In only 23% of the papers reviewed, did the system appear to tackle unstructured decisions. This knowledge should be useful in helping workers in the field develop more effective systems and tools, especially by being exposed to the approaches in different, but related, disciplines. We propose that a standard blueprint for reporting on DSS be developed for consideration by journal editors to aid them in filtering papers that use the term, “decision support”.

Palmprint identification using FRIT

NASA Astrophysics Data System (ADS)

Kisku, D. R.; Rattani, A.; Gupta, P.; Hwang, C. J.; Sing, J. K.

2011-06-01

This paper proposes a palmprint identification system using Finite Ridgelet Transform (FRIT) and Bayesian classifier. FRIT is applied on the ROI (region of interest), which is extracted from palmprint image, to extract a set of distinctive features from palmprint image. These features are used to classify with the help of Bayesian classifier. The proposed system has been tested on CASIA and IIT Kanpur palmprint databases. The experimental results reveal better performance compared to all well known systems.

NESSUS/EXPERT - An expert system for probabilistic structural analysis methods

NASA Technical Reports Server (NTRS)

Millwater, H.; Palmer, K.; Fink, P.

1988-01-01

An expert system (NESSUS/EXPERT) is presented which provides assistance in using probabilistic structural analysis methods. NESSUS/EXPERT is an interactive menu-driven expert system that provides information to assist in the use of the probabilistic finite element code NESSUS/FEM and the fast probability integrator. NESSUS/EXPERT was developed with a combination of FORTRAN and CLIPS, a C language expert system tool, to exploit the strengths of each language.

Bayesian estimation inherent in a Mexican-hat-type neural network

NASA Astrophysics Data System (ADS)

Takiyama, Ken

2016-05-01

Brain functions, such as perception, motor control and learning, and decision making, have been explained based on a Bayesian framework, i.e., to decrease the effects of noise inherent in the human nervous system or external environment, our brain integrates sensory and a priori information in a Bayesian optimal manner. However, it remains unclear how Bayesian computations are implemented in the brain. Herein, I address this issue by analyzing a Mexican-hat-type neural network, which was used as a model of the visual cortex, motor cortex, and prefrontal cortex. I analytically demonstrate that the dynamics of an order parameter in the model corresponds exactly to a variational inference of a linear Gaussian state-space model, a Bayesian estimation, when the strength of recurrent synaptic connectivity is appropriately stronger than that of an external stimulus, a plausible condition in the brain. This exact correspondence can reveal the relationship between the parameters in the Bayesian estimation and those in the neural network, providing insight for understanding brain functions.

From axiomatics of quantum probability to modelling geological uncertainty and management of intelligent hydrocarbon reservoirs with the theory of open quantum systems.

PubMed

Lozada Aguilar, Miguel Ángel; Khrennikov, Andrei; Oleschko, Klaudia

2018-04-28

As was recently shown by the authors, quantum probability theory can be used for the modelling of the process of decision-making (e.g. probabilistic risk analysis) for macroscopic geophysical structures such as hydrocarbon reservoirs. This approach can be considered as a geophysical realization of Hilbert's programme on axiomatization of statistical models in physics (the famous sixth Hilbert problem). In this conceptual paper , we continue development of this approach to decision-making under uncertainty which is generated by complexity, variability, heterogeneity, anisotropy, as well as the restrictions to accessibility of subsurface structures. The belief state of a geological expert about the potential of exploring a hydrocarbon reservoir is continuously updated by outputs of measurements, and selection of mathematical models and scales of numerical simulation. These outputs can be treated as signals from the information environment E The dynamics of the belief state can be modelled with the aid of the theory of open quantum systems: a quantum state (representing uncertainty in beliefs) is dynamically modified through coupling with E ; stabilization to a steady state determines a decision strategy. In this paper, the process of decision-making about hydrocarbon reservoirs (e.g. 'explore or not?'; 'open new well or not?'; 'contaminated by water or not?'; 'double or triple porosity medium?') is modelled by using the Gorini-Kossakowski-Sudarshan-Lindblad equation. In our model, this equation describes the evolution of experts' predictions about a geophysical structure. We proceed with the information approach to quantum theory and the subjective interpretation of quantum probabilities (due to quantum Bayesianism).This article is part of the theme issue 'Hilbert's sixth problem'. © 2018 The Author(s).

From axiomatics of quantum probability to modelling geological uncertainty and management of intelligent hydrocarbon reservoirs with the theory of open quantum systems

NASA Astrophysics Data System (ADS)

Lozada Aguilar, Miguel Ángel; Khrennikov, Andrei; Oleschko, Klaudia

2018-04-01

As was recently shown by the authors, quantum probability theory can be used for the modelling of the process of decision-making (e.g. probabilistic risk analysis) for macroscopic geophysical structures such as hydrocarbon reservoirs. This approach can be considered as a geophysical realization of Hilbert's programme on axiomatization of statistical models in physics (the famous sixth Hilbert problem). In this conceptual paper, we continue development of this approach to decision-making under uncertainty which is generated by complexity, variability, heterogeneity, anisotropy, as well as the restrictions to accessibility of subsurface structures. The belief state of a geological expert about the potential of exploring a hydrocarbon reservoir is continuously updated by outputs of measurements, and selection of mathematical models and scales of numerical simulation. These outputs can be treated as signals from the information environment E. The dynamics of the belief state can be modelled with the aid of the theory of open quantum systems: a quantum state (representing uncertainty in beliefs) is dynamically modified through coupling with E; stabilization to a steady state determines a decision strategy. In this paper, the process of decision-making about hydrocarbon reservoirs (e.g. `explore or not?'; `open new well or not?'; `contaminated by water or not?'; `double or triple porosity medium?') is modelled by using the Gorini-Kossakowski-Sudarshan-Lindblad equation. In our model, this equation describes the evolution of experts' predictions about a geophysical structure. We proceed with the information approach to quantum theory and the subjective interpretation of quantum probabilities (due to quantum Bayesianism). This article is part of the theme issue `Hilbert's sixth problem'.

Bayesian Inference for Source Term Estimation: Application to the International Monitoring System Radionuclide Network

DTIC Science & Technology

2014-10-01

de l’exactitude et de la précision), comparativement au modèle de mesure plus simple qui n’utilise pas de multiplicateurs. Importance pour la défense...3) Bayesian experimental design for receptor placement in order to maximize the expected information in the measured concen- tration data for...applications of the Bayesian inferential methodology for source recon- struction have used high-quality concentration data from well- designed atmospheric

Assessing uncertainty in sighting records: an example of the Barbary lion.

PubMed

Lee, Tamsin E; Black, Simon A; Fellous, Amina; Yamaguchi, Nobuyuki; Angelici, Francesco M; Al Hikmani, Hadi; Reed, J Michael; Elphick, Chris S; Roberts, David L

2015-01-01

As species become rare and approach extinction, purported sightings can be controversial, especially when scarce management resources are at stake. We consider the probability that each individual sighting of a series is valid. Obtaining these probabilities requires a strict framework to ensure that they are as accurately representative as possible. We used a process, which has proven to provide accurate estimates from a group of experts, to obtain probabilities for the validation of 32 sightings of the Barbary lion. We consider the scenario where experts are simply asked whether a sighting was valid, as well as asking them to score the sighting based on distinguishablity, observer competence, and verifiability. We find that asking experts to provide scores for these three aspects resulted in each sighting being considered more individually, meaning that this new questioning method provides very different estimated probabilities that a sighting is valid, which greatly affects the outcome from an extinction model. We consider linear opinion pooling and logarithm opinion pooling to combine the three scores, and also to combine opinions on each sighting. We find the two methods produce similar outcomes, allowing the user to focus on chosen features of each method, such as satisfying the marginalisation property or being externally Bayesian.

Pragmatic precision oncology: the secondary uses of clinical tumor molecular profiling.

PubMed

Rioth, Matthew J; Thota, Ramya; Staggs, David B; Johnson, Douglas B; Warner, Jeremy L

2016-07-01

Precision oncology increasingly utilizes molecular profiling of tumors to determine treatment decisions with targeted therapeutics. The molecular profiling data is valuable in the treatment of individual patients as well as for multiple secondary uses. To automatically parse, categorize, and aggregate clinical molecular profile data generated during cancer care as well as use this data to address multiple secondary use cases. A system to parse, categorize and aggregate molecular profile data was created. A naÿve Bayesian classifier categorized results according to clinical groups. The accuracy of these systems were validated against a published expertly-curated subset of molecular profiling data. Following one year of operation, 819 samples have been accurately parsed and categorized to generate a data repository of 10,620 genetic variants. The database has been used for operational, clinical trial, and discovery science research. A real-time database of molecular profiling data is a pragmatic solution to several knowledge management problems in the practice and science of precision oncology. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Application of Bayesian informative priors to enhance the transferability of safety performance functions.

PubMed

Farid, Ahmed; Abdel-Aty, Mohamed; Lee, Jaeyoung; Eluru, Naveen

2017-09-01

Safety performance functions (SPFs) are essential tools for highway agencies to predict crashes, identify hotspots and assess safety countermeasures. In the Highway Safety Manual (HSM), a variety of SPFs are provided for different types of roadway facilities, crash types and severity levels. Agencies, lacking the necessary resources to develop own localized SPFs, may opt to apply the HSM's SPFs for their jurisdictions. Yet, municipalities that want to develop and maintain their regional SPFs might encounter the issue of the small sample bias. Bayesian inference is being conducted to address this issue by combining the current data with prior information to achieve reliable results. It follows that the essence of Bayesian statistics is the application of informative priors, obtained from other SPFs or experts' experiences. In this study, we investigate the applicability of informative priors for Bayesian negative binomial SPFs for rural divided multilane highway segments in Florida and California. An SPF with non-informative priors is developed for each state and its parameters' distributions are assigned to the other state's SPF as informative priors. The performances of SPFs are evaluated by applying each state's SPFs to the other state. The analysis is conducted for both total (KABCO) and severe (KAB) crashes. As per the results, applying one state's SPF with informative priors, which are the other state's SPF independent variable estimates, to the latter state's conditions yields better goodness of fit (GOF) values than applying the former state's SPF with non-informative priors to the conditions of the latter state. This is for both total and severe crash SPFs. Hence, for localities where it is not preferred to develop own localized SPFs and adopt SPFs from elsewhere to cut down on resources, application of informative priors is shown to facilitate the process. Copyright © 2017 National Safety Council and Elsevier Ltd. All rights reserved.

Coupling Self-Organizing Maps with a Naïve Bayesian classifier: A case study for classifying Vermont streams using geomorphic, habitat and biological assessment data

NASA Astrophysics Data System (ADS)

Fytilis, N.; Rizzo, D. M.

2012-12-01

Environmental managers are increasingly required to forecast the long-term effects and the resilience or vulnerability of biophysical systems to human-generated stresses. Mitigation strategies for hydrological and environmental systems need to be assessed in the presence of uncertainty. An important aspect of such complex systems is the assessment of variable uncertainty on the model response outputs. We develop a new classification tool that couples a Naïve Bayesian Classifier with a modified Kohonen Self-Organizing Map to tackle this challenge. For proof-of-concept, we use rapid geomorphic and reach-scale habitat assessments data from over 2500 Vermont stream reaches (~1371 stream miles) assessed by the Vermont Agency of Natural Resources (VTANR). In addition, the Vermont Department of Environmental Conservation (VTDEC) estimates stream habitat biodiversity indices (macro-invertebrates and fish) and a variety of water quality data. Our approach fully utilizes the existing VTANR and VTDEC data sets to improve classification of stream-reach habitat and biological integrity. The combined SOM-Naïve Bayesian architecture is sufficiently flexible to allow for continual updates and increased accuracy associated with acquiring new data. The Kohonen Self-Organizing Map (SOM) is an unsupervised artificial neural network that autonomously analyzes properties inherent in a given a set of data. It is typically used to cluster data vectors into similar categories when a priori classes do not exist. The ability of the SOM to convert nonlinear, high dimensional data to some user-defined lower dimension and mine large amounts of data types (i.e., discrete or continuous, biological or geomorphic data) makes it ideal for characterizing the sensitivity of river networks in a variety of contexts. The procedure is data-driven, and therefore does not require the development of site-specific, process-based classification stream models, or sets of if-then-else rules associated with expert systems. This has the potential to save time and resources, while enabling a truly adaptive management approach using existing knowledge (expressed as prior probabilities) and new information (expressed as likelihood functions) to update estimates (i.e., in this case, improved stream classifications expressed as posterior probabilities). The distribution parameters of these posterior probabilities are used to quantify uncertainty associated with environmental data. Since classification plays a leading role in the future development of data-enabled science and engineering, such a computational tool is applicable to a variety of engineering applications. The ability of the new classification neural network to characterize streams with high environmental risk is essential for a proactive adaptive watershed management approach.

Bayesian Modeling of the Assimilative Capacity Component of Stream Nutrient Export

EPA Science Inventory

Implementing stream restoration techniques and best management practices to reduce nonpoint source nutrients implies enhancement of the assimilative capacity for the stream system. In this paper, a Bayesian method for evaluating this component of a TMDL load capacity is developed...

A demonstration of expert systems applications in transportation engineering : volume I, transportation engineers and expert systems.

DOT National Transportation Integrated Search

1987-01-01

Expert systems, a branch of artificial-intelligence studies, is introduced with a view to its relevance in transportation engineering. Knowledge engineering, the process of building expert systems or transferring knowledge from human experts to compu...

Universal Darwinism As a Process of Bayesian Inference.

PubMed

Campbell, John O

2016-01-01

Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an "experiment" in the external world environment, and the results of that "experiment" or the "surprise" entailed by predicted and actual outcomes of the "experiment." Minimization of free energy implies that the implicit measure of "surprise" experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature.

Universal Darwinism As a Process of Bayesian Inference

PubMed Central

Campbell, John O.

2016-01-01

Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an “experiment” in the external world environment, and the results of that “experiment” or the “surprise” entailed by predicted and actual outcomes of the “experiment.” Minimization of free energy implies that the implicit measure of “surprise” experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature. PMID:27375438

PCLIPS: Parallel CLIPS

NASA Technical Reports Server (NTRS)

Gryphon, Coranth D.; Miller, Mark D.

1991-01-01

PCLIPS (Parallel CLIPS) is a set of extensions to the C Language Integrated Production System (CLIPS) expert system language. PCLIPS is intended to provide an environment for the development of more complex, extensive expert systems. Multiple CLIPS expert systems are now capable of running simultaneously on separate processors, or separate machines, thus dramatically increasing the scope of solvable tasks within the expert systems. As a tool for parallel processing, PCLIPS allows for an expert system to add to its fact-base information generated by other expert systems, thus allowing systems to assist each other in solving a complex problem. This allows individual expert systems to be more compact and efficient, and thus run faster or on smaller machines.

ART-Ada: An Ada-based expert system tool

NASA Technical Reports Server (NTRS)

Lee, S. Daniel; Allen, Bradley P.

1990-01-01

The Department of Defense mandate to standardize on Ada as the language for software systems development has resulted in an increased interest in making expert systems technology readily available in Ada environments. NASA's Space Station Freedom is an example of the large Ada software development projects that will require expert systems in the 1990's. Another large scale application that can benefit from Ada based expert system tool technology is the Pilot's Associate (PA) expert system project for military combat aircraft. The Automated Reasoning Tool-Ada (ART-Ada), an Ada expert system tool, is explained. ART-Ada allows applications of a C-based expert system tool called ART-IM to be deployed in various Ada environments. ART-Ada is being used to implement several prototype expert systems for NASA's Space Station Freedom program and the U.S. Air Force.

ART-Ada: An Ada-based expert system tool

NASA Technical Reports Server (NTRS)

Lee, S. Daniel; Allen, Bradley P.

1991-01-01

The Department of Defense mandate to standardize on Ada as the language for software systems development has resulted in increased interest in making expert systems technology readily available in Ada environments. NASA's Space Station Freedom is an example of the large Ada software development projects that will require expert systems in the 1990's. Another large scale application that can benefit from Ada based expert system tool technology is the Pilot's Associate (PA) expert system project for military combat aircraft. Automated Reasoning Tool (ART) Ada, an Ada Expert system tool is described. ART-Ada allow applications of a C-based expert system tool called ART-IM to be deployed in various Ada environments. ART-Ada is being used to implement several prototype expert systems for NASA's Space Station Freedom Program and the U.S. Air Force.

Porting a Mental Expert System to a Mainstream Programming Environment

PubMed Central

Jao, Chiang S.; Hier, Daniel B.; Dollear, Winifred; Fu, Wenying

2001-01-01

Expert systems are increasingly being applied to problems in medical diagnosis and treatment. Initial medical expert systems were programmed in specialized “expert system” shell programming environments. As the power of mainstream programming languages has increased, it has become possible to implement medical expert systems within these mainstream languages. We originally implemented an expert system to record and score the mental status examination utilizing a specialized expert system programming environment. We have now ported that application to a mainstream programming environment without losing any functionality of an accurate and comprehensive diagnostic tool. New system supplements the need of normative consultation report and offline reference library to the traditional patient care system.

A Bayesian alternative for multi-objective ecohydrological model specification

NASA Astrophysics Data System (ADS)

Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori

2018-01-01

Recent studies have identified the importance of vegetation processes in terrestrial hydrologic systems. Process-based ecohydrological models combine hydrological, physical, biochemical and ecological processes of the catchments, and as such are generally more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov chain Monte Carlo (MCMC) techniques. The Bayesian approach offers an appealing alternative to traditional multi-objective hydrologic model calibrations by defining proper prior distributions that can be considered analogous to the ad-hoc weighting often prescribed in multi-objective calibration. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique. In our study, a Pareto-based multi-objective optimization and a formal Bayesian framework are implemented in a conceptual ecohydrological model that combines a hydrological model (HYMOD) and a modified Bucket Grassland Model (BGM). Simulations focused on one objective (streamflow/LAI) and multiple objectives (streamflow and LAI) with different emphasis defined via the prior distribution of the model error parameters. Results show more reliable outputs for both predicted streamflow and LAI using Bayesian multi-objective calibration with specified prior distributions for error parameters based on results from the Pareto front in the ecohydrological modeling. The methodology implemented here provides insight into the usefulness of multiobjective Bayesian calibration for ecohydrologic systems and the importance of appropriate prior distributions in such approaches.

Bayesian networks and statistical analysis application to analyze the diagnostic test accuracy

NASA Astrophysics Data System (ADS)

Orzechowski, P.; Makal, Jaroslaw; Onisko, A.

2005-02-01

The computer aided BPH diagnosis system based on Bayesian network is described in the paper. First result are compared to a given statistical method. Different statistical methods are used successfully in medicine for years. However, the undoubted advantages of probabilistic methods make them useful in application in newly created systems which are frequent in medicine, but do not have full and competent knowledge. The article presents advantages of the computer aided BPH diagnosis system in clinical practice for urologists.

An expert system for the design of heating, ventilating, and air-conditioning systems

NASA Astrophysics Data System (ADS)

Camejo, Pedro Jose

1989-12-01

Expert systems are computer programs that seek to mimic human reason. An expert system shelf, a software program commonly used for developing expert systems in a relatively short time, was used to develop a prototypical expert system for the design of heating, ventilating, and air-conditioning (HVAC) systems in buildings. Because HVAC design involves several related knowledge domains, developing an expert system for HVAC design requires the integration of several smaller expert systems known as knowledge bases. A menu program and several auxiliary programs for gathering data, completing calculations, printing project reports, and passing data between the knowledge bases are needed and have been developed to join the separate knowledge bases into one simple-to-use program unit.

A fast combination method in DSmT and its application to recommender system

PubMed Central

Liu, Yihai

2018-01-01

In many applications involving epistemic uncertainties usually modeled by belief functions, it is often necessary to approximate general (non-Bayesian) basic belief assignments (BBAs) to subjective probabilities (called Bayesian BBAs). This necessity occurs if one needs to embed the fusion result in a system based on the probabilistic framework and Bayesian inference (e.g. tracking systems), or if one needs to make a decision in the decision making problems. In this paper, we present a new fast combination method, called modified rigid coarsening (MRC), to obtain the final Bayesian BBAs based on hierarchical decomposition (coarsening) of the frame of discernment. Regarding this method, focal elements with probabilities are coarsened efficiently to reduce computational complexity in the process of combination by using disagreement vector and a simple dichotomous approach. In order to prove the practicality of our approach, this new approach is applied to combine users’ soft preferences in recommender systems (RSs). Additionally, in order to make a comprehensive performance comparison, the proportional conflict redistribution rule #6 (PCR6) is regarded as a baseline in a range of experiments. According to the results of experiments, MRC is more effective in accuracy of recommendations compared to original Rigid Coarsening (RC) method and comparable in computational time. PMID:29351297

Value of evidence from syndromic surveillance with cumulative evidence from multiple data streams with delayed reporting.

PubMed

Struchen, R; Vial, F; Andersson, M G

2017-04-26

Delayed reporting of health data may hamper the early detection of infectious diseases in surveillance systems. Furthermore, combining multiple data streams, e.g. aiming at improving a system's sensitivity, can be challenging. In this study, we used a Bayesian framework where the result is presented as the value of evidence, i.e. the likelihood ratio for the evidence under outbreak versus baseline conditions. Based on a historical data set of routinely collected cattle mortality events, we evaluated outbreak detection performance (sensitivity, time to detection, in-control run length) under the Bayesian approach among three scenarios: presence of delayed data reporting, but not accounting for it; presence of delayed data reporting accounted for; and absence of delayed data reporting (i.e. an ideal system). Performance on larger and smaller outbreaks was compared with a classical approach, considering syndromes separately or combined. We found that the Bayesian approach performed better than the classical approach, especially for the smaller outbreaks. Furthermore, the Bayesian approach performed similarly well in the scenario where delayed reporting was accounted for to the scenario where it was absent. We argue that the value of evidence framework may be suitable for surveillance systems with multiple syndromes and delayed reporting of data.

MARBLE: A system for executing expert systems in parallel

NASA Technical Reports Server (NTRS)

Myers, Leonard; Johnson, Coe; Johnson, Dean

1990-01-01

This paper details the MARBLE 2.0 system which provides a parallel environment for cooperating expert systems. The work has been done in conjunction with the development of an intelligent computer-aided design system, ICADS, by the CAD Research Unit of the Design Institute at California Polytechnic State University. MARBLE (Multiple Accessed Rete Blackboard Linked Experts) is a system of C Language Production Systems (CLIPS) expert system tool. A copied blackboard is used for communication between the shells to establish an architecture which supports cooperating expert systems that execute in parallel. The design of MARBLE is simple, but it provides support for a rich variety of configurations, while making it relatively easy to demonstrate the correctness of its parallel execution features. In its most elementary configuration, individual CLIPS expert systems execute on their own processors and communicate with each other through a modified blackboard. Control of the system as a whole, and specifically of writing to the blackboard is provided by one of the CLIPS expert systems, an expert control system.

Towards a Fuzzy Bayesian Network Based Approach for Safety Risk Analysis of Tunnel-Induced Pipeline Damage.

PubMed

Zhang, Limao; Wu, Xianguo; Qin, Yawei; Skibniewski, Miroslaw J; Liu, Wenli

2016-02-01

Tunneling excavation is bound to produce significant disturbances to surrounding environments, and the tunnel-induced damage to adjacent underground buried pipelines is of considerable importance for geotechnical practice. A fuzzy Bayesian networks (FBNs) based approach for safety risk analysis is developed in this article with detailed step-by-step procedures, consisting of risk mechanism analysis, the FBN model establishment, fuzzification, FBN-based inference, defuzzification, and decision making. In accordance with the failure mechanism analysis, a tunnel-induced pipeline damage model is proposed to reveal the cause-effect relationships between the pipeline damage and its influential variables. In terms of the fuzzification process, an expert confidence indicator is proposed to reveal the reliability of the data when determining the fuzzy probability of occurrence of basic events, with both the judgment ability level and the subjectivity reliability level taken into account. By means of the fuzzy Bayesian inference, the approach proposed in this article is capable of calculating the probability distribution of potential safety risks and identifying the most likely potential causes of accidents under both prior knowledge and given evidence circumstances. A case concerning the safety analysis of underground buried pipelines adjacent to the construction of the Wuhan Yangtze River Tunnel is presented. The results demonstrate the feasibility of the proposed FBN approach and its application potential. The proposed approach can be used as a decision tool to provide support for safety assurance and management in tunnel construction, and thus increase the likelihood of a successful project in a complex project environment. © 2015 Society for Risk Analysis.

Use of surrogate indicators for the evaluation of potential health risks due to poor urban water quality: A Bayesian Network approach.

PubMed

Wijesiri, Buddhi; Deilami, Kaveh; McGree, James; Goonetilleke, Ashantha

2018-02-01

Urban water pollution poses risks of waterborne infectious diseases. Therefore, in order to improve urban liveability, effective pollution mitigation strategies are required underpinned by predictions generated using water quality models. However, the lack of reliability in current modelling practices detrimentally impacts planning and management decision making. This research study adopted a novel approach in the form of Bayesian Networks to model urban water quality to better investigate the factors that influence risks to human health. The application of Bayesian Networks was found to enhance the integration of quantitative and qualitative spatially distributed data for analysing the influence of environmental and anthropogenic factors using three surrogate indicators of human health risk, namely, turbidity, total nitrogen and fats/oils. Expert knowledge was found to be of critical importance in assessing the interdependent relationships between health risk indicators and influential factors. The spatial variability maps of health risk indicators developed enabled the initial identification of high risk areas in which flooding was found to be the most significant influential factor in relation to human health risk. Surprisingly, population density was found to be less significant in influencing health risk indicators. These high risk areas in turn can be subjected to more in-depth investigations instead of the entire region, saving time and resources. It was evident that decision making in relation to the design of pollution mitigation strategies needs to account for the impact of landscape characteristics on water quality, which can be related to risk to human health. Copyright © 2017 Elsevier Ltd. All rights reserved.

Psychology of developing and designing expert systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tonn, B.; MacGregor, D.

This paper discusses psychological problems relevant to developing and designing expert systems. With respect to the former, the psychological literature suggests that several cognitive biases may affect the elicitation of a valid knowledge base from the expert. The literature also suggests that common expert system inference engines may be quite inconsistent with reasoning heuristics employed by experts. With respect to expert system user interfaces, care should be taken when eliciting uncertainty estimates from users, presenting system conclusions, and ordering questions.

Efficient Dependency Computation for Dynamic Hybrid Bayesian Network in On-line System Health Management Applications

DTIC Science & Technology

2014-10-02

intervals (Neil, Tailor, Marquez, Fenton , & Hear, 2007). This is cumbersome, error prone and usually inaccurate. Even though a universal framework...Science. Neil, M., Tailor, M., Marquez, D., Fenton , N., & Hear. (2007). Inference in Bayesian networks using dynamic discretisation. Statistics

Dynamic Bayesian Networks for Student Modeling

ERIC Educational Resources Information Center

Kaser, Tanja; Klingler, Severin; Schwing, Alexander G.; Gross, Markus

2017-01-01

Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore, an accurate representation and prediction of student knowledge is essential. Bayesian Knowledge Tracing (BKT) is a popular approach for student modeling. The structure of BKT models, however, makes it impossible to represent the hierarchy and…

An interactive Bayesian geostatistical inverse protocol for hydraulic tomography

USGS Publications Warehouse

Fienen, Michael N.; Clemo, Tom; Kitanidis, Peter K.

2008-01-01

Hydraulic tomography is a powerful technique for characterizing heterogeneous hydrogeologic parameters. An explicit trade-off between characterization based on measurement misfit and subjective characterization using prior information is presented. We apply a Bayesian geostatistical inverse approach that is well suited to accommodate a flexible model with the level of complexity driven by the data and explicitly considering uncertainty. Prior information is incorporated through the selection of a parameter covariance model characterizing continuity and providing stability. Often, discontinuities in the parameter field, typically caused by geologic contacts between contrasting lithologic units, necessitate subdivision into zones across which there is no correlation among hydraulic parameters. We propose an interactive protocol in which zonation candidates are implied from the data and are evaluated using cross validation and expert knowledge. Uncertainty introduced by limited knowledge of dynamic regional conditions is mitigated by using drawdown rather than native head values. An adjoint state formulation of MODFLOW-2000 is used to calculate sensitivities which are used both for the solution to the inverse problem and to guide protocol decisions. The protocol is tested using synthetic two-dimensional steady state examples in which the wells are located at the edge of the region of interest.

Validation and sensitivity of the FINE Bayesian network for forecasting aquatic exposure to nano-silver.

PubMed

Money, Eric S; Barton, Lauren E; Dawson, Joseph; Reckhow, Kenneth H; Wiesner, Mark R

2014-03-01

The adaptive nature of the Forecasting the Impacts of Nanomaterials in the Environment (FINE) Bayesian network is explored. We create an updated FINE model (FINEAgNP-2) for predicting aquatic exposure concentrations of silver nanoparticles (AgNP) by combining the expert-based parameters from the baseline model established in previous work with literature data related to particle behavior, exposure, and nano-ecotoxicology via parameter learning. We validate the AgNP forecast from the updated model using mesocosm-scale field data and determine the sensitivity of several key variables to changes in environmental conditions, particle characteristics, and particle fate. Results show that the prediction accuracy of the FINEAgNP-2 model increased approximately 70% over the baseline model, with an error rate of only 20%, suggesting that FINE is a reliable tool to predict aquatic concentrations of nano-silver. Sensitivity analysis suggests that fractal dimension, particle diameter, conductivity, time, and particle fate have the most influence on aquatic exposure given the current knowledge; however, numerous knowledge gaps can be identified to suggest further research efforts that will reduce the uncertainty in subsequent exposure and risk forecasts. Copyright © 2013 Elsevier B.V. All rights reserved.

Extracting recurrent scenarios from narrative texts using a Bayesian network: application to serious occupational accidents with movement disturbance.

PubMed

Abdat, F; Leclercq, S; Cuny, X; Tissot, C

2014-09-01

A probabilistic approach has been developed to extract recurrent serious Occupational Accident with Movement Disturbance (OAMD) scenarios from narrative texts within a prevention framework. Relevant data extracted from 143 accounts was initially coded as logical combinations of generic accident factors. A Bayesian Network (BN)-based model was then built for OAMDs using these data and expert knowledge. A data clustering process was subsequently performed to group the OAMDs into similar classes from generic factor occurrence and pattern standpoints. Finally, the Most Probable Explanation (MPE) was evaluated and identified as the associated recurrent scenario for each class. Using this approach, 8 scenarios were extracted to describe 143 OAMDs in the construction and metallurgy sectors. Their recurrent nature is discussed. Probable generic factor combinations provide a fair representation of particularly serious OAMDs, as described in narrative texts. This work represents a real contribution to raising company awareness of the variety of circumstances, in which these accidents occur, to progressing in the prevention of such accidents and to developing an analysis framework dedicated to this kind of accident. Copyright © 2014 Elsevier Ltd. All rights reserved.

Processes in construction of failure management expert systems from device design information

NASA Technical Reports Server (NTRS)

Malin, Jane T.; Lance, Nick

1987-01-01

This paper analyzes the tasks and problem solving methods used by an engineer in constructing a failure management expert system from design information about the device to te diagnosed. An expert test engineer developed a trouble-shooting expert system based on device design information and experience with similar devices, rather than on specific expert knowledge gained from operating the device or troubleshooting its failures. The construction of the expert system was intensively observed and analyzed. This paper characterizes the knowledge, tasks, methods, and design decisions involved in constructing this type of expert system, and makes recommendations concerning tools for aiding and automating construction of such systems.

Bayesian paradox in homeland security and homeland defense

NASA Astrophysics Data System (ADS)

Jannson, Tomasz; Forrester, Thomas; Wang, Wenjian

2011-06-01

In this paper we discuss a rather surprising result of Bayesian inference analysis: performance of a broad variety of sensors depends not only on a sensor system itself, but also on CONOPS parameters in such a way that even an excellent sensor system can perform poorly if absolute probabilities of a threat (target) are lower than a false alarm probability. This result, which we call Bayesian paradox, holds not only for binary sensors as discussed in the lead author's previous papers, but also for a more general class of multi-target sensors, discussed also in this paper. Examples include: ATR (automatic target recognition), luggage X-ray inspection for explosives, medical diagnostics, car engine diagnostics, judicial decisions, and many other issues.

Precise Network Modeling of Systems Genetics Data Using the Bayesian Network Webserver.

PubMed

Ziebarth, Jesse D; Cui, Yan

2017-01-01

The Bayesian Network Webserver (BNW, http://compbio.uthsc.edu/BNW ) is an integrated platform for Bayesian network modeling of biological datasets. It provides a web-based network modeling environment that seamlessly integrates advanced algorithms for probabilistic causal modeling and reasoning with Bayesian networks. BNW is designed for precise modeling of relatively small networks that contain less than 20 nodes. The structure learning algorithms used by BNW guarantee the discovery of the best (most probable) network structure given the data. To facilitate network modeling across multiple biological levels, BNW provides a very flexible interface that allows users to assign network nodes into different tiers and define the relationships between and within the tiers. This function is particularly useful for modeling systems genetics datasets that often consist of multiscalar heterogeneous genotype-to-phenotype data. BNW enables users to, within seconds or minutes, go from having a simply formatted input file containing a dataset to using a network model to make predictions about the interactions between variables and the potential effects of experimental interventions. In this chapter, we will introduce the functions of BNW and show how to model systems genetics datasets with BNW.

Testing expert systems

NASA Technical Reports Server (NTRS)

Chang, C. L.; Stachowitz, R. A.

1988-01-01

Software quality is of primary concern in all large-scale expert system development efforts. Building appropriate validation and test tools for ensuring software reliability of expert systems is therefore required. The Expert Systems Validation Associate (EVA) is a validation system under development at the Lockheed Artificial Intelligence Center. EVA provides a wide range of validation and test tools to check correctness, consistency, and completeness of an expert system. Testing a major function of EVA. It means executing an expert system with test cases with the intent of finding errors. In this paper, we describe many different types of testing such as function-based testing, structure-based testing, and data-based testing. We describe how appropriate test cases may be selected in order to perform good and thorough testing of an expert system.

Real Time Data System (RTDS)

NASA Technical Reports Server (NTRS)

Muratore, John F.

1991-01-01

Lessons learned from operational real time expert systems are examined. The basic system architecture is discussed. An expert system is any software that performs tasks to a standard that would normally require a human expert. An expert system implies knowledge contained in data rather than code. And an expert system implies the use of heuristics as well as algorithms. The 15 top lessons learned by the operation of a real time data system are presented.

Expert systems applications for space shuttle payload integration automation

NASA Technical Reports Server (NTRS)

Morris, Keith

1988-01-01

Expert systems technologies have been and are continuing to be applied to NASA's Space Shuttle orbiter payload integration problems to provide a level of automation previously unrealizable. NASA's Space Shuttle orbiter was designed to be extremely flexible in its ability to accommodate many different types and combinations of satellites and experiments (payloads) within its payload bay. This flexibility results in differnet and unique engineering resource requirements for each of its payloads, creating recurring payload and cargo integration problems. Expert systems provide a successful solution for these recurring problems. The Orbiter Payload Bay Cabling Expert (EXCABL) was the first expert system, developed to solve the electrical services provisioning problem. A second expert system, EXMATCH, was developed to generate a list of the reusable installation drawings available for each EXCABL solution. These successes have proved the applicability of expert systems technologies to payload integration problems and consequently a third expert system is currently in work. These three expert systems, the manner in which they resolve payload problems and how they will be integrated are described.

Multinomial Bayesian learning for modeling classical and nonclassical receptive field properties.

PubMed

Hosoya, Haruo

2012-08-01

We study the interplay of Bayesian inference and natural image learning in a hierarchical vision system, in relation to the response properties of early visual cortex. We particularly focus on a Bayesian network with multinomial variables that can represent discrete feature spaces similar to hypercolumns combining minicolumns, enforce sparsity of activation to learn efficient representations, and explain divisive normalization. We demonstrate that maximal-likelihood learning using sampling-based Bayesian inference gives rise to classical receptive field properties similar to V1 simple cells and V2 cells, while inference performed on the trained network yields nonclassical context-dependent response properties such as cross-orientation suppression and filling in. Comparison with known physiological properties reveals some qualitative and quantitative similarities.

Uncertainty Quantification Techniques for Population Density Estimates Derived from Sparse Open Source Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stewart, Robert N; White, Devin A; Urban, Marie L

2013-01-01

The Population Density Tables (PDT) project at the Oak Ridge National Laboratory (www.ornl.gov) is developing population density estimates for specific human activities under normal patterns of life based largely on information available in open source. Currently, activity based density estimates are based on simple summary data statistics such as range and mean. Researchers are interested in improving activity estimation and uncertainty quantification by adopting a Bayesian framework that considers both data and sociocultural knowledge. Under a Bayesian approach knowledge about population density may be encoded through the process of expert elicitation. Due to the scale of the PDT effort whichmore » considers over 250 countries, spans 40 human activity categories, and includes numerous contributors, an elicitation tool is required that can be operationalized within an enterprise data collection and reporting system. Such a method would ideally require that the contributor have minimal statistical knowledge, require minimal input by a statistician or facilitator, consider human difficulties in expressing qualitative knowledge in a quantitative setting, and provide methods by which the contributor can appraise whether their understanding and associated uncertainty was well captured. This paper introduces an algorithm that transforms answers to simple, non-statistical questions into a bivariate Gaussian distribution as the prior for the Beta distribution. Based on geometric properties of the Beta distribution parameter feasibility space and the bivariate Gaussian distribution, an automated method for encoding is developed that responds to these challenging enterprise requirements. Though created within the context of population density, this approach may be applicable to a wide array of problem domains requiring informative priors for the Beta distribution.« less

Expert system technology

NASA Technical Reports Server (NTRS)

Prince, Mary Ellen

1987-01-01

The expert system is a computer program which attempts to reproduce the problem-solving behavior of an expert, who is able to view problems from a broad perspective and arrive at conclusions rapidly, using intuition, shortcuts, and analogies to previous situations. Expert systems are a departure from the usual artificial intelligence approach to problem solving. Researchers have traditionally tried to develop general modes of human intelligence that could be applied to many different situations. Expert systems, on the other hand, tend to rely on large quantities of domain specific knowledge, much of it heuristic. The reasoning component of the system is relatively simple and straightforward. For this reason, expert systems are often called knowledge based systems. The report expands on the foregoing. Section 1 discusses the architecture of a typical expert system. Section 2 deals with the characteristics that make a problem a suitable candidate for expert system solution. Section 3 surveys current technology, describing some of the software aids available for expert system development. Section 4 discusses the limitations of the latter. The concluding section makes predictions of future trends.

Expert system for the design of heating, ventilating, and air-conditioning systems. Master's thesis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Camejo, P.J.

1989-12-01

Expert systems are computer programs that seek to mimic human reason. An expert system shelf, a software program commonly used for developing expert systems in a relatively short time, was used to develop a prototypical expert system for the design of heating, ventilating, and air-conditioning (HVAC) systems in buildings. Because HVAC design involves several related knowledge domains, developing an expert system for HVAC design requires the integration of several smaller expert systems known as knowledge bases. A menu program and several auxiliary programs for gathering data, completing calculations, printing project reports, and passing data between the knowledge bases are neededmore » and have been developed to join the separate knowledge bases into one simple-to-use program unit.« less

Spectral Bayesian Knowledge Tracing

ERIC Educational Resources Information Center

Falakmasir, Mohammad; Yudelson, Michael; Ritter, Steve; Koedinger, Ken

2015-01-01

Bayesian Knowledge Tracing (BKT) has been in wide use for modeling student skill acquisition in Intelligent Tutoring Systems (ITS). BKT tracks and updates student's latent mastery of a skill as a probability distribution of a binary variable. BKT does so by accounting for observed student successes in applying the skill correctly, where success is…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ng, B

This survey gives an overview of popular generative models used in the modeling of stochastic temporal systems. In particular, this survey is organized into two parts. The first part discusses the discrete-time representations of dynamic Bayesian networks and dynamic relational probabilistic models, while the second part discusses the continuous-time representation of continuous-time Bayesian networks.

Multi-Scale Validation of a Nanodiamond Drug Delivery System and Multi-Scale Engineering Education

ERIC Educational Resources Information Center

Schwalbe, Michelle Kristin

2010-01-01

This dissertation has two primary concerns: (i) evaluating the uncertainty and prediction capabilities of a nanodiamond drug delivery model using Bayesian calibration and bias correction, and (ii) determining conceptual difficulties of multi-scale analysis from an engineering education perspective. A Bayesian uncertainty quantification scheme…

Engineering monitoring expert system's developer

NASA Technical Reports Server (NTRS)

Lo, Ching F.

1991-01-01

This research project is designed to apply artificial intelligence technology including expert systems, dynamic interface of neural networks, and hypertext to construct an expert system developer. The developer environment is specifically suited to building expert systems which monitor the performance of ground support equipment for propulsion systems and testing facilities. The expert system developer, through the use of a graphics interface and a rule network, will be transparent to the user during rule constructing and data scanning of the knowledge base. The project will result in a software system that allows its user to build specific monitoring type expert systems which monitor various equipments used for propulsion systems or ground testing facilities and accrues system performance information in a dynamic knowledge base.

Expert systems built by the Expert: An evaluation of OPS5

NASA Technical Reports Server (NTRS)

Jackson, Robert

1987-01-01

Two expert systems were written in OPS5 by the expert, a Ph.D. astronomer with no prior experience in artificial intelligence or expert systems, without the use of a knowledge engineer. The first system was built from scratch and uses 146 rules to check for duplication of scientific information within a pool of prospective observations. The second system was grafted onto another expert system and uses 149 additional rules to estimate the spacecraft and ground resources consumed by a set of prospective observations. The small vocabulary, the IF this occurs THEN do that logical structure of OPS5, and the ability to follow program execution allowed the expert to design and implement these systems with only the data structures and rules of another OPS5 system as an example. The modularity of the rules in OPS5 allowed the second system to modify the rulebase of the system onto which it was grafted without changing the code or the operation of that system. These experiences show that experts are able to develop their own expert systems due to the ease of programming and code reusability in OPS5.

Using expert knowledge to incorporate uncertainty in cause-of-death assignments for modeling of cause-specific mortality

USGS Publications Warehouse

Walsh, Daniel P.; Norton, Andrew S.; Storm, Daniel J.; Van Deelen, Timothy R.; Heisy, Dennis M.

2018-01-01

Implicit and explicit use of expert knowledge to inform ecological analyses is becoming increasingly common because it often represents the sole source of information in many circumstances. Thus, there is a need to develop statistical methods that explicitly incorporate expert knowledge, and can successfully leverage this information while properly accounting for associated uncertainty during analysis. Studies of cause-specific mortality provide an example of implicit use of expert knowledge when causes-of-death are uncertain and assigned based on the observer's knowledge of the most likely cause. To explicitly incorporate this use of expert knowledge and the associated uncertainty, we developed a statistical model for estimating cause-specific mortality using a data augmentation approach within a Bayesian hierarchical framework. Specifically, for each mortality event, we elicited the observer's belief of cause-of-death by having them specify the probability that the death was due to each potential cause. These probabilities were then used as prior predictive values within our framework. This hierarchical framework permitted a simple and rigorous estimation method that was easily modified to include covariate effects and regularizing terms. Although applied to survival analysis, this method can be extended to any event-time analysis with multiple event types, for which there is uncertainty regarding the true outcome. We conducted simulations to determine how our framework compared to traditional approaches that use expert knowledge implicitly and assume that cause-of-death is specified accurately. Simulation results supported the inclusion of observer uncertainty in cause-of-death assignment in modeling of cause-specific mortality to improve model performance and inference. Finally, we applied the statistical model we developed and a traditional method to cause-specific survival data for white-tailed deer, and compared results. We demonstrate that model selection results changed between the two approaches, and incorporating observer knowledge in cause-of-death increased the variability associated with parameter estimates when compared to the traditional approach. These differences between the two approaches can impact reported results, and therefore, it is critical to explicitly incorporate expert knowledge in statistical methods to ensure rigorous inference.

Neglected chaos in international stock markets: Bayesian analysis of the joint return-volatility dynamical system

NASA Astrophysics Data System (ADS)

Tsionas, Mike G.; Michaelides, Panayotis G.

2017-09-01

We use a novel Bayesian inference procedure for the Lyapunov exponent in the dynamical system of returns and their unobserved volatility. In the dynamical system, computation of largest Lyapunov exponent by traditional methods is impossible as the stochastic nature has to be taken explicitly into account due to unobserved volatility. We apply the new techniques to daily stock return data for a group of six countries, namely USA, UK, Switzerland, Netherlands, Germany and France, from 2003 to 2014, by means of Sequential Monte Carlo for Bayesian inference. The evidence points to the direction that there is indeed noisy chaos both before and after the recent financial crisis. However, when a much simpler model is examined where the interaction between returns and volatility is not taken into consideration jointly, the hypothesis of chaotic dynamics does not receive much support by the data ("neglected chaos").

Bayesian Inference on Proportional Elections

PubMed Central

Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio

2015-01-01

Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software. PMID:25786259

Bayesian inference on proportional elections.

PubMed

Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio

2015-01-01

Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software.

Artificial intelligence and space power systems automation

NASA Technical Reports Server (NTRS)

Weeks, David J.

1987-01-01

Various applications of artificial intelligence to space electrical power systems are discussed. An overview is given of completed, on-going, and planned knowledge-based system activities. These applications include the Nickel-Cadmium Battery Expert System (NICBES) (the expert system interfaced with the Hubble Space Telescope electrical power system test bed); the early work with the Space Station Experiment Scheduler (SSES); the three expert systems under development in the space station advanced development effort in the core module power management and distribution system test bed; planned cooperation of expert systems in the Core Module Power Management and Distribution (CM/PMAD) system breadboard with expert systems for the space station at other research centers; and the intelligent data reduction expert system under development.

Application of Bayesian inference to the study of hierarchical organization in self-organized complex adaptive systems

NASA Astrophysics Data System (ADS)

Knuth, K. H.

2001-05-01

We consider the application of Bayesian inference to the study of self-organized structures in complex adaptive systems. In particular, we examine the distribution of elements, agents, or processes in systems dominated by hierarchical structure. We demonstrate that results obtained by Caianiello [1] on Hierarchical Modular Systems (HMS) can be found by applying Jaynes' Principle of Group Invariance [2] to a few key assumptions about our knowledge of hierarchical organization. Subsequent application of the Principle of Maximum Entropy allows inferences to be made about specific systems. The utility of the Bayesian method is considered by examining both successes and failures of the hierarchical model. We discuss how Caianiello's original statements suffer from the Mind Projection Fallacy [3] and we restate his assumptions thus widening the applicability of the HMS model. The relationship between inference and statistical physics, described by Jaynes [4], is reiterated with the expectation that this realization will aid the field of complex systems research by moving away from often inappropriate direct application of statistical mechanics to a more encompassing inferential methodology.

Considerations in development of expert systems for real-time space applications

NASA Technical Reports Server (NTRS)

Murugesan, S.

1988-01-01

Over the years, demand on space systems has increased tremendously and this trend will continue for the near future. Enhanced capabilities of space systems, however, can only be met with increased complexity and sophistication of onboard and ground systems. Artificial Intelligence and expert system techniques have great potential in space applications. Expert systems could facilitate autonomous decision making, improve in-orbit fault diagnosis and repair, enhance performance and reduce reliance on ground support. However, real-time expert systems, unlike conventional off-line consultative systems, have to satisfy certain special stringent requirements before they could be used for onboard space applications. Challenging and interesting new environments are faced while developing expert system space applications. This paper discusses the special characteristics, requirements and typical life cycle issues for onboard expert systems. Further, it also describes considerations in design, development, and implementation which are particularly important to real-time expert systems for space applications.

Mineral Potential in India Using Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) Data

NASA Astrophysics Data System (ADS)

Oommen, T.; Chatterjee, S.

2017-12-01

NASA and the Indian Space Research Organization (ISRO) are generating Earth surface features data using Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) within 380 to 2500 nm spectral range. This research focuses on the utilization of such data to better understand the mineral potential in India and to demonstrate the application of spectral data in rock type discrimination and mapping for mineral exploration by using automated mapping techniques. The primary focus area of this research is the Hutti-Maski greenstone belt, located in Karnataka, India. The AVIRIS-NG data was integrated with field analyzed data (laboratory scaled compositional analysis, mineralogy, and spectral library) to characterize minerals and rock types. An expert system was developed to produce mineral maps from AVIRIS-NG data automatically. The ground truth data from the study areas was obtained from the existing literature and collaborators from India. The Bayesian spectral unmixing algorithm was used in AVIRIS-NG data for endmember selection. The classification maps of the minerals and rock types were developed using support vector machine algorithm. The ground truth data was used to verify the mineral maps.

Privacy-Preserving Patient-Centric Clinical Decision Support System on Naïve Bayesian Classification.

PubMed

Liu, Ximeng; Lu, Rongxing; Ma, Jianfeng; Chen, Le; Qin, Baodong

2016-03-01

Clinical decision support system, which uses advanced data mining techniques to help clinician make proper decisions, has received considerable attention recently. The advantages of clinical decision support system include not only improving diagnosis accuracy but also reducing diagnosis time. Specifically, with large amounts of clinical data generated everyday, naïve Bayesian classification can be utilized to excavate valuable information to improve a clinical decision support system. Although the clinical decision support system is quite promising, the flourish of the system still faces many challenges including information security and privacy concerns. In this paper, we propose a new privacy-preserving patient-centric clinical decision support system, which helps clinician complementary to diagnose the risk of patients' disease in a privacy-preserving way. In the proposed system, the past patients' historical data are stored in cloud and can be used to train the naïve Bayesian classifier without leaking any individual patient medical data, and then the trained classifier can be applied to compute the disease risk for new coming patients and also allow these patients to retrieve the top- k disease names according to their own preferences. Specifically, to protect the privacy of past patients' historical data, a new cryptographic tool called additive homomorphic proxy aggregation scheme is designed. Moreover, to leverage the leakage of naïve Bayesian classifier, we introduce a privacy-preserving top- k disease names retrieval protocol in our system. Detailed privacy analysis ensures that patient's information is private and will not be leaked out during the disease diagnosis phase. In addition, performance evaluation via extensive simulations also demonstrates that our system can efficiently calculate patient's disease risk with high accuracy in a privacy-preserving way.

An hierarchical approach to performance evaluation of expert systems

NASA Technical Reports Server (NTRS)

Dominick, Wayne D. (Editor); Kavi, Srinu

1985-01-01

The number and size of expert systems is growing rapidly. Formal evaluation of these systems - which is not performed for many systems - increases the acceptability by the user community and hence their success. Hierarchical evaluation that had been conducted for computer systems is applied for expert system performance evaluation. Expert systems are also evaluated by treating them as software systems (or programs). This paper reports many of the basic concepts and ideas in the Performance Evaluation of Expert Systems Study being conducted at the University of Southwestern Louisiana.

An engineering approach to the use of expert systems technology in avionics applications

NASA Technical Reports Server (NTRS)

Duke, E. L.; Regenie, V. A.; Brazee, M.; Brumbaugh, R. W.

1986-01-01

The concept of using a knowledge compiler to transform the knowledge base and inference mechanism of an expert system into a conventional program is presented. The need to accommodate real-time systems requirements in applications such as embedded avionics is outlined. Expert systems and a brief comparison of expert systems and conventional programs are reviewed. Avionics applications of expert systems are discussed before the discussions of applying the proposed concept to example systems using forward and backward chaining.

Expert systems for real-time monitoring and fault diagnosis

NASA Technical Reports Server (NTRS)

Edwards, S. J.; Caglayan, A. K.

1989-01-01

Methods for building real-time onboard expert systems were investigated, and the use of expert systems technology was demonstrated in improving the performance of current real-time onboard monitoring and fault diagnosis applications. The potential applications of the proposed research include an expert system environment allowing the integration of expert systems into conventional time-critical application solutions, a grammar for describing the discrete event behavior of monitoring and fault diagnosis systems, and their applications to new real-time hardware fault diagnosis and monitoring systems for aircraft.

Equating an expert system to a classifier in order to evaluate the expert system

NASA Technical Reports Server (NTRS)

Odell, Patrick L.

1989-01-01

A strategy to evaluate an expert system is formulated. The strategy proposed is based on finding an equivalent classifier to an expert system and evaluate that classifier with respect to an optimal classifier, a Bayes classifier. Here it is shown that for the rules considered an equivalent classifier exists. Also, a brief consideration of meta and meta-meta rules is included. Also, a taxonomy of expert systems is presented and an assertion made that an equivalent classifier exists for each type of expert system in the taxonomy with associated sets of underlying assumptions.

Three CLIPS-based expert systems for solving engineering problems

NASA Technical Reports Server (NTRS)

Parkinson, W. J.; Luger, G. F.; Bretz, R. E.

1990-01-01

We have written three expert systems, using the CLIPS PC-based expert system shell. These three expert systems are rule based and are relatively small, with the largest containing slightly less than 200 rules. The first expert system is an expert assistant that was written to help users of the ASPEN computer code choose the proper thermodynamic package to use with their particular vapor-liquid equilibrium problem. The second expert system was designed to help petroleum engineers choose the proper enhanced oil recovery method to be used with a given reservoir. The effectiveness of each technique is highly dependent upon the reservoir conditions. The third expert system is a combination consultant and control system. This system was designed specifically for silicon carbide whisker growth. Silicon carbide whiskers are an extremely strong product used to make ceramic and metal composites. The manufacture of whiskers is a very complicated process. which to date. has defied a good mathematical model. The process was run by experts who had gained their expertise by trial and error. A system of rules was devised by these experts both for procedure setup and for the process control. In this paper we discuss the three problem areas of the design, development and evaluation of the CLIPS-based programs.

Using expert knowledge for test linking.

PubMed

Bolsinova, Maria; Hoijtink, Herbert; Vermeulen, Jorine Adinda; Béguin, Anton

2017-12-01

Linking and equating procedures are used to make the results of different test forms comparable. In the cases where no assumption of random equivalent groups can be made some form of linking design is used. In practice the amount of data available to link the two tests is often very limited due to logistic and security reasons, which affects the precision of linking procedures. This study proposes to enhance the quality of linking procedures based on sparse data by using Bayesian methods which combine the information in the linking data with background information captured in informative prior distributions. We propose two methods for the elicitation of prior knowledge about the difference in difficulty of two tests from subject-matter experts and explain how these results can be used in the specification of priors. To illustrate the proposed methods and evaluate the quality of linking with and without informative priors, an empirical example of linking primary school mathematics tests is presented. The results suggest that informative priors can increase the precision of linking without decreasing the accuracy. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Small Knowledge-Based Systems in Education and Training: Something New Under the Sun.

ERIC Educational Resources Information Center

Wilson, Brent G.; Welsh, Jack R.

1986-01-01

Discusses artificial intelligence, robotics, natural language processing, and expert or knowledge-based systems research; examines two large expert systems, MYCIN and XCON; and reviews the resources required to build large expert systems and affordable smaller systems (intelligent job aids) for training. Expert system vendors and products are…

Expert systems for C3I. Volume 1. A user's introduction

NASA Astrophysics Data System (ADS)

Clapp, J. A.; Hockett, S. M.; Prelle, M. J.; Tallant, A. M.; Triant, D. D.

1985-10-01

There has been a tremendous burgeoning of interest in artificial intelligence (AI) over the last few years. Investments of commercial and government sponsors reflect a widespread belief that AI is now ready for practical applications. The area of AI currently receiving the greatest attention and investment is expert system technology. Most major high tech corporations have begun to develop expert systems, and many software houses specializing in expert system tools and applications have recently appeared. The defense community is one of the heaviest investors in expert system technology, and within this community one of the application areas receiving greatest attention is C3I. Many ESD programs are now beginning to ask whether expert system applications for C3I are ready for incorporation into ESD-developed systems, and, if so, what are the potential benefits and risks of doing so. This report was prepared to help ESD and MITRE personnel working on acquisition programs to address these issues and to gain a better understanding of what expert systems are all about. The primary intention of this report is to investigate what expert systems are and the advances that are being made in expert system technology for C3I applications. The report begins with a brief tutorial on expert systems, emphasizing how they differ from conventional software systems and what they are best at doing.

Interfaces and Expert Systems for Online Retrieval.

ERIC Educational Resources Information Center

Kehoe, Cynthia A.

1985-01-01

This paper reviews the history of separate online system interfaces which led to efforts to develop expert systems for searching databases, particularly for end users, and introduces the research on such expert systems. Appended is a bibliography of sources on interfaces and expert systems for online retrieval. (Author/EJS)

Expert and Knowledge Based Systems.

ERIC Educational Resources Information Center

Demaid, Adrian; Edwards, Lyndon

1987-01-01

Discusses the nature and current state of knowledge-based systems and expert systems. Describes an expert system from the viewpoints of a computer programmer and an applications expert. Addresses concerns related to materials selection and forecasts future developments in the teaching of materials engineering. (ML)

Knowledge-Based Systems Approach to Wilderness Fire Management.

NASA Astrophysics Data System (ADS)

Saveland, James M.

The 1988 and 1989 forest fire seasons in the Intermountain West highlight the shortcomings of current fire policy. To fully implement an optimization policy that minimizes the costs and net value change of resources affected by fire, long-range fire severity information is essential, yet lacking. This information is necessary for total mobility of suppression forces, implementing contain and confine suppression strategies, effectively dealing with multiple fire situations, scheduling summer prescribed burning, and wilderness fire management. A knowledge-based system, Delphi, was developed to help provide long-range information. Delphi provides: (1) a narrative of advice on where a fire might spread, if allowed to burn, (2) a summary of recent weather and fire danger information, and (3) a Bayesian analysis of long-range fire danger potential. Uncertainty is inherent in long-range information. Decision theory and judgment research can be used to help understand the heuristics experts use to make decisions under uncertainty, heuristics responsible both for expert performance and bias. Judgment heuristics and resulting bias are examined from a fire management perspective. Signal detection theory and receiver operating curve (ROC) analysis can be used to develop a long-range forecast to improve decisions. ROC analysis mimics some of the heuristics and compensates for some of the bias. Most importantly, ROC analysis displays a continuum of bias from which an optimum operating point can be selected. ROC analysis is especially appropriate for long-range forecasting since (1) the occurrence of possible future events is stated in terms of probability, (2) skill prediction is displayed, (3) inherent trade-offs are displayed, and (4) fire danger is explicitly defined. Statements on the probability of the energy release component of the National Fire Danger Rating System exceeding a critical value later in the fire season can be made early July in the Intermountain West. Delphi was evaluated formally and informally. Continual evaluation and feedback to update knowledge-based systems results in a repository for current knowledge, and a means to devise policy that will augment existing knowledge. Thus, knowledge-based systems can help implement adaptive resource management.

From complex questionnaire and interviewing data to intelligent Bayesian Network models for medical decision support

PubMed Central

Constantinou, Anthony Costa; Fenton, Norman; Marsh, William; Radlinski, Lukasz

2016-01-01

Objectives 1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; 2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; 3) To ensure the BN model can be used for interventional analysis; 4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available. Method The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. Results When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. Conclusions This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way. PMID:26830286

From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support.

PubMed

Constantinou, Anthony Costa; Fenton, Norman; Marsh, William; Radlinski, Lukasz

2016-02-01

(1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; (2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; (3) To ensure the BN model can be used for interventional analysis; (4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available. The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way. Copyright © 2016 Elsevier B.V. All rights reserved.

Reliability and performance evaluation of systems containing embedded rule-based expert systems

NASA Technical Reports Server (NTRS)

Beaton, Robert M.; Adams, Milton B.; Harrison, James V. A.

1989-01-01

A method for evaluating the reliability of real-time systems containing embedded rule-based expert systems is proposed and investigated. It is a three stage technique that addresses the impact of knowledge-base uncertainties on the performance of expert systems. In the first stage, a Markov reliability model of the system is developed which identifies the key performance parameters of the expert system. In the second stage, the evaluation method is used to determine the values of the expert system's key performance parameters. The performance parameters can be evaluated directly by using a probabilistic model of uncertainties in the knowledge-base or by using sensitivity analyses. In the third and final state, the performance parameters of the expert system are combined with performance parameters for other system components and subsystems to evaluate the reliability and performance of the complete system. The evaluation method is demonstrated in the context of a simple expert system used to supervise the performances of an FDI algorithm associated with an aircraft longitudinal flight-control system.

Bayesian analysis of rare events

NASA Astrophysics Data System (ADS)

Straub, Daniel; Papaioannou, Iason; Betz, Wolfgang

2016-06-01

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.

Use of limited data to construct Bayesian networks for probabilistic risk assessment.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Groth, Katrina M.; Swiler, Laura Painton

2013-03-01

Probabilistic Risk Assessment (PRA) is a fundamental part of safety/quality assurance for nuclear power and nuclear weapons. Traditional PRA very effectively models complex hardware system risks using binary probabilistic models. However, traditional PRA models are not flexible enough to accommodate non-binary soft-causal factors, such as digital instrumentation&control, passive components, aging, common cause failure, and human errors. Bayesian Networks offer the opportunity to incorporate these risks into the PRA framework. This report describes the results of an early career LDRD project titled %E2%80%9CUse of Limited Data to Construct Bayesian Networks for Probabilistic Risk Assessment%E2%80%9D. The goal of the work was tomore » establish the capability to develop Bayesian Networks from sparse data, and to demonstrate this capability by producing a data-informed Bayesian Network for use in Human Reliability Analysis (HRA) as part of nuclear power plant Probabilistic Risk Assessment (PRA). This report summarizes the research goal and major products of the research.« less

Exoplanet Biosignatures: Future Directions

PubMed Central

Bains, William; Cronin, Leroy; DasSarma, Shiladitya; Danielache, Sebastian; Domagal-Goldman, Shawn; Kacar, Betul; Kiang, Nancy Y.; Lenardic, Adrian; Reinhard, Christopher T.; Moore, William; Schwieterman, Edward W.; Shkolnik, Evgenya L.; Smith, Harrison B.

2018-01-01

Abstract We introduce a Bayesian method for guiding future directions for detection of life on exoplanets. We describe empirical and theoretical work necessary to place constraints on the relevant likelihoods, including those emerging from better understanding stellar environment, planetary climate and geophysics, geochemical cycling, the universalities of physics and chemistry, the contingencies of evolutionary history, the properties of life as an emergent complex system, and the mechanisms driving the emergence of life. We provide examples for how the Bayesian formalism could guide future search strategies, including determining observations to prioritize or deciding between targeted searches or larger lower resolution surveys to generate ensemble statistics and address how a Bayesian methodology could constrain the prior probability of life with or without a positive detection. Key Words: Exoplanets—Biosignatures—Life detection—Bayesian analysis. Astrobiology 18, 779–824. PMID:29938538

Exoplanet Biosignatures: Future Directions.

PubMed

Walker, Sara I; Bains, William; Cronin, Leroy; DasSarma, Shiladitya; Danielache, Sebastian; Domagal-Goldman, Shawn; Kacar, Betul; Kiang, Nancy Y; Lenardic, Adrian; Reinhard, Christopher T; Moore, William; Schwieterman, Edward W; Shkolnik, Evgenya L; Smith, Harrison B

2018-06-01

We introduce a Bayesian method for guiding future directions for detection of life on exoplanets. We describe empirical and theoretical work necessary to place constraints on the relevant likelihoods, including those emerging from better understanding stellar environment, planetary climate and geophysics, geochemical cycling, the universalities of physics and chemistry, the contingencies of evolutionary history, the properties of life as an emergent complex system, and the mechanisms driving the emergence of life. We provide examples for how the Bayesian formalism could guide future search strategies, including determining observations to prioritize or deciding between targeted searches or larger lower resolution surveys to generate ensemble statistics and address how a Bayesian methodology could constrain the prior probability of life with or without a positive detection. Key Words: Exoplanets-Biosignatures-Life detection-Bayesian analysis. Astrobiology 18, 779-824.

Executing CLIPS expert systems in a distributed environment

NASA Technical Reports Server (NTRS)

Taylor, James; Myers, Leonard

1990-01-01

This paper describes a framework for running cooperating agents in a distributed environment to support the Intelligent Computer Aided Design System (ICADS), a project in progress at the CAD Research Unit of the Design Institute at the California Polytechnic State University. Currently, the systems aids an architectural designer in creating a floor plan that satisfies some general architectural constraints and project specific requirements. At the core of ICADS is the Blackboard Control System. Connected to the blackboard are any number of domain experts called Intelligent Design Tools (IDT). The Blackboard Control System monitors the evolving design as it is being drawn and helps resolve conflicts from the domain experts. The user serves as a partner in this system by manipulating the floor plan in the CAD system and validating recommendations made by the domain experts. The primary components of the Blackboard Control System are two expert systems executed by a modified CLIPS shell. The first is the Message Handler. The second is the Conflict Resolver. The Conflict Resolver synthesizes the suggestions made by the domain experts, which can be either CLIPS expert systems, or compiled C programs. In DEMO1, the current ICADS prototype, the CLIPS domain expert systems are Acoustics, Lighting, Structural, and Thermal; the compiled C domain experts are the CAD system and the User Interface.

Expert system prototype developments for NASA-KSC business and engineering applications

NASA Technical Reports Server (NTRS)

Ragusa, James M.; Gonzalez, Avelino J.

1988-01-01

Prototype expert systems developed for a variety of NASA projects in the business/management and engineering domains are discussed. Business-related problems addressed include an assistant for simulating launch vehicle processing, a plan advisor for the acquisition of automated data processing equipment, and an expert system for the identification of customer requirements. Engineering problems treated include an expert system for detecting potential ignition sources in LOX and gaseous-oxygen transportation systems and an expert system for hazardous-gas detection.

A CLIPS expert system for maximizing alfalfa (Medicago Sativa L.) production

NASA Technical Reports Server (NTRS)

Engel, B. A.; Jones, D. D.; Rhykerd, R. L.; Rhykerd, L. M.; Rhykerd, C. L., Jr.; Rhykerd, C. L.

1990-01-01

An alfalfa management expert system originally developed by Purdue University agricultural scientists on the PC Plus expert system shell from Texas Instrument has been updated and successfully converted to CLIPS (C Language Integrated Production System). This reduces the cost and legal restrictions associated with making the expert system available to agribusiness industries, extension personnel and farm managers and operators. The expert system includes recommendations concerning soil drainage, liming, P and K fertilization, weed control, variety selection and seeding rate including pure live seeds.

A functional-dependencies-based Bayesian networks learning method and its application in a mobile commerce system.

PubMed

Liao, Stephen Shaoyi; Wang, Huai Qing; Li, Qiu Dan; Liu, Wei Yi

2006-06-01

This paper presents a new method for learning Bayesian networks from functional dependencies (FD) and third normal form (3NF) tables in relational databases. The method sets up a linkage between the theory of relational databases and probabilistic reasoning models, which is interesting and useful especially when data are incomplete and inaccurate. The effectiveness and practicability of the proposed method is demonstrated by its implementation in a mobile commerce system.

Knowledge-based fault diagnosis system for refuse collection vehicle

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tan, CheeFai; Juffrizal, K.; Khalil, S. N.

The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledgemore » that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle.« less

Bayesian modelling of the emission spectrum of the Joint European Torus Lithium Beam Emission Spectroscopy system.

PubMed

Kwak, Sehyun; Svensson, J; Brix, M; Ghim, Y-C

2016-02-01

A Bayesian model of the emission spectrum of the JET lithium beam has been developed to infer the intensity of the Li I (2p-2s) line radiation and associated uncertainties. The detected spectrum for each channel of the lithium beam emission spectroscopy system is here modelled by a single Li line modified by an instrumental function, Bremsstrahlung background, instrumental offset, and interference filter curve. Both the instrumental function and the interference filter curve are modelled with non-parametric Gaussian processes. All free parameters of the model, the intensities of the Li line, Bremsstrahlung background, and instrumental offset, are inferred using Bayesian probability theory with a Gaussian likelihood for photon statistics and electronic background noise. The prior distributions of the free parameters are chosen as Gaussians. Given these assumptions, the intensity of the Li line and corresponding uncertainties are analytically available using a Bayesian linear inversion technique. The proposed approach makes it possible to extract the intensity of Li line without doing a separate background subtraction through modulation of the Li beam.

A parallel strategy for implementing real-time expert systems using CLIPS

NASA Technical Reports Server (NTRS)

Ilyes, Laszlo A.; Villaseca, F. Eugenio; Delaat, John

1994-01-01

As evidenced by current literature, there appears to be a continued interest in the study of real-time expert systems. It is generally recognized that speed of execution is only one consideration when designing an effective real-time expert system. Some other features one must consider are the expert system's ability to perform temporal reasoning, handle interrupts, prioritize data, contend with data uncertainty, and perform context focusing as dictated by the incoming data to the expert system. This paper presents a strategy for implementing a real time expert system on the iPSC/860 hypercube parallel computer using CLIPS. The strategy takes into consideration not only the execution time of the software, but also those features which define a true real-time expert system. The methodology is then demonstrated using a practical implementation of an expert system which performs diagnostics on the Space Shuttle Main Engine (SSME). This particular implementation uses an eight node hypercube to process ten sensor measurements in order to simultaneously diagnose five different failure modes within the SSME. The main program is written in ANSI C and embeds CLIPS to better facilitate and debug the rule based expert system.

Incorporating approximation error in surrogate based Bayesian inversion

NASA Astrophysics Data System (ADS)

Zhang, J.; Zeng, L.; Li, W.; Wu, L.

2015-12-01

There are increasing interests in applying surrogates for inverse Bayesian modeling to reduce repetitive evaluations of original model. In this way, the computational cost is expected to be saved. However, the approximation error of surrogate model is usually overlooked. This is partly because that it is difficult to evaluate the approximation error for many surrogates. Previous studies have shown that, the direct combination of surrogates and Bayesian methods (e.g., Markov Chain Monte Carlo, MCMC) may lead to biased estimations when the surrogate cannot emulate the highly nonlinear original system. This problem can be alleviated by implementing MCMC in a two-stage manner. However, the computational cost is still high since a relatively large number of original model simulations are required. In this study, we illustrate the importance of incorporating approximation error in inverse Bayesian modeling. Gaussian process (GP) is chosen to construct the surrogate for its convenience in approximation error evaluation. Numerical cases of Bayesian experimental design and parameter estimation for contaminant source identification are used to illustrate this idea. It is shown that, once the surrogate approximation error is well incorporated into Bayesian framework, promising results can be obtained even when the surrogate is directly used, and no further original model simulations are required.

Explainable expert systems: A research program in information processing

NASA Technical Reports Server (NTRS)

Paris, Cecile L.

1993-01-01

Our work in Explainable Expert Systems (EES) had two goals: to extend and enhance the range of explanations that expert systems can offer, and to ease their maintenance and evolution. As suggested in our proposal, these goals are complementary because they place similar demands on the underlying architecture of the expert system: they both require the knowledge contained in a system to be explicitly represented, in a high-level declarative language and in a modular fashion. With these two goals in mind, the Explainable Expert Systems (EES) framework was designed to remedy limitations to explainability and evolvability that stem from related fundamental flaws in the underlying architecture of current expert systems.

SWAN: An expert system with natural language interface for tactical air capability assessment

NASA Technical Reports Server (NTRS)

Simmons, Robert M.

1987-01-01

SWAN is an expert system and natural language interface for assessing the war fighting capability of Air Force units in Europe. The expert system is an object oriented knowledge based simulation with an alternate worlds facility for performing what-if excursions. Responses from the system take the form of generated text, tables, or graphs. The natural language interface is an expert system in its own right, with a knowledge base and rules which understand how to access external databases, models, or expert systems. The distinguishing feature of the Air Force expert system is its use of meta-knowledge to generate explanations in the frame and procedure based environment.

DATMAN: A reliability data analysis program using Bayesian updating

DOE Office of Scientific and Technical Information (OSTI.GOV)

Becker, M.; Feltus, M.A.

1996-12-31

Preventive maintenance (PM) techniques focus on the prevention of failures, in particular, system components that are important to plant functions. Reliability-centered maintenance (RCM) improves on the PM techniques by introducing a set of guidelines by which to evaluate the system functions. It also minimizes intrusive maintenance, labor, and equipment downtime without sacrificing system performance when its function is essential for plant safety. Both the PM and RCM approaches require that system reliability data be updated as more component failures and operation time are acquired. Systems reliability and the likelihood of component failures can be calculated by Bayesian statistical methods, whichmore » can update these data. The DATMAN computer code has been developed at Penn State to simplify the Bayesian analysis by performing tedious calculations needed for RCM reliability analysis. DATMAN reads data for updating, fits a distribution that best fits the data, and calculates component reliability. DATMAN provides a user-friendly interface menu that allows the user to choose from several common prior and posterior distributions, insert new failure data, and visually select the distribution that matches the data most accurately.« less

Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation

NASA Astrophysics Data System (ADS)

Krishnanathan, Kirubhakaran; Anderson, Sean R.; Billings, Stephen A.; Kadirkamanathan, Visakan

2016-11-01

In this paper, we derive a system identification framework for continuous-time nonlinear systems, for the first time using a simulation-focused computational Bayesian approach. Simulation approaches to nonlinear system identification have been shown to outperform regression methods under certain conditions, such as non-persistently exciting inputs and fast-sampling. We use the approximate Bayesian computation (ABC) algorithm to perform simulation-based inference of model parameters. The framework has the following main advantages: (1) parameter distributions are intrinsically generated, giving the user a clear description of uncertainty, (2) the simulation approach avoids the difficult problem of estimating signal derivatives as is common with other continuous-time methods, and (3) as noted above, the simulation approach improves identification under conditions of non-persistently exciting inputs and fast-sampling. Term selection is performed by judging parameter significance using parameter distributions that are intrinsically generated as part of the ABC procedure. The results from a numerical example demonstrate that the method performs well in noisy scenarios, especially in comparison to competing techniques that rely on signal derivative estimation.

DELTA: An Expert System for Diesel Electric Locomotive Repair

DTIC Science & Technology

1984-06-01

Rules and Inference Mechanisms. AD-P003 943 The ACE (Automated Cable Expert) Exlpelient: Initial Evaluation of an Expert System for Preventive...tions. The first field prototype expert system, designated CATS -i (Computer-Aided Troubleshooting System - Version 1), was delivered in July 1983 and is

Bayesian classification for the selection of in vitro human embryos using morphological and clinical data.

PubMed

Morales, Dinora Araceli; Bengoetxea, Endika; Larrañaga, Pedro; García, Miguel; Franco, Yosu; Fresnada, Mónica; Merino, Marisa

2008-05-01

In vitro fertilization (IVF) is a medically assisted reproduction technique that enables infertile couples to achieve successful pregnancy. Given the uncertainty of the treatment, we propose an intelligent decision support system based on supervised classification by Bayesian classifiers to aid to the selection of the most promising embryos that will form the batch to be transferred to the woman's uterus. The aim of the supervised classification system is to improve overall success rate of each IVF treatment in which a batch of embryos is transferred each time, where the success is achieved when implantation (i.e. pregnancy) is obtained. Due to ethical reasons, different legislative restrictions apply in every country on this technique. In Spain, legislation allows a maximum of three embryos to form each transfer batch. As a result, clinicians prefer to select the embryos by non-invasive embryo examination based on simple methods and observation focused on morphology and dynamics of embryo development after fertilization. This paper proposes the application of Bayesian classifiers to this embryo selection problem in order to provide a decision support system that allows a more accurate selection than with the actual procedures which fully rely on the expertise and experience of embryologists. For this, we propose to take into consideration a reduced subset of feature variables related to embryo morphology and clinical data of patients, and from this data to induce Bayesian classification models. Results obtained applying a filter technique to choose the subset of variables, and the performance of Bayesian classifiers using them, are presented.

Techniques for capturing expert knowledge - An expert systems/hypertext approach

NASA Technical Reports Server (NTRS)

Lafferty, Larry; Taylor, Greg; Schumann, Robin; Evans, Randy; Koller, Albert M., Jr.

1990-01-01

The knowledge-acquisition strategy developed for the Explosive Hazards Classification (EHC) Expert System is described in which expert systems and hypertext are combined, and broad applications are proposed. The EHC expert system is based on rapid prototyping in which primary knowledge acquisition from experts is not emphasized; the explosive hazards technical bulletin, technical guidance, and minimal interviewing are used to develop the knowledge-based system. Hypertext is used to capture the technical information with respect to four issues including procedural, materials, test, and classification issues. The hypertext display allows the integration of multiple knowlege representations such as clarifications or opinions, and thereby allows the performance of a broad range of tasks on a single machine. Among other recommendations, it is suggested that the integration of hypertext and expert systems makes the resulting synergistic system highly efficient.

Diagnosis - Using automatic test equipment and artificial intelligence expert systems

NASA Astrophysics Data System (ADS)

Ramsey, J. E., Jr.

Three expert systems (ATEOPS, ATEFEXPERS, and ATEFATLAS), which were created to direct automatic test equipment (ATE), are reviewed. The purpose of the project was to develop an expert system to troubleshoot the converter-programmer power supply card for the F-15 aircraft and have that expert system direct the automatic test equipment. Each expert system uses a different knowledge base or inference engine, basing the testing on the circuit schematic, test requirements document, or ATLAS code. Implementing generalized modules allows the expert systems to be used for any different unit under test. Using converted ATLAS to LISP code allows the expert system to direct any ATE using ATLAS. The constraint propagated frame system allows for the expansion of control by creating the ATLAS code, checking the code for good software engineering techniques, directing the ATE, and changing the test sequence as needed (planning).

Techniques and implementation of the embedded rule-based expert system using Ada

NASA Technical Reports Server (NTRS)

Liberman, Eugene M.; Jones, Robert E.

1991-01-01

Ada is becoming an increasingly popular programming language for large Government-funded software projects. Ada with its portability, transportability, and maintainability lends itself well to today's complex programming environment. In addition, expert systems have also assured a growing role in providing human-like reasoning capability and expertise for computer systems. The integration of expert system technology with Ada programming language, specifically a rule-based expert system using an ART-Ada (Automated Reasoning Tool for Ada) system shell is discussed. The NASA Lewis Research Center was chosen as a beta test site for ART-Ada. The test was conducted by implementing the existing Autonomous Power EXpert System (APEX), a Lisp-base power expert system, in ART-Ada. Three components, the rule-based expert system, a graphics user interface, and communications software make up SMART-Ada (Systems fault Management with ART-Ada). The main objective, to conduct a beta test on the ART-Ada rule-based expert system shell, was achieved. The system is operational. New Ada tools will assist in future successful projects. ART-Ada is one such tool and is a viable alternative to the straight Ada code when an application requires a rule-based or knowledge-based approach.

A comparison of Monte Carlo-based Bayesian parameter estimation methods for stochastic models of genetic networks

PubMed Central

Zaikin, Alexey; Míguez, Joaquín

2017-01-01

We compare three state-of-the-art Bayesian inference methods for the estimation of the unknown parameters in a stochastic model of a genetic network. In particular, we introduce a stochastic version of the paradigmatic synthetic multicellular clock model proposed by Ullner et al., 2007. By introducing dynamical noise in the model and assuming that the partial observations of the system are contaminated by additive noise, we enable a principled mechanism to represent experimental uncertainties in the synthesis of the multicellular system and pave the way for the design of probabilistic methods for the estimation of any unknowns in the model. Within this setup, we tackle the Bayesian estimation of a subset of the model parameters. Specifically, we compare three Monte Carlo based numerical methods for the approximation of the posterior probability density function of the unknown parameters given a set of partial and noisy observations of the system. The schemes we assess are the particle Metropolis-Hastings (PMH) algorithm, the nonlinear population Monte Carlo (NPMC) method and the approximate Bayesian computation sequential Monte Carlo (ABC-SMC) scheme. We present an extensive numerical simulation study, which shows that while the three techniques can effectively solve the problem there are significant differences both in estimation accuracy and computational efficiency. PMID:28797087

Statistical comparison of a hybrid approach with approximate and exact inference models for Fusion 2+

NASA Astrophysics Data System (ADS)

Lee, K. David; Wiesenfeld, Eric; Gelfand, Andrew

2007-04-01

One of the greatest challenges in modern combat is maintaining a high level of timely Situational Awareness (SA). In many situations, computational complexity and accuracy considerations make the development and deployment of real-time, high-level inference tools very difficult. An innovative hybrid framework that combines Bayesian inference, in the form of Bayesian Networks, and Possibility Theory, in the form of Fuzzy Logic systems, has recently been introduced to provide a rigorous framework for high-level inference. In previous research, the theoretical basis and benefits of the hybrid approach have been developed. However, lacking is a concrete experimental comparison of the hybrid framework with traditional fusion methods, to demonstrate and quantify this benefit. The goal of this research, therefore, is to provide a statistical analysis on the comparison of the accuracy and performance of hybrid network theory, with pure Bayesian and Fuzzy systems and an inexact Bayesian system approximated using Particle Filtering. To accomplish this task, domain specific models will be developed under these different theoretical approaches and then evaluated, via Monte Carlo Simulation, in comparison to situational ground truth to measure accuracy and fidelity. Following this, a rigorous statistical analysis of the performance results will be performed, to quantify the benefit of hybrid inference to other fusion tools.

Expert Systems--The New International Language of Business.

ERIC Educational Resources Information Center

Sondak, Norman E.; And Others

A discussion of expert systems, computer programs designed to simulate human reasoning and expertise, begins with the assumption that few business educators understand the impact that expert systems will have on international business. The fundamental principles of the design and development of expert systems in business are outlined, with special…

Order priors for Bayesian network discovery with an application to malware phylogeny

DOE PAGES

Oyen, Diane; Anderson, Blake; Sentz, Kari; ...

2017-09-15

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less

Order priors for Bayesian network discovery with an application to malware phylogeny

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oyen, Diane; Anderson, Blake; Sentz, Kari

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less

Application of artificial intelligence to the management of urological cancer.

PubMed

Abbod, Maysam F; Catto, James W F; Linkens, Derek A; Hamdy, Freddie C

2007-10-01

Artificial intelligence techniques, such as artificial neural networks, Bayesian belief networks and neuro-fuzzy modeling systems, are complex mathematical models based on the human neuronal structure and thinking. Such tools are capable of generating data driven models of biological systems without making assumptions based on statistical distributions. A large amount of study has been reported of the use of artificial intelligence in urology. We reviewed the basic concepts behind artificial intelligence techniques and explored the applications of this new dynamic technology in various aspects of urological cancer management. A detailed and systematic review of the literature was performed using the MEDLINE and Inspec databases to discover reports using artificial intelligence in urological cancer. The characteristics of machine learning and their implementation were described and reports of artificial intelligence use in urological cancer were reviewed. While most researchers in this field were found to focus on artificial neural networks to improve the diagnosis, staging and prognostic prediction of urological cancers, some groups are exploring other techniques, such as expert systems and neuro-fuzzy modeling systems. Compared to traditional regression statistics artificial intelligence methods appear to be accurate and more explorative for analyzing large data cohorts. Furthermore, they allow individualized prediction of disease behavior. Each artificial intelligence method has characteristics that make it suitable for different tasks. The lack of transparency of artificial neural networks hinders global scientific community acceptance of this method but this can be overcome by neuro-fuzzy modeling systems.

Eyewitness identification: Bayesian information gain, base-rate effect equivalency curves, and reasonable suspicion.

PubMed

Wells, Gary L; Yang, Yueran; Smalarz, Laura

2015-04-01

We provide a novel Bayesian treatment of the eyewitness identification problem as it relates to various system variables, such as instruction effects, lineup presentation format, lineup-filler similarity, lineup administrator influence, and show-ups versus lineups. We describe why eyewitness identification is a natural Bayesian problem and how numerous important observations require careful consideration of base rates. Moreover, we argue that the base rate in eyewitness identification should be construed as a system variable (under the control of the justice system). We then use prior-by-posterior curves and information-gain curves to examine data obtained from a large number of published experiments. Next, we show how information-gain curves are moderated by system variables and by witness confidence and we note how information-gain curves reveal that lineups are consistently more proficient at incriminating the guilty than they are at exonerating the innocent. We then introduce a new type of analysis that we developed called base rate effect-equivalency (BREE) curves. BREE curves display how much change in the base rate is required to match the impact of any given system variable. The results indicate that even relatively modest changes to the base rate can have more impact on the reliability of eyewitness identification evidence than do the traditional system variables that have received so much attention in the literature. We note how this Bayesian analysis of eyewitness identification has implications for the question of whether there ought to be a reasonable-suspicion criterion for placing a person into the jeopardy of an identification procedure. (c) 2015 APA, all rights reserved).

Expert Systems in Education and Training: Automated Job Aids or Sophisticated Instructional Media?

ERIC Educational Resources Information Center

Romiszowski, Alexander J.

1987-01-01

Describes the current status and limitations of expert systems, and explores the possible applications of such systems in education and training. The use of expert systems as tutors, as job aids, and as a vehicle for students to develop their own expert systems on specific topics are discussed. (40 references) (CLB)

A surrogate-based sensitivity quantification and Bayesian inversion of a regional groundwater flow model

NASA Astrophysics Data System (ADS)

Chen, Mingjie; Izady, Azizallah; Abdalla, Osman A.; Amerjeed, Mansoor

2018-02-01

Bayesian inference using Markov Chain Monte Carlo (MCMC) provides an explicit framework for stochastic calibration of hydrogeologic models accounting for uncertainties; however, the MCMC sampling entails a large number of model calls, and could easily become computationally unwieldy if the high-fidelity hydrogeologic model simulation is time consuming. This study proposes a surrogate-based Bayesian framework to address this notorious issue, and illustrates the methodology by inverse modeling a regional MODFLOW model. The high-fidelity groundwater model is approximated by a fast statistical model using Bagging Multivariate Adaptive Regression Spline (BMARS) algorithm, and hence the MCMC sampling can be efficiently performed. In this study, the MODFLOW model is developed to simulate the groundwater flow in an arid region of Oman consisting of mountain-coast aquifers, and used to run representative simulations to generate training dataset for BMARS model construction. A BMARS-based Sobol' method is also employed to efficiently calculate input parameter sensitivities, which are used to evaluate and rank their importance for the groundwater flow model system. According to sensitivity analysis, insensitive parameters are screened out of Bayesian inversion of the MODFLOW model, further saving computing efforts. The posterior probability distribution of input parameters is efficiently inferred from the prescribed prior distribution using observed head data, demonstrating that the presented BMARS-based Bayesian framework is an efficient tool to reduce parameter uncertainties of a groundwater system.

Expert systems and simulation models; Proceedings of the Seminar, Tucson, AZ, November 18, 19, 1985

NASA Technical Reports Server (NTRS)

1986-01-01

The seminar presents papers on modeling and simulation methodology, artificial intelligence and expert systems, environments for simulation/expert system development, and methodology for simulation/expert system development. Particular attention is given to simulation modeling concepts and their representation, modular hierarchical model specification, knowledge representation, and rule-based diagnostic expert system development. Other topics include the combination of symbolic and discrete event simulation, real time inferencing, and the management of large knowledge-based simulation projects.

Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

NASA Technical Reports Server (NTRS)

Stclair, D. C.; Sabharwal, C. L.; Bond, W. E.; Hacke, Keith

1988-01-01

Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.

ART-Ada design project, phase 2

NASA Technical Reports Server (NTRS)

Lee, S. Daniel; Allen, Bradley P.

1990-01-01

Interest in deploying expert systems in Ada has increased. An Ada based expert system tool is described called ART-Ada, which was built to support research into the language and methodological issues of expert systems in Ada. ART-Ada allows applications of an existing expert system tool called ART-IM (Automated Reasoning Tool for Information Management) to be deployed in various Ada environments. ART-IM, a C-based expert system tool, is used to generate Ada source code which is compiled and linked with an Ada based inference engine to produce an Ada executable image. ART-Ada is being used to implement several expert systems for NASA's Space Station Freedom Program and the U.S. Air Force.

Modeling Increased Complexity and the Reliance on Automation: FLightdeck Automation Problems (FLAP) Model

NASA Technical Reports Server (NTRS)

Ancel, Ersin; Shih, Ann T.

2014-01-01

This paper highlights the development of a model that is focused on the safety issue of increasing complexity and reliance on automation systems in transport category aircraft. Recent statistics show an increase in mishaps related to manual handling and automation errors due to pilot complacency and over-reliance on automation, loss of situational awareness, automation system failures and/or pilot deficiencies. Consequently, the aircraft can enter a state outside the flight envelope and/or air traffic safety margins which potentially can lead to loss-of-control (LOC), controlled-flight-into-terrain (CFIT), or runway excursion/confusion accidents, etc. The goal of this modeling effort is to provide NASA's Aviation Safety Program (AvSP) with a platform capable of assessing the impacts of AvSP technologies and products towards reducing the relative risk of automation related accidents and incidents. In order to do so, a generic framework, capable of mapping both latent and active causal factors leading to automation errors, is developed. Next, the framework is converted into a Bayesian Belief Network model and populated with data gathered from Subject Matter Experts (SMEs). With the insertion of technologies and products, the model provides individual and collective risk reduction acquired by technologies and methodologies developed within AvSP.

A neural network architecture for implementation of expert systems for real time monitoring

NASA Technical Reports Server (NTRS)

Ramamoorthy, P. A.

1991-01-01

Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.

Novel health economic evaluation of a vaccination strategy to prevent HPV-related diseases: the BEST study.

PubMed

Favato, Giampiero; Baio, Gianluca; Capone, Alessandro; Marcellusi, Andrea; Costa, Silvano; Garganese, Giorgia; Picardo, Mauro; Drummond, Mike; Jonsson, Bengt; Scambia, Giovanni; Zweifel, Peter; Mennini, Francesco S

2012-12-01

The development of human papillomavirus (HPV)-related diseases is not understood perfectly and uncertainties associated with commonly utilized probabilistic models must be considered. The study assessed the cost-effectiveness of a quadrivalent-based multicohort HPV vaccination strategy within a Bayesian framework. A full Bayesian multicohort Markov model was used, in which all unknown quantities were associated with suitable probability distributions reflecting the state of currently available knowledge. These distributions were informed by observed data or expert opinion. The model cycle lasted 1 year, whereas the follow-up time horizon was 90 years. Precancerous cervical lesions, cervical cancers, and anogenital warts were considered as outcomes. The base case scenario (2 cohorts of girls aged 12 and 15 y) and other multicohort vaccination strategies (additional cohorts aged 18 and 25 y) were cost-effective, with a discounted cost per quality-adjusted life-year gained that corresponded to €12,013, €13,232, and €15,890 for vaccination programs based on 2, 3, and 4 cohorts, respectively. With multicohort vaccination strategies, the reduction in the number of HPV-related events occurred earlier (range, 3.8-6.4 y) when compared with a single cohort. The analysis of the expected value of information showed that the results of the model were subject to limited uncertainty (cost per patient = €12.6). This methodological approach is designed to incorporate the uncertainty associated with HPV vaccination. Modeling the cost-effectiveness of a multicohort vaccination program with Bayesian statistics confirmed the value for money of quadrivalent-based HPV vaccination. The expected value of information gave the most appropriate and feasible representation of the true value of this program.

Combined N-of-1 trials to investigate mexiletine in non-dystrophic myotonia using a Bayesian approach; study rationale and protocol.

PubMed

Stunnenberg, Bas C; Woertman, Willem; Raaphorst, Joost; Statland, Jeffrey M; Griggs, Robert C; Timmermans, Janneke; Saris, Christiaan G; Schouwenberg, Bas J; Groenewoud, Hans M; Stegeman, Dick F; van Engelen, Baziel G M; Drost, Gea; van der Wilt, Gert Jan

2015-03-25

To obtain evidence for the clinical and cost-effectiveness of treatments for patients with rare diseases is a challenge. Non-dystrophic myotonia (NDM) is a group of inherited, rare muscle diseases characterized by muscle stiffness. The reimbursement of mexiletine, the expert opinion drug for NDM, has been discontinued in some countries due to a lack of independent randomized controlled trials (RCTs). It remains unclear however, which concessions can be accepted towards the level 1 evidence needed for coverage decisions, in rare diseases. Considering the large number of rare diseases with a lack of treatment evidence, more experience with innovative trial designs is needed. Both NDM and mexiletine are well suited for an N-of-1 trial design. A Bayesian approach allows for the combination of N-of-1 trials, which enables the assessment of outcomes on the patient and group level simultaneously. We will combine 30 individual, double-blind, randomized, placebo-controlled N-of-1 trials of mexiletine (600 mg daily) vs. placebo in genetically confirmed NDM patients using hierarchical Bayesian modeling. Our results will be compared and combined with the main results of an international cross-over RCT (mexiletine vs. placebo in NDM) published in 2012 that will be used as an informative prior. Similar criteria of eligibility, treatment regimen, end-points and measurement instruments are employed as used in the international cross-over RCT. The treatment of patients with NDM with mexiletine offers a unique opportunity to compare outcomes and efficiency of novel N-of-1 trial-based designs and conventional approaches in producing evidence of clinical and cost-effectiveness of treatments for patients with rare diseases. ClinicalTrials.gov Identifier: NCT02045667.

Continuous event monitoring via a Bayesian predictive approach.

PubMed

Di, Jianing; Wang, Daniel; Brashear, H Robert; Dragalin, Vladimir; Krams, Michael

2016-01-01

In clinical trials, continuous monitoring of event incidence rate plays a critical role in making timely decisions affecting trial outcome. For example, continuous monitoring of adverse events protects the safety of trial participants, while continuous monitoring of efficacy events helps identify early signals of efficacy or futility. Because the endpoint of interest is often the event incidence associated with a given length of treatment duration (e.g., incidence proportion of an adverse event with 2 years of dosing), assessing the event proportion before reaching the intended treatment duration becomes challenging, especially when the event onset profile evolves over time with accumulated exposure. In particular, in the earlier part of the study, ignoring censored subjects may result in significant bias in estimating the cumulative event incidence rate. Such a problem is addressed using a predictive approach in the Bayesian framework. In the proposed approach, experts' prior knowledge about both the frequency and timing of the event occurrence is combined with observed data. More specifically, during any interim look, each event-free subject will be counted with a probability that is derived using prior knowledge. The proposed approach is particularly useful in early stage studies for signal detection based on limited information. But it can also be used as a tool for safety monitoring (e.g., data monitoring committee) during later stage trials. Application of the approach is illustrated using a case study where the incidence rate of an adverse event is continuously monitored during an Alzheimer's disease clinical trial. The performance of the proposed approach is also assessed and compared with other Bayesian and frequentist methods via simulation. Copyright © 2015 John Wiley & Sons, Ltd.

Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition

PubMed Central

Bill, Johannes; Buesing, Lars; Habenschuss, Stefan; Nessler, Bernhard; Maass, Wolfgang; Legenstein, Robert

2015-01-01

During the last decade, Bayesian probability theory has emerged as a framework in cognitive science and neuroscience for describing perception, reasoning and learning of mammals. However, our understanding of how probabilistic computations could be organized in the brain, and how the observed connectivity structure of cortical microcircuits supports these calculations, is rudimentary at best. In this study, we investigate statistical inference and self-organized learning in a spatially extended spiking network model, that accommodates both local competitive and large-scale associative aspects of neural information processing, under a unified Bayesian account. Specifically, we show how the spiking dynamics of a recurrent network with lateral excitation and local inhibition in response to distributed spiking input, can be understood as sampling from a variational posterior distribution of a well-defined implicit probabilistic model. This interpretation further permits a rigorous analytical treatment of experience-dependent plasticity on the network level. Using machine learning theory, we derive update rules for neuron and synapse parameters which equate with Hebbian synaptic and homeostatic intrinsic plasticity rules in a neural implementation. In computer simulations, we demonstrate that the interplay of these plasticity rules leads to the emergence of probabilistic local experts that form distributed assemblies of similarly tuned cells communicating through lateral excitatory connections. The resulting sparse distributed spike code of a well-adapted network carries compressed information on salient input features combined with prior experience on correlations among them. Our theory predicts that the emergence of such efficient representations benefits from network architectures in which the range of local inhibition matches the spatial extent of pyramidal cells that share common afferent input. PMID:26284370

Use of Bayesian event trees in semi-quantitative volcano eruption forecasting and hazard analysis

NASA Astrophysics Data System (ADS)

Wright, Heather; Pallister, John; Newhall, Chris

2015-04-01

Use of Bayesian event trees to forecast eruptive activity during volcano crises is an increasingly common practice for the USGS-USAID Volcano Disaster Assistance Program (VDAP) in collaboration with foreign counterparts. This semi-quantitative approach combines conceptual models of volcanic processes with current monitoring data and patterns of occurrence to reach consensus probabilities. This approach allows a response team to draw upon global datasets, local observations, and expert judgment, where the relative influence of these data depends upon the availability and quality of monitoring data and the degree to which the volcanic history is known. The construction of such event trees additionally relies upon existence and use of relevant global databases and documented past periods of unrest. Because relevant global databases may be underpopulated or nonexistent, uncertainty in probability estimations may be large. Our 'hybrid' approach of combining local and global monitoring data and expert judgment facilitates discussion and constructive debate between disciplines: including seismology, gas geochemistry, geodesy, petrology, physical volcanology and technology/engineering, where difference in opinion between response team members contributes to definition of the uncertainty in the probability estimations. In collaboration with foreign colleagues, we have created event trees for numerous areas experiencing volcanic unrest. Event trees are created for a specified time frame and are updated, revised, or replaced as the crisis proceeds. Creation of an initial tree is often prompted by a change in monitoring data, such that rapid assessment of probability is needed. These trees are intended as a vehicle for discussion and a way to document relevant data and models, where the target audience is the scientists themselves. However, the probabilities derived through the event-tree analysis can also be used to help inform communications with emergency managers and the public. VDAP trees evaluate probabilities of: magmatic intrusion, likelihood of eruption, magnitude of eruption, and types of associated hazardous events and their extents. In a few cases, trees have been extended to also assess and communicate vulnerability and relative risk.

What Is An Expert System? ERIC Digest.

ERIC Educational Resources Information Center

Boss, Richard W.

This digest describes and defines the various components of an expert system, e.g., a computerized tool designed to enhance the quality and availability of knowledge required by decision makers. It is noted that expert systems differ from conventional applications software in the following areas: (1) the existence of the expert systems shell, or…

Rhetorical Consequences of the Computer Society: Expert Systems and Human Communication.

ERIC Educational Resources Information Center

Skopec, Eric Wm.

Expert systems are computer programs that solve selected problems by modelling domain-specific behaviors of human experts. These computer programs typically consist of an input/output system that feeds data into the computer and retrieves advice, an inference system using the reasoning and heuristic processes of human experts, and a knowledge…

Expert Systems: A Challenge for the Reading Profession.

ERIC Educational Resources Information Center

Balajthy, Ernest

The expert systems are designed to imitate the reasoning of a human expert in a content area field. Designed to be advisors, these software systems combine the content area knowledge and decision-making ability of an expert with the user's understanding and knowledge of particular circumstances. The reading diagnosis system, the RD2P System…

Expert Systems: Implications for the Diagnosis and Treatment of Learning Disabilities.

ERIC Educational Resources Information Center

Hofmeister, Alan M.; Lubke, Margaret M.

1988-01-01

The article examines characteristics and present or potential applications of expert systems technology for diagnosis and treatment of learning disabilities. Preliminary findings indicate that expert systems can perform as well as humans in specific areas, and that the process of organizing knowledge bases for expert systems helps clarify existing…

Adaptive cyber-attack modeling system

NASA Astrophysics Data System (ADS)

Gonsalves, Paul G.; Dougherty, Edward T.

2006-05-01

The pervasiveness of software and networked information systems is evident across a broad spectrum of business and government sectors. Such reliance provides an ample opportunity not only for the nefarious exploits of lone wolf computer hackers, but for more systematic software attacks from organized entities. Much effort and focus has been placed on preventing and ameliorating network and OS attacks, a concomitant emphasis is required to address protection of mission critical software. Typical software protection technique and methodology evaluation and verification and validation (V&V) involves the use of a team of subject matter experts (SMEs) to mimic potential attackers or hackers. This manpower intensive, time-consuming, and potentially cost-prohibitive approach is not amenable to performing the necessary multiple non-subjective analyses required to support quantifying software protection levels. To facilitate the evaluation and V&V of software protection solutions, we have designed and developed a prototype adaptive cyber attack modeling system. Our approach integrates an off-line mechanism for rapid construction of Bayesian belief network (BN) attack models with an on-line model instantiation, adaptation and knowledge acquisition scheme. Off-line model construction is supported via a knowledge elicitation approach for identifying key domain requirements and a process for translating these requirements into a library of BN-based cyber-attack models. On-line attack modeling and knowledge acquisition is supported via BN evidence propagation and model parameter learning.

Probabilistic parameter estimation of activated sludge processes using Markov Chain Monte Carlo.

PubMed

Sharifi, Soroosh; Murthy, Sudhir; Takács, Imre; Massoudieh, Arash

2014-03-01

One of the most important challenges in making activated sludge models (ASMs) applicable to design problems is identifying the values of its many stoichiometric and kinetic parameters. When wastewater characteristics data from full-scale biological treatment systems are used for parameter estimation, several sources of uncertainty, including uncertainty in measured data, external forcing (e.g. influent characteristics), and model structural errors influence the value of the estimated parameters. This paper presents a Bayesian hierarchical modeling framework for the probabilistic estimation of activated sludge process parameters. The method provides the joint probability density functions (JPDFs) of stoichiometric and kinetic parameters by updating prior information regarding the parameters obtained from expert knowledge and literature. The method also provides the posterior correlations between the parameters, as well as a measure of sensitivity of the different constituents with respect to the parameters. This information can be used to design experiments to provide higher information content regarding certain parameters. The method is illustrated using the ASM1 model to describe synthetically generated data from a hypothetical biological treatment system. The results indicate that data from full-scale systems can narrow down the ranges of some parameters substantially whereas the amount of information they provide regarding other parameters is small, due to either large correlations between some of the parameters or a lack of sensitivity with respect to the parameters. Copyright © 2013 Elsevier Ltd. All rights reserved.

Design of an Ada expert system shell for the VHSIC avionic modular flight processor

NASA Technical Reports Server (NTRS)

Fanning, F. Jesse

1992-01-01

The Embedded Computer System Expert System Shell (ES Shell) is an Ada-based expert system shell developed at the Avionics Laboratory for use on the VHSIC Avionic Modular Processor (VAMP) running under the Ada Avionics Real-Time Software (AARTS) Operating System. The ES Shell provides the interface between the expert system and the avionics environment, and controls execution of the expert system. Testing of the ES Shell in the Avionics Laboratory's Integrated Test Bed (ITB) has demonstrated its ability to control a non-deterministic software application executing on the VAMP's which can control the ITB's real-time closed-loop aircraft simulation. The results of these tests and the conclusions reached in the design and development of the ES Shell have played an important role in the formulation of the requirements for a production-quality expert system inference engine, an ingredient necessary for the successful use of expert systems on the VAMP embedded avionic flight processor.

The need for a comprehensive expert system development methodology

NASA Technical Reports Server (NTRS)

Baumert, John; Critchfield, Anna; Leavitt, Karen

1988-01-01

In a traditional software development environment, the introduction of standardized approaches has led to higher quality, maintainable products on the technical side and greater visibility into the status of the effort on the management side. This study examined expert system development to determine whether it differed enough from traditional systems to warrant a reevaluation of current software development methodologies. Its purpose was to identify areas of similarity with traditional software development and areas requiring tailoring to the unique needs of expert systems. A second purpose was to determine whether existing expert system development methodologies meet the needs of expert system development, management, and maintenance personnel. The study consisted of a literature search and personal interviews. It was determined that existing methodologies and approaches to developing expert systems are not comprehensive nor are they easily applied, especially to cradle to grave system development. As a result, requirements were derived for an expert system development methodology and an initial annotated outline derived for such a methodology.

Little Bayesians or Little Einsteins? Probability and Explanatory Virtue in Children's Inferences

ERIC Educational Resources Information Center

Johnston, Angie M.; Johnson, Samuel G. B.; Koven, Marissa L.; Keil, Frank C.

2017-01-01

Like scientists, children seek ways to explain causal systems in the world. But are children scientists in the strict Bayesian tradition of maximizing posterior probability? Or do they attend to other explanatory considerations, as laypeople and scientists--such as Einstein--do? Four experiments support the latter possibility. In particular, we…

Using Bayesian Stable Isotope Mixing Models to Enhance Marine Ecosystem Models

EPA Science Inventory

The use of stable isotopes in food web studies has proven to be a valuable tool for ecologists. We investigated the use of Bayesian stable isotope mixing models as constraints for an ecosystem model of a temperate seagrass system on the Atlantic coast of France. δ13C and δ15N i...

Validation of an expert system intended for research in distributed artificial intelligence

NASA Technical Reports Server (NTRS)

Grossner, C.; Lyons, J.; Radhakrishnan, T.

1991-01-01

The expert system discussed in this paper is designed to function as a testbed for research on cooperating expert systems. Cooperating expert systems are members of an organization which dictates the manner in which the expert systems will interact when solving a problem. The Blackbox Expert described in this paper has been constructed using the C Language Integrated Production System (CLIPS), C++, and X windowing environment. CLIPS is embedded in a C++ program which provides objects that are used to maintain the state of the Blackbox puzzle. These objects are accessed by CLIPS rules through user-defined functions calls. The performance of the Blackbox Expert is validated by experimentation. A group of people are asked to solve a set of test cases for the Blackbox puzzle. A metric has been devised which evaluates the 'correctness' of a solution proposed for a test case of Blackbox. Using this metric and the solutions proposed by the humans, each person receives a rating for their ability to solve the Blackbox puzzle. The Blackbox Expert solves the same set of test cases and is assigned a rating for its ability. Then the rating obtained by the Blackbox Expert is compared with the ratings of the people, thus establishing the skill level of our expert system.

Photolithography diagnostic expert systems: a systematic approach to problem solving in a wafer fabrication facility

NASA Astrophysics Data System (ADS)

Weatherwax Scott, Caroline; Tsareff, Christopher R.

1990-06-01

One of the main goals of process engineering in the semiconductor industry is to improve wafer fabrication productivity and throughput. Engineers must work continuously toward this goal in addition to performing sustaining and development tasks. To accomplish these objectives, managers must make efficient use of engineering resources. One of the tools being used to improve efficiency is the diagnostic expert system. Expert systems are knowledge based computer programs designed to lead the user through the analysis and solution of a problem. Several photolithography diagnostic expert systems have been implemented at the Hughes Technology Center to provide a systematic approach to process problem solving. This systematic approach was achieved by documenting cause and effect analyses for a wide variety of processing problems. This knowledge was organized in the form of IF-THEN rules, a common structure for knowledge representation in expert system technology. These rules form the knowledge base of the expert system which is stored in the computer. The systems also include the problem solving methodology used by the expert when addressing a problem in his area of expertise. Operators now use the expert systems to solve many process problems without engineering assistance. The systems also facilitate the collection of appropriate data to assist engineering in solving unanticipated problems. Currently, several expert systems have been implemented to cover all aspects of the photolithography process. The systems, which have been in use for over a year, include wafer surface preparation (HMDS), photoresist coat and softbake, align and expose on a wafer stepper, and develop inspection. These systems are part of a plan to implement an expert system diagnostic environment throughout the wafer fabrication facility. In this paper, the systems' construction is described, including knowledge acquisition, rule construction, knowledge refinement, testing, and evaluation. The roles played by the process engineering expert and the knowledge engineer are discussed. The features of the systems are shown, particularly the interactive quality of the consultations and the ease of system use.

MOORE: A prototype expert system for diagnosing spacecraft problems

NASA Technical Reports Server (NTRS)

Howlin, Katherine; Weissert, Jerry; Krantz, Kerry

1988-01-01

MOORE is a rule-based, prototype expert system that assists in diagnosing operational Tracking and Data Relay Satellite (TDRS) problems. It is intended to assist spacecraft engineers at the TDRS ground terminal in trouble shooting problems that are not readily solved with routine procedures, and without expert counsel. An additional goal of the prototype system is to develop in-house expert system and knowledge engineering skills. The prototype system diagnoses antenna pointing and earth pointing problems that may occur within the TDRS Attitude Control System (ACS). Plans include expansion to fault isolation of problems in the most critical subsystems of the TDRS spacecraft. Long term benefits are anticipated with use of an expert system during future TDRS programs with increased mission support time, reduced problem solving time, and retained expert knowledge and experience. Phase 2 of the project is intended to provide NASA the necessary expertise and capability to define requirements, evaluate proposals, and monitor the development progress of a highly competent expert system for NASA's Tracking Data Relay Satellite. Phase 2 also envisions addressing two unexplored applications for expert systems, spacecraft integration and tests (I and T) and support to launch activities. The concept, goals, domain, tools, knowledge acquisition, developmental approach, and design of the expert system. It will explain how NASA obtained the knowledge and capability to develop the system in-house without assistance from outside consultants. Future plans will also be presented.

A Bayesian approach to meta-analysis of plant pathology studies.

PubMed

Mila, A L; Ngugi, H K

2011-01-01

Bayesian statistical methods are used for meta-analysis in many disciplines, including medicine, molecular biology, and engineering, but have not yet been applied for quantitative synthesis of plant pathology studies. In this paper, we illustrate the key concepts of Bayesian statistics and outline the differences between Bayesian and classical (frequentist) methods in the way parameters describing population attributes are considered. We then describe a Bayesian approach to meta-analysis and present a plant pathological example based on studies evaluating the efficacy of plant protection products that induce systemic acquired resistance for the management of fire blight of apple. In a simple random-effects model assuming a normal distribution of effect sizes and no prior information (i.e., a noninformative prior), the results of the Bayesian meta-analysis are similar to those obtained with classical methods. Implementing the same model with a Student's t distribution and a noninformative prior for the effect sizes, instead of a normal distribution, yields similar results for all but acibenzolar-S-methyl (Actigard) which was evaluated only in seven studies in this example. Whereas both the classical (P = 0.28) and the Bayesian analysis with a noninformative prior (95% credibility interval [CRI] for the log response ratio: -0.63 to 0.08) indicate a nonsignificant effect for Actigard, specifying a t distribution resulted in a significant, albeit variable, effect for this product (CRI: -0.73 to -0.10). These results confirm the sensitivity of the analytical outcome (i.e., the posterior distribution) to the choice of prior in Bayesian meta-analyses involving a limited number of studies. We review some pertinent literature on more advanced topics, including modeling of among-study heterogeneity, publication bias, analyses involving a limited number of studies, and methods for dealing with missing data, and show how these issues can be approached in a Bayesian framework. Bayesian meta-analysis can readily include information not easily incorporated in classical methods, and allow for a full evaluation of competing models. Given the power and flexibility of Bayesian methods, we expect them to become widely adopted for meta-analysis of plant pathology studies.

On the limitations of standard statistical modeling in biological systems: a full Bayesian approach for biology.

PubMed

Gomez-Ramirez, Jaime; Sanz, Ricardo

2013-09-01

One of the most important scientific challenges today is the quantitative and predictive understanding of biological function. Classical mathematical and computational approaches have been enormously successful in modeling inert matter, but they may be inadequate to address inherent features of biological systems. We address the conceptual and methodological obstacles that lie in the inverse problem in biological systems modeling. We introduce a full Bayesian approach (FBA), a theoretical framework to study biological function, in which probability distributions are conditional on biophysical information that physically resides in the biological system that is studied by the scientist. Copyright © 2013 Elsevier Ltd. All rights reserved.

Expert systems applied to spacecraft fire safety

NASA Technical Reports Server (NTRS)

Smith, Richard L.; Kashiwagi, Takashi

1989-01-01

Expert systems are problem-solving programs that combine a knowledge base and a reasoning mechanism to simulate a human expert. The development of an expert system to manage fire safety in spacecraft, in particular the NASA Space Station Freedom, is difficult but clearly advantageous in the long-term. Some needs in low-gravity flammability characteristics, ventilating-flow effects, fire detection, fire extinguishment, and decision models, all necessary to establish the knowledge base for an expert system, are discussed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

MacAllister, D.J.; Day, R.; McCormack, M.D.

This paper gives an overview of a major integrated oil company`s experience with artificial intelligence (AI) over the last 5 years, with an emphasis on expert systems. The authors chronicle the development of an AI group, including details on development tool selection, project selection strategies, potential pitfalls, and descriptions of several completed expert systems. Small expert systems produced by teams of petroleum technology experts and experienced expert system developers that are focused in well-defined technical areas have produced substantial benefits and accelerated petroleum technology transfer.

An expert systems approach to automated fault management in a regenerative life support subsystem

NASA Technical Reports Server (NTRS)

Malin, J. T.; Lance, N., Jr.

1986-01-01

This paper describes FIXER, a prototype expert system for automated fault management in a regenerative life support subsystem typical of Space Station applications. The development project provided an evaluation of the use of expert systems technology to enhance controller functions in space subsystems. The software development approach permitted evaluation of the effectiveness of direct involvement of the expert in design and development. The approach also permitted intensive observation of the knowledge and methods of the expert. This paper describes the development of the prototype expert system and presents results of the evaluation.

Bayesian analysis of rare events

DOE Office of Scientific and Technical Information (OSTI.GOV)

Straub, Daniel, E-mail: straub@tum.de; Papaioannou, Iason; Betz, Wolfgang

2016-06-01

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into themore » probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.« less

Bayesian Software Health Management for Aircraft Guidance, Navigation, and Control

NASA Technical Reports Server (NTRS)

Schumann, Johann; Mbaya, Timmy; Menghoel, Ole

2011-01-01

Modern aircraft, both piloted fly-by-wire commercial aircraft as well as UAVs, more and more depend on highly complex safety critical software systems with many sensors and computer-controlled actuators. Despite careful design and V&V of the software, severe incidents have happened due to malfunctioning software. In this paper, we discuss the use of Bayesian networks (BNs) to monitor the health of the on-board software and sensor system, and to perform advanced on-board diagnostic reasoning. We will focus on the approach to develop reliable and robust health models for the combined software and sensor systems.

Evolution of a research prototype expert system for endemic populations of mountain pine beetle in lodgepole pine forests

Treesearch

Dale L. Bartos; Kent B. Downing

1989-01-01

A knowledge acquisition program was written to aid in obtaining knowledge from the experts concerning endemic populations of mountain pine beetle in lodgepole pine forest. An application expert system is then automatically generated by the knowledge acquisition program that contains the codified base of expert knowledge. Data can then be entered into the expert system...

An SSME High Pressure Oxidizer Turbopump diagnostic system using G2 real-time expert system

NASA Technical Reports Server (NTRS)

Guo, Ten-Huei

1991-01-01

An expert system which diagnoses various seal leakage faults in the High Pressure Oxidizer Turbopump of the SSME was developed using G2 real-time expert system. Three major functions of the software were implemented: model-based data generation, real-time expert system reasoning, and real-time input/output communication. This system is proposed as one module of a complete diagnostic system for the SSME. Diagnosis of a fault is defined as the determination of its type, severity, and likelihood. Since fault diagnosis is often accomplished through the use of heuristic human knowledge, an expert system based approach has been adopted as a paradigm to develop this diagnostic system. To implement this approach, a software shell which can be easily programmed to emulate the human decision process, the G2 Real-Time Expert System, was selected. Lessons learned from this implementation are discussed.

An SSME high pressure oxidizer turbopump diagnostic system using G2(TM) real-time expert system

NASA Technical Reports Server (NTRS)

Guo, Ten-Huei

1991-01-01

An expert system which diagnoses various seal leakage faults in the High Pressure Oxidizer Turbopump of the SSME was developed using G2(TM) real-time expert system. Three major functions of the software were implemented: model-based data generation, real-time expert system reasoning, and real-time input/output communication. This system is proposed as one module of a complete diagnostic system for Space Shuttle Main Engine. Diagnosis of a fault is defined as the determination of its type, severity, and likelihood. Since fault diagnosis is often accomplished through the use of heuristic human knowledge, an expert system based approach was adopted as a paradigm to develop this diagnostic system. To implement this approach, a software shell which can be easily programmed to emulate the human decision process, the G2 Real-Time Expert System, was selected. Lessons learned from this implementation are discussed.

A Bayesian-based two-stage inexact optimization method for supporting stream water quality management in the Three Gorges Reservoir region.

PubMed

Hu, X H; Li, Y P; Huang, G H; Zhuang, X W; Ding, X W

2016-05-01

In this study, a Bayesian-based two-stage inexact optimization (BTIO) method is developed for supporting water quality management through coupling Bayesian analysis with interval two-stage stochastic programming (ITSP). The BTIO method is capable of addressing uncertainties caused by insufficient inputs in water quality model as well as uncertainties expressed as probabilistic distributions and interval numbers. The BTIO method is applied to a real case of water quality management for the Xiangxi River basin in the Three Gorges Reservoir region to seek optimal water quality management schemes under various uncertainties. Interval solutions for production patterns under a range of probabilistic water quality constraints have been generated. Results obtained demonstrate compromises between the system benefit and the system failure risk due to inherent uncertainties that exist in various system components. Moreover, information about pollutant emission is accomplished, which would help managers to adjust production patterns of regional industry and local policies considering interactions of water quality requirement, economic benefit, and industry structure.

Real-time prediction of acute cardiovascular events using hardware-implemented Bayesian networks.

PubMed

Tylman, Wojciech; Waszyrowski, Tomasz; Napieralski, Andrzej; Kamiński, Marek; Trafidło, Tamara; Kulesza, Zbigniew; Kotas, Rafał; Marciniak, Paweł; Tomala, Radosław; Wenerski, Maciej

2016-02-01

This paper presents a decision support system that aims to estimate a patient׳s general condition and detect situations which pose an immediate danger to the patient׳s health or life. The use of this system might be especially important in places such as accident and emergency departments or admission wards, where a small medical team has to take care of many patients in various general conditions. Particular stress is laid on cardiovascular and pulmonary conditions, including those leading to sudden cardiac arrest. The proposed system is a stand-alone microprocessor-based device that works in conjunction with a standard vital signs monitor, which provides input signals such as temperature, blood pressure, pulseoxymetry, ECG, and ICG. The signals are preprocessed and analysed by a set of artificial intelligence algorithms, the core of which is based on Bayesian networks. The paper focuses on the construction and evaluation of the Bayesian network, both its structure and numerical specification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Bayesian inference of interaction properties of noisy dynamical systems with time-varying coupling: capabilities and limitations

NASA Astrophysics Data System (ADS)

Wilting, Jens; Lehnertz, Klaus

2015-08-01

We investigate a recently published analysis framework based on Bayesian inference for the time-resolved characterization of interaction properties of noisy, coupled dynamical systems. It promises wide applicability and a better time resolution than well-established methods. At the example of representative model systems, we show that the analysis framework has the same weaknesses as previous methods, particularly when investigating interacting, structurally different non-linear oscillators. We also inspect the tracking of time-varying interaction properties and propose a further modification of the algorithm, which improves the reliability of obtained results. We exemplarily investigate the suitability of this algorithm to infer strength and direction of interactions between various regions of the human brain during an epileptic seizure. Within the limitations of the applicability of this analysis tool, we show that the modified algorithm indeed allows a better time resolution through Bayesian inference when compared to previous methods based on least square fits.

Operation and Structure of an Artificial Intelligence Expert Consultative System for Reading and Learning.

ERIC Educational Resources Information Center

Balajthy, Ernest

1989-01-01

The article examines decision-making expert systems and discusses their implications for diagnosis and prescription of reading difficulties. A detailed description of how a reading diagnostic expert system might operate to aid classroom teachers is followed by a discussion of advantages and limitations of expert systems for educational use.…

EXSPRT: An Expert Systems Approach to Computer-Based Adaptive Testing.

ERIC Educational Resources Information Center

Frick, Theodore W.; And Others

Expert systems can be used to aid decision making. A computerized adaptive test (CAT) is one kind of expert system, although it is not commonly recognized as such. A new approach, termed EXSPRT, was devised that combines expert systems reasoning and sequential probability ratio test stopping rules. EXSPRT-R uses random selection of test items,…

Expert database system for quality control

NASA Astrophysics Data System (ADS)

Wang, Anne J.; Li, Zhi-Cheng

1993-09-01

There are more competitors today. Markets are not homogeneous they are fragmented into increasingly focused niches requiring greater flexibility in the product mix shorter manufacturing production runs and above allhigher quality. In this paper the author identified a real-time expert system as a way to improve plantwide quality management. The quality control expert database system (QCEDS) by integrating knowledge of experts in operations quality management and computer systems use all information relevant to quality managementfacts as well as rulesto determine if a product meets quality standards. Keywords: expert system quality control data base

Parallel processing and expert systems

NASA Technical Reports Server (NTRS)

Yan, Jerry C.; Lau, Sonie

1991-01-01

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 90's cannot enjoy an increased level of autonomy without the efficient use of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real time demands are met for large expert systems. Speed-up via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial labs in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems was surveyed. The survey is divided into three major sections: (1) multiprocessors for parallel expert systems; (2) parallel languages for symbolic computations; and (3) measurements of parallelism of expert system. Results to date indicate that the parallelism achieved for these systems is small. In order to obtain greater speed-ups, data parallelism and application parallelism must be exploited.

Fault isolation detection expert (FIDEX). Part 1: Expert system diagnostics for a 30/20 Gigahertz satellite transponder

NASA Technical Reports Server (NTRS)

Durkin, John; Schlegelmilch, Richard; Tallo, Donald

1992-01-01

LeRC has recently completed the design of a Ka-band satellite transponder system, as part of the Advanced Communication Technology Satellite (ACTS) System. To enhance the reliability of this satellite, NASA funded the University of Akron to explore the application of an expert system to provide the transponder with an autonomous diagnosis capability. The results of this research was the development of a prototype diagnosis expert system called FIDEX (fault-isolation and diagnosis expert). FIDEX is a frame-based expert system that was developed in the NEXPERT Object development environment by Neuron Data, Inc. It is a MicroSoft Windows version 3.0 application, and was designed to operate on an Intel i80386 based personal computer system.

Ethical Expert Systems

PubMed Central

Victoroff, Michael S.

1985-01-01

The title is a double entendre. The discussion approaches expert systems from two directions: “What ethical hazards are created by expert systems in medicine?” and “Would it be ethical to design an expert system for solving problems in bioethics?” Computers present new ethical problems to society, some of which are unprecedented. These can be categorized under several rubrics. The paper describes a rudimentary scheme for understanding ethical issues raised by computers, in general, and medical expert systems, in particular. It focuses on bioethical implications of AI in medicine; explores norms, assumptions and taboos; and highlights certain ethical pitfalls. Principles are elucidated, for building ethically sound systems. Finally, a proposal is discussed, for the design of an expert system for moral problem solving, and the ethical implications of this notion are analyzed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miller, M.; Kempner, L. Jr.; Mueller, W. III

The concept of an Expert System is not new. It has been around since the days of the early computers when scientists had dreams of robot automation to do everything from washing windows to automobile design. This paper discusses an application of an expert system and addresses software development issues and various levels of expert system development form a structural engineering viewpoint. An expert system designed to aid the structural engineer in first order inelastic analysis of latticed steel transmission powers is presented. The utilization of expert systems with large numerical analysis programs is discussed along with the software developmentmore » of such a system.« less

CLEAR: Communications Link Expert Assistance Resource

NASA Technical Reports Server (NTRS)

Hull, Larry G.; Hughes, Peter M.

1987-01-01

Communications Link Expert Assistance Resource (CLEAR) is a real time, fault diagnosis expert system for the Cosmic Background Explorer (COBE) Mission Operations Room (MOR). The CLEAR expert system is an operational prototype which assists the MOR operator/analyst by isolating and diagnosing faults in the spacecraft communication link with the Tracking and Data Relay Satellite (TDRS) during periods of realtime data acquisition. The mission domain, user requirements, hardware configuration, expert system concept, tool selection, development approach, and system design were discussed. Development approach and system implementation are emphasized. Also discussed are system architecture, tool selection, operation, and future plans.

Design and implementation of a status at a glance user interface for a power distribution expert system

NASA Technical Reports Server (NTRS)

Liberman, Eugene M.; Manner, David B.; Dolce, James L.; Mellor, Pamela A.

1993-01-01

Expert systems are widely used in health monitoring and fault detection applications. One of the key features of an expert system is that it possesses a large body of knowledge about the application for which it was designed. When the user consults this knowledge base, it is essential that the expert system's reasoning process and its conclusions be as concise as possible. If, in addition, an expert system is part of a process monitoring system, the expert system's conclusions must be combined with current events of the process. Under these circumstances, it is difficult for a user to absorb and respond to all the available information. For example, a user can become distracted and confused if two or more unrelated devices in different parts of the system require attention. A human interface designed to integrate expert system diagnoses with process data and to focus the user's attention to the important matters provides a solution to the 'information overload' problem. This paper will discuss a user interface to the power distribution expert system for Space Station Freedom. The importance of features which simplify assessing system status and which minimize navigating through layers of information will be discussed. Design rationale and implementation choices will also be presented.

Developing a Web-Based Advisory Expert System for Implementing Traffic Calming Strategies

PubMed Central

Falamarzi, Amir; Borhan, Muhamad Nazri; Rahmat, Riza Atiq O. K.

2014-01-01

Lack of traffic safety has become a serious issue in residential areas. In this paper, a web-based advisory expert system for the purpose of applying traffic calming strategies on residential streets is described because there currently lacks a structured framework for the implementation of such strategies. Developing an expert system can assist and advise engineers for dealing with traffic safety problems. This expert system is developed to fill the gap between the traffic safety experts and people who seek to employ traffic calming strategies including decision makers, engineers, and students. In order to build the expert system, examining sources related to traffic calming studies as well as interviewing with domain experts have been carried out. The system includes above 150 rules and 200 images for different types of measures. The system has three main functions including classifying traffic calming measures, prioritizing traffic calming strategies, and presenting solutions for different traffic safety problems. Verifying, validating processes, and comparing the system with similar works have shown that the system is consistent and acceptable for practical uses. Finally, some recommendations for improving the system are presented. PMID:25276861

Developing a web-based advisory expert system for implementing traffic calming strategies.

PubMed

Falamarzi, Amir; Borhan, Muhamad Nazri; Rahmat, Riza Atiq O K

2014-01-01

Lack of traffic safety has become a serious issue in residential areas. In this paper, a web-based advisory expert system for the purpose of applying traffic calming strategies on residential streets is described because there currently lacks a structured framework for the implementation of such strategies. Developing an expert system can assist and advise engineers for dealing with traffic safety problems. This expert system is developed to fill the gap between the traffic safety experts and people who seek to employ traffic calming strategies including decision makers, engineers, and students. In order to build the expert system, examining sources related to traffic calming studies as well as interviewing with domain experts have been carried out. The system includes above 150 rules and 200 images for different types of measures. The system has three main functions including classifying traffic calming measures, prioritizing traffic calming strategies, and presenting solutions for different traffic safety problems. Verifying, validating processes, and comparing the system with similar works have shown that the system is consistent and acceptable for practical uses. Finally, some recommendations for improving the system are presented.

Executive system software design and expert system implementation

NASA Technical Reports Server (NTRS)

Allen, Cheryl L.

1992-01-01

The topics are presented in viewgraph form and include: software requirements; design layout of the automated assembly system; menu display for automated composite command; expert system features; complete robot arm state diagram and logic; and expert system benefits.

Expert Systems in Reference Services.

ERIC Educational Resources Information Center

Roysdon, Christine, Ed.; White, Howard D., Ed.

1989-01-01

Eleven articles introduce expert systems applications in library and information science, and present design and implementation issues of system development for reference services. Topics covered include knowledge based systems, prototype development, the use of artificial intelligence to remedy current system inadequacies, and an expert system to…

A demonstration of expert systems applications in transportation engineering : volume III, evaluation of the prototype expert system TRANZ.

DOT National Transportation Integrated Search

1990-01-01

The validation and evaluation of an expert system for traffic control in highway work zones (TRANZ) is described. The stages in the evaluation process consisted of the following: revisit the experts, selectively distribute copies of TRANZ with docume...

Improving default risk prediction using Bayesian model uncertainty techniques.

PubMed

Kazemi, Reza; Mosleh, Ali

2012-11-01

Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from "nominal predictions" due to "upsetting events" such as the 2008 global banking crisis. © 2012 Society for Risk Analysis.

Hazard Screening Methods for Nanomaterials: A Comparative Study

PubMed Central

Murphy, Finbarr; Mullins, Martin; Furxhi, Irini; Costa, Anna L.; Simeone, Felice C.

2018-01-01

Hazard identification is the key step in risk assessment and management of manufactured nanomaterials (NM). However, the rapid commercialisation of nano-enabled products continues to out-pace the development of a prudent risk management mechanism that is widely accepted by the scientific community and enforced by regulators. However, a growing body of academic literature is developing promising quantitative methods. Two approaches have gained significant currency. Bayesian networks (BN) are a probabilistic, machine learning approach while the weight of evidence (WoE) statistical framework is based on expert elicitation. This comparative study investigates the efficacy of quantitative WoE and Bayesian methodologies in ranking the potential hazard of metal and metal-oxide NMs—TiO2, Ag, and ZnO. This research finds that hazard ranking is consistent for both risk assessment approaches. The BN and WoE models both utilize physico-chemical, toxicological, and study type data to infer the hazard potential. The BN exhibits more stability when the models are perturbed with new data. The BN has the significant advantage of self-learning with new data; however, this assumes all input data is equally valid. This research finds that a combination of WoE that would rank input data along with the BN is the optimal hazard assessment framework. PMID:29495342

Short-term volcanic hazard assessment through Bayesian inference: retrospective application to the Pinatubo 1991 volcanic crisis

NASA Astrophysics Data System (ADS)

Sobradelo, Rosa; Martí, Joan

2015-01-01

One of the most challenging aspects of managing a volcanic crisis is the interpretation of the monitoring data, so as to anticipate to the evolution of the unrest and implement timely mitigation actions. An unrest episode may include different stages or time intervals of increasing activity that may or may not precede a volcanic eruption, depending on the causes of the unrest (magmatic, geothermal or tectonic). Therefore, one of the main goals in monitoring volcanic unrest is to forecast whether or not such increase of activity will end up with an eruption, and if this is the case, how, when, and where this eruption will take place. As an alternative method to expert elicitation for assessing and merging monitoring data and relevant past information, we present a probabilistic method to transform precursory activity into the probability of experiencing a significant variation by the next time interval (i.e. the next step in the unrest), given its preceding evolution, and by further estimating the probability of the occurrence of a particular eruptive scenario combining monitoring and past data. With the 1991 Pinatubo volcanic crisis as a reference, we have developed such a method to assess short-term volcanic hazard using Bayesian inference.

Development and practice of a Telehealthcare Expert System (TES).

PubMed

Lin, Hanjun; Hsu, Yeh-Liang; Hsu, Ming-Shinn; Cheng, Chih-Ming

2013-07-01

Expert systems have been widely used in medical and healthcare practice for various purposes. In addition to vital sign data, important concerns in telehealthcare include the compliance with the measurement prescription, the accuracy of vital sign measurements, and the functioning of vital sign meters and home gateways. However, few expert system applications are found in the telehealthcare domain to address these issues. This article presents an expert system application for one of the largest commercialized telehealthcare practices in Taiwan by Min-Sheng General Hospital. The main function of the Telehealthcare Expert System (TES) developed in this research is to detect and classify events based on the measurement data transmitted to the database at the call center, including abnormality of vital signs, violation of vital sign measurement prescriptions, and malfunction of hardware devices (home gateway and vital sign meter). When the expert system detects an abnormal event, it assigns an "urgent degree" and alerts the nursing team in the call center to take action, such as phoning the patient for counseling or to urge the patient to return to the hospital for further tests. During 2 years of clinical practice, from 2009 to 2011, 19,182 patients were served by the expert system. The expert system detected 41,755 events, of which 22.9% indicated abnormality of vital signs, 75.2% indicated violation of measurement prescription, and 1.9% indicated malfunction of devices. On average, the expert system reduced by 76.5% the time that the nursing team in the call center spent in handling the events. The expert system helped to reduce cost and improve quality of the telehealthcare service.

An SQL query generator for CLIPS

NASA Technical Reports Server (NTRS)

Snyder, James; Chirica, Laurian

1990-01-01

As expert systems become more widely used, their access to large amounts of external information becomes increasingly important. This information exists in several forms such as statistical, tabular data, knowledge gained by experts and large databases of information maintained by companies. Because many expert systems, including CLIPS, do not provide access to this external information, much of the usefulness of expert systems is left untapped. The scope of this paper is to describe a database extension for the CLIPS expert system shell. The current industry standard database language is SQL. Due to SQL standardization, large amounts of information stored on various computers, potentially at different locations, will be more easily accessible. Expert systems should be able to directly access these existing databases rather than requiring information to be re-entered into the expert system environment. The ORACLE relational database management system (RDBMS) was used to provide a database connection within the CLIPS environment. To facilitate relational database access a query generation system was developed as a CLIPS user function. The queries are entered in a CLlPS-like syntax and are passed to the query generator, which constructs and submits for execution, an SQL query to the ORACLE RDBMS. The query results are asserted as CLIPS facts. The query generator was developed primarily for use within the ICADS project (Intelligent Computer Aided Design System) currently being developed by the CAD Research Unit in the California Polytechnic State University (Cal Poly). In ICADS, there are several parallel or distributed expert systems accessing a common knowledge base of facts. Expert system has a narrow domain of interest and therefore needs only certain portions of the information. The query generator provides a common method of accessing this information and allows the expert system to specify what data is needed without specifying how to retrieve it.

Cataloging Expert Systems: Optimism and Frustrated Reality.

ERIC Educational Resources Information Center

Olmstadt, William J.

2000-01-01

Discusses artificial intelligence and attempts to catalog expert systems. Topics include the nature of expertise; examples of cataloging expert systems; barriers to implementation; and problems, including total automation, cataloging expertise, priorities, and system design. (LRW)

Development of an instructional expert system for hole drilling processes

NASA Technical Reports Server (NTRS)

Al-Mutawa, Souhaila; Srinivas, Vijay; Moon, Young Bai

1990-01-01

An expert system which captures the expertise of workshop technicians in the drilling domain was developed. The expert system is aimed at novice technicians who know how to operate the machines but have not acquired the decision making skills that are gained with experience. This paper describes the domain background and the stages of development of the expert system.

Perception as Evidence Accumulation and Bayesian Inference: Insights from Masked Priming

ERIC Educational Resources Information Center

Norris, Dennis; Kinoshita, Sachiko

2008-01-01

The authors argue that perception is Bayesian inference based on accumulation of noisy evidence and that, in masked priming, the perceptual system is tricked into treating the prime and the target as a single object. Of the 2 algorithms considered for formalizing how the evidence sampled from a prime and target is combined, only 1 was shown to be…

The Approximate Bayesian Computation methods in the localization of the atmospheric contamination source

NASA Astrophysics Data System (ADS)

Kopka, P.; Wawrzynczak, A.; Borysiewicz, M.

2015-09-01

In many areas of application, a central problem is a solution to the inverse problem, especially estimation of the unknown model parameters to model the underlying dynamics of a physical system precisely. In this situation, the Bayesian inference is a powerful tool to combine observed data with prior knowledge to gain the probability distribution of searched parameters. We have applied the modern methodology named Sequential Approximate Bayesian Computation (S-ABC) to the problem of tracing the atmospheric contaminant source. The ABC is technique commonly used in the Bayesian analysis of complex models and dynamic system. Sequential methods can significantly increase the efficiency of the ABC. In the presented algorithm, the input data are the on-line arriving concentrations of released substance registered by distributed sensor network from OVER-LAND ATMOSPHERIC DISPERSION (OLAD) experiment. The algorithm output are the probability distributions of a contamination source parameters i.e. its particular location, release rate, speed and direction of the movement, start time and duration. The stochastic approach presented in this paper is completely general and can be used in other fields where the parameters of the model bet fitted to the observable data should be found.

A parallel expert system for the control of a robotic air vehicle

NASA Technical Reports Server (NTRS)

Shakley, Donald; Lamont, Gary B.

1988-01-01

Expert systems can be used to govern the intelligent control of vehicles, for example the Robotic Air Vehicle (RAV). Due to the nature of the RAV system the associated expert system needs to perform in a demanding real-time environment. The use of a parallel processing capability to support the associated expert system's computational requirement is critical in this application. Thus, algorithms for parallel real-time expert systems must be designed, analyzed, and synthesized. The design process incorporates a consideration of the rule-set/face-set size along with representation issues. These issues are looked at in reference to information movement and various inference mechanisms. Also examined is the process involved with transporting the RAV expert system functions from the TI Explorer, where they are implemented in the Automated Reasoning Tool (ART), to the iPSC Hypercube, where the system is synthesized using Concurrent Common LISP (CCLISP). The transformation process for the ART to CCLISP conversion is described. The performance characteristics of the parallel implementation of these expert systems on the iPSC Hypercube are compared to the TI Explorer implementation.

Innovation on Energy Power Technology (22)Challenge to Development of Expert System stored Knowledge of Expert Power Network Operators

NASA Astrophysics Data System (ADS)

Sakaguchi, Hideharu

Do you remember an expert system? I think there are various impressions about the system. For example, some might say “It reminds me of old days”. On the other hand, some might say “It was really troublesome”. About 25 years ago, from late 1980s to the middle of 1990s, when the Showa era was about to change into the Heisei Era, artificial intelligence boomed. Research and development for an expert system which was equipped with expertise and worked as smart as expert, was advanced in various fields. Our company also picked up the system as the new system which covered weak point of conventional computer technology. We started research and development in 1984, and installed an expert system in a SCADA system, which started operating in March 1990 in the Fukuoka Integrated Control Center. In this essay, as an electric power engineer who involved in development at that time, I introduce the situation and travail story about developing an expert system which support restorative actions from the outage and overload condition of power networks.

Cooperating Expert Systems For Space Station Power Distribution Management

NASA Astrophysics Data System (ADS)

Nguyen, T. A.; Chiou, W. C.

1987-02-01

In a complex system such as the manned Space Station, it is deem necessary that many expert systems must perform tasks in a concurrent and cooperative manner. An important question arise is: what cooperative-task-performing models are appropriate for multiple expert systems to jointly perform tasks. The solution to this question will provide a crucial automation design criteria for the Space Station complex systems architecture. Based on a client/server model for performing tasks, we have developed a system that acts as a front-end to support loosely-coupled communications between expert systems running on multiple Symbolics machines. As an example, we use two ART*-based expert systems to demonstrate the concept of parallel symbolic manipulation for power distribution management and dynamic load planner/scheduler in the simulated Space Station environment. This on-going work will also explore other cooperative-task-performing models as alternatives which can evaluate inter and intra expert system communication mechanisms. It will be served as a testbed and a bench-marking tool for other Space Station expert subsystem communication and information exchange.

Closed-loop control for cardiopulmonary management and intensive care unit sedation using digital imaging

NASA Astrophysics Data System (ADS)

Gholami, Behnood

This dissertation introduces a new problem in the delivery of healthcare, which could result in lower cost and a higher quality of medical care as compared to the current healthcare practice. In particular, a framework is developed for sedation and cardiopulmonary management for patients in the intensive care unit. A method is introduced to automatically detect pain and agitation in nonverbal patients, specifically in sedated patients in the intensive care unit, using their facial expressions. Furthermore, deterministic as well as probabilistic expert systems are developed to suggest the appropriate drug dose based on patient sedation level. Patients in the intensive care unit who require mechanical ventilation due to acute respiratory failure also frequently require the administration of sedative agents. The need for sedation arises both from patient anxiety due to the loss of personal control and the unfamiliar and intrusive environment of the intensive care unit, and also due to pain or other variants of noxious stimuli. In this dissertation, we develop a rule-based expert system for cardiopulmonary management and intensive care unit sedation. Furthermore, we use probability theory to quantify uncertainty and to extend the proposed rule-based expert system to deal with more realistic situations. Pain assessment in patients who are unable to verbally communicate is a challenging problem. The fundamental limitations in pain assessment stem from subjective assessment criteria, rather than quantifiable, measurable data. The relevance vector machine (RVM) classification technique is a Bayesian extension of the support vector machine (SVM) algorithm which achieves comparable performance to SVM while providing posterior probabilities for class memberships and a sparser model. In this dissertation, we use the RVM classification technique to distinguish pain from non-pain as well as assess pain intensity levels. We also correlate our results with the pain intensity assessed by expert and non-expert human examiners. Next, we consider facial expression recognition using an unsupervised learning framework. We show that different facial expressions reside on distinct subspaces if the manifold is unfolded. In particular, semi-definite embedding is used to reduce the dimensionality and unfold the manifold of facial images. Next, generalized principal component analysis is used to fit a series of subspaces to the data points and associate each data point to a subspace. Data points that belong to the same subspace are shown to belong to the same facial expression. In clinical intensive care unit practice sedative/analgesic agents are titrated to achieve a specific level of sedation. The level of sedation is currently based on clinical scoring systems. Examples include the motor activity assessment scale (MAAS), the Richmond agitation-sedation scale (RASS), and the modified Ramsay sedation scale (MRSS). In general, the goal of the clinician is to find the drug dose that maintains the patient at a sedation score corresponding to a moderately sedated state. In this research, we use pharmacokinetic and pharmacodynamic modeling to find an optimal drug dosing control policy to drive the patient to a desired MRSS score. Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. One treatment, referred to as catheter ablation, targets specific parts of the left atrium for radio frequency ablation using an intracardiac catheter. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images. (Abstract shortened by UMI.)

A software engineering approach to expert system design and verification

NASA Technical Reports Server (NTRS)

Bochsler, Daniel C.; Goodwin, Mary Ann

1988-01-01

Software engineering design and verification methods for developing expert systems are not yet well defined. Integration of expert system technology into software production environments will require effective software engineering methodologies to support the entire life cycle of expert systems. The software engineering methods used to design and verify an expert system, RENEX, is discussed. RENEX demonstrates autonomous rendezvous and proximity operations, including replanning trajectory events and subsystem fault detection, onboard a space vehicle during flight. The RENEX designers utilized a number of software engineering methodologies to deal with the complex problems inherent in this system. An overview is presented of the methods utilized. Details of the verification process receive special emphasis. The benefits and weaknesses of the methods for supporting the development life cycle of expert systems are evaluated, and recommendations are made based on the overall experiences with the methods.

Development of an expert system prototype for determining software functional requirements for command management activities at NASA Goddard

NASA Technical Reports Server (NTRS)

Liebowitz, J.

1986-01-01

The development of an expert system prototype for software functional requirement determination for NASA Goddard's Command Management System, as part of its process of transforming general requests into specific near-earth satellite commands, is described. The present knowledge base was formulated through interactions with domain experts, and was then linked to the existing Knowledge Engineering Systems (KES) expert system application generator. Steps in the knowledge-base development include problem-oriented attribute hierarchy development, knowledge management approach determination, and knowledge base encoding. The KES Parser and Inspector, in addition to backcasting and analogical mapping, were used to validate the expert system-derived requirements for one of the major functions of a spacecraft, the solar Maximum Mission. Knowledge refinement, evaluation, and implementation procedures of the expert system were then accomplished.

Third CLIPS Conference Proceedings, volume 1

NASA Technical Reports Server (NTRS)

Riley, Gary (Editor)

1994-01-01

Expert systems are computed programs which emulate human expertise in well defined problem domains. The potential payoff from expert systems is high: valuable expertise can be captured and preserved, repetitive and/or mundane tasks requiring human expertise can be automated, and uniformity can be applied in decision making processes. The C Language Integrated Production Systems (CLIPS) is an expert system building tool, developed at the Johnson Space Center, which provides a complete environment for the development and delivery of rule and/or object based expert systems. CLIPS was specifically designed to provide a low cost option for developing and deploying expert system applications across a wide range of hardware platforms. The development of CLIPS has helped to improve the ability to deliver expert systems technology throughout the public and private sectors for a wide range of applications and diverse computing environments.

CLIPS: An expert system building tool

NASA Technical Reports Server (NTRS)

Riley, Gary

1991-01-01

The C Language Integrated Production System (CLIPS) is an expert system building tool, which provides a complete environment for the development and delivery of rule and/or object based expert systems. CLIPS was specifically designed to provide a low cost option for developing and deploying expert system applications across a wide range of hardware platforms. The commercial potential of CLIPS is vast. Currently, CLIPS is being used by over 3,300 individuals throughout the public and private sector. Because the CLIPS source code is readily available, numerous groups have used CLIPS as a basis for their own expert system tools. To date, three commercially available tools have been derived from CLIPS. In general, the development of CLIPS has helped to improve the ability to deliver expert system technology throughout the public and private sectors for a wide range of applications and diverse computing environments.

System of experts for intelligent data management (SEIDAM)

NASA Technical Reports Server (NTRS)

Goodenough, David G.; Iisaka, Joji; Fung, KO

1993-01-01

A proposal to conduct research and development on a system of expert systems for intelligent data management (SEIDAM) is being developed. CCRS has much expertise in developing systems for integrating geographic information with space and aircraft remote sensing data and in managing large archives of remotely sensed data. SEIDAM will be composed of expert systems grouped in three levels. At the lowest level, the expert systems will manage and integrate data from diverse sources, taking account of symbolic representation differences and varying accuracies. Existing software can be controlled by these expert systems, without rewriting existing software into an Artificial Intelligence (AI) language. At the second level, SEIDAM will take the interpreted data (symbolic and numerical) and combine these with data models. at the top level, SEIDAM will respond to user goals for predictive outcomes given existing data. The SEIDAM Project will address the research areas of expert systems, data management, storage and retrieval, and user access and interfaces.

System of Experts for Intelligent Data Management (SEIDAM)

NASA Technical Reports Server (NTRS)

Goodenough, David G.; Iisaka, Joji; Fung, KO

1992-01-01

It is proposed to conduct research and development on a system of expert systems for intelligent data management (SEIDAM). CCRS has much expertise in developing systems for integrating geographic information with space and aircraft remote sensing data and in managing large archives of remotely sensed data. SEIDAM will be composed of expert systems grouped in three levels. At the lowest level, the expert systems will manage and integrate data from diverse sources, taking account of symbolic representation differences and varying accuracies. Existing software can be controlled by these expert systems, without rewriting existing software into an Artificial Intelligence (AI) language. At the second level, SEIDAM will take the interpreted data (symbolic and numerical) and combine these with data models. At the top level, SEIDAM will respond to user goals for predictive outcomes given existing data. The SEIDAM Project will address the research areas of expert systems, data management, storage and retrieval, and user access and interfaces.

TES: A modular systems approach to expert system development for real-time space applications

NASA Technical Reports Server (NTRS)

Cacace, Ralph; England, Brenda

1988-01-01

A major goal of the Space Station era is to reduce reliance on support from ground based experts. The development of software programs using expert systems technology is one means of reaching this goal without requiring crew members to become intimately familiar with the many complex spacecraft subsystems. Development of an expert systems program requires a validation of the software with actual flight hardware. By combining accurate hardware and software modelling techniques with a modular systems approach to expert systems development, the validation of these software programs can be successfully completed with minimum risk and effort. The TIMES Expert System (TES) is an application that monitors and evaluates real time data to perform fault detection and fault isolation tasks as they would otherwise be carried out by a knowledgeable designer. The development process and primary features of TES, a modular systems approach, and the lessons learned are discussed.

Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques

NASA Technical Reports Server (NTRS)

Saha, Bhaskar; Goebel, kai

2007-01-01

Uncertainty management has always been the key hurdle faced by diagnostics and prognostics algorithms. A Bayesian treatment of this problem provides an elegant and theoretically sound approach to the modern Condition- Based Maintenance (CBM)/Prognostic Health Management (PHM) paradigm. The application of the Bayesian techniques to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation as in Particle Filters (PF), provides a powerful tool to integrate the diagnosis and prognosis of battery health. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for model identification, while the PF framework uses the learnt model, statistical estimates of noise and anticipated operational conditions to provide estimates of remaining useful life (RUL) in the form of a probability density function (PDF). This type of prognostics generates a significant value addition to the management of any operation involving electrical systems.

Clinical and pharmacogenomic data mining: 1. Generalized theory of expected information and application to the development of tools.

PubMed

Robson, Barry

2003-01-01

New scientific problems, arising from the human genome project, are challenging the classical means of using statistics. Yet quantified knowledge in the form of rules and rule strengths based on real relationships in data, as opposed to expert opinion, is urgently required for researcher and physician decision support. The problem is that with many parameters, the space to be analyzed is highly dimensional. That is, the combinations of data to examine are subject to a combinatorial explosion as the number of possible events (entries, items, sub-records) (a),(b),(c),... per record (a,b,c,..) increases, and hence much of the space is sparsely populated. These combinatorial considerations are particularly problematic for identifying those associations called "Unicorn Events" which occur significantly less than expected to the extent that they are never seen to be counted. To cope with the combinatorial explosion, a novel numerical "book keeping" approach is taken to generate information terms relating to the combinatorial subsets of events (a,b,c,..), and, most importantly, the zeta (Zeta) function is employed. The incomplete Zeta function zeta(s,n) with s = 1, in which frequencies of occurrence such as n = n(a,b,c,...) determine the range of summation n, is argued to be the natural choice of information function. It emerges from Bayesian integration, taken over the distribution of possible values of information measures for sparse and ample data alike. Expected mutual information l(a;b;c) in nats (i.e., natural units analogous to bits but based on the natural logarithm), such as is available to the observer, is measured as e.g., the difference zeta(s,o(a,b,c..)) - zeta(s,e(a,b,c..)) where o(a,b,c,..) and e(a,b,c,..) are, or relate to, the observed and expected frequencies of occurrence, respectively. For real values of s > 1 the qualitative impact of strongly (positively or negatively) ranked data is preserved despite several numerical approximations. As real s increases, and the output of the information functions converge into three values +1, 0, and -1 nats representing a trinary logic system. For quantitative data, a useful ad hoc method, to report sigma-normalized covariations in an analogous manner to mutual information for significance comparison purposes, is demonstrated. Finally, the potential ability to make use of mutual information in a complex biomedical study, and to include Bayesian prior information derived from statistical, tabular, anecdotal, and expert opinion is briefly illustrated.

Statistical analysis of modal parameters of a suspension bridge based on Bayesian spectral density approach and SHM data

NASA Astrophysics Data System (ADS)

Li, Zhijun; Feng, Maria Q.; Luo, Longxi; Feng, Dongming; Xu, Xiuli

2018-01-01

Uncertainty of modal parameters estimation appear in structural health monitoring (SHM) practice of civil engineering to quite some significant extent due to environmental influences and modeling errors. Reasonable methodologies are needed for processing the uncertainty. Bayesian inference can provide a promising and feasible identification solution for the purpose of SHM. However, there are relatively few researches on the application of Bayesian spectral method in the modal identification using SHM data sets. To extract modal parameters from large data sets collected by SHM system, the Bayesian spectral density algorithm was applied to address the uncertainty of mode extraction from output-only response of a long-span suspension bridge. The posterior most possible values of modal parameters and their uncertainties were estimated through Bayesian inference. A long-term variation and statistical analysis was performed using the sensor data sets collected from the SHM system of the suspension bridge over a one-year period. The t location-scale distribution was shown to be a better candidate function for frequencies of lower modes. On the other hand, the burr distribution provided the best fitting to the higher modes which are sensitive to the temperature. In addition, wind-induced variation of modal parameters was also investigated. It was observed that both the damping ratios and modal forces increased during the period of typhoon excitations. Meanwhile, the modal damping ratios exhibit significant correlation with the spectral intensities of the corresponding modal forces.

Autonomously acquiring declarative and procedural knowledge for ICAT systems

NASA Technical Reports Server (NTRS)

Kovarik, Vincent J., Jr.

1993-01-01

The construction of Intelligent Computer Aided Training (ICAT) systems is critically dependent on the ability to define and encode knowledge. This knowledge engineering effort can be broadly divided into two categories: domain knowledge and expert or task knowledge. Domain knowledge refers to the physical environment or system with which the expert interacts. Expert knowledge consists of the set of procedures and heuristics employed by the expert in performing their task. Both these areas are a significant bottleneck in the acquisition of knowledge for ICAT systems. This paper presents a research project in the area of autonomous knowledge acquisition using a passive observation concept. The system observes an expert and then generalizes the observations into production rules representing the domain expert's knowledge.

Expert networks in CLIPS

NASA Technical Reports Server (NTRS)

Hruska, S. I.; Dalke, A.; Ferguson, J. J.; Lacher, R. C.

1991-01-01

Rule-based expert systems may be structurally and functionally mapped onto a special class of neural networks called expert networks. This mapping lends itself to adaptation of connectionist learning strategies for the expert networks. A parsing algorithm to translate C Language Integrated Production System (CLIPS) rules into a network of interconnected assertion and operation nodes has been developed. The translation of CLIPS rules to an expert network and back again is illustrated. Measures of uncertainty similar to those rules in MYCIN-like systems are introduced into the CLIPS system and techniques for combining and hiring nodes in the network based on rule-firing with these certainty factors in the expert system are presented. Several learning algorithms are under study which automate the process of attaching certainty factors to rules.

RAMBOT: A Connectionist Expert System That Learns by Example.

ERIC Educational Resources Information Center

Mozer, Michael C.

One solution to the problem of getting expert knowledge into expert systems would be to endow the systems with powerful learning procedures that could discover appropriate behaviors by observing an expert in action. A promising source of such learning procedures can be found in recent work on connectionist networks, which are massively parallel…

Model selection and parameter estimation in structural dynamics using approximate Bayesian computation

NASA Astrophysics Data System (ADS)

Ben Abdessalem, Anis; Dervilis, Nikolaos; Wagg, David; Worden, Keith

2018-01-01

This paper will introduce the use of the approximate Bayesian computation (ABC) algorithm for model selection and parameter estimation in structural dynamics. ABC is a likelihood-free method typically used when the likelihood function is either intractable or cannot be approached in a closed form. To circumvent the evaluation of the likelihood function, simulation from a forward model is at the core of the ABC algorithm. The algorithm offers the possibility to use different metrics and summary statistics representative of the data to carry out Bayesian inference. The efficacy of the algorithm in structural dynamics is demonstrated through three different illustrative examples of nonlinear system identification: cubic and cubic-quintic models, the Bouc-Wen model and the Duffing oscillator. The obtained results suggest that ABC is a promising alternative to deal with model selection and parameter estimation issues, specifically for systems with complex behaviours.

Bayesian sparse channel estimation

NASA Astrophysics Data System (ADS)

Chen, Chulong; Zoltowski, Michael D.

2012-05-01

In Orthogonal Frequency Division Multiplexing (OFDM) systems, the technique used to estimate and track the time-varying multipath channel is critical to ensure reliable, high data rate communications. It is recognized that wireless channels often exhibit a sparse structure, especially for wideband and ultra-wideband systems. In order to exploit this sparse structure to reduce the number of pilot tones and increase the channel estimation quality, the application of compressed sensing to channel estimation is proposed. In this article, to make the compressed channel estimation more feasible for practical applications, it is investigated from a perspective of Bayesian learning. Under the Bayesian learning framework, the large-scale compressed sensing problem, as well as large time delay for the estimation of the doubly selective channel over multiple consecutive OFDM symbols, can be avoided. Simulation studies show a significant improvement in channel estimation MSE and less computing time compared to the conventional compressed channel estimation techniques.

A new method for E-government procurement using collaborative filtering and Bayesian approach.

PubMed

Zhang, Shuai; Xi, Chengyu; Wang, Yan; Zhang, Wenyu; Chen, Yanhong

2013-01-01

Nowadays, as the Internet services increase faster than ever before, government systems are reinvented as E-government services. Therefore, government procurement sectors have to face challenges brought by the explosion of service information. This paper presents a novel method for E-government procurement (eGP) to search for the optimal procurement scheme (OPS). Item-based collaborative filtering and Bayesian approach are used to evaluate and select the candidate services to get the top-M recommendations such that the involved computation load can be alleviated. A trapezoidal fuzzy number similarity algorithm is applied to support the item-based collaborative filtering and Bayesian approach, since some of the services' attributes can be hardly expressed as certain and static values but only be easily represented as fuzzy values. A prototype system is built and validated with an illustrative example from eGP to confirm the feasibility of our approach.

A New Method for E-Government Procurement Using Collaborative Filtering and Bayesian Approach

PubMed Central

Wang, Yan

2013-01-01

Nowadays, as the Internet services increase faster than ever before, government systems are reinvented as E-government services. Therefore, government procurement sectors have to face challenges brought by the explosion of service information. This paper presents a novel method for E-government procurement (eGP) to search for the optimal procurement scheme (OPS). Item-based collaborative filtering and Bayesian approach are used to evaluate and select the candidate services to get the top-M recommendations such that the involved computation load can be alleviated. A trapezoidal fuzzy number similarity algorithm is applied to support the item-based collaborative filtering and Bayesian approach, since some of the services' attributes can be hardly expressed as certain and static values but only be easily represented as fuzzy values. A prototype system is built and validated with an illustrative example from eGP to confirm the feasibility of our approach. PMID:24385869

Tools and technologies for expert systems: A human factors perspective

NASA Technical Reports Server (NTRS)

Rajaram, Navaratna S.

1987-01-01

It is widely recognized that technologies based on artificial intelligence (AI), especially expert systems, can make significant contributions to the productivity and effectiveness of operations of information and knowledge intensive organizations such as NASA. At the same time, these being relatively new technologies, there is the problem of transfering technology to key personnel of such organizations. The problems of examining the potential of expert systems and of technology transfer is addressed in the context of human factors applications. One of the topics of interest was the investigation of the potential use of expert system building tools, particularly NEXPERT as a technology transfer medium. Two basic conclusions were reached in this regard. First, NEXPERT is an excellent tool for rapid prototyping of experimental expert systems, but not ideal as a delivery vehicle. Therefore, it is not a substitute for general purpose system implementation languages such a LISP or C. This assertion probably holds for nearly all such tools on the market today. Second, an effective technology transfer mechanism is to formulate and implement expert systems for problems which members of the organization in question can relate to. For this purpose, the LIghting EnGineering Expert (LIEGE) was implemented using NEXPERT as the tool for technology transfer and to illustrate the value of expert systems to the activities of the Man-System Division.

An Expert System Solution for the Quantitative Condition Assessment of Electrical Distribution Systems in the United States Air Force

DTIC Science & Technology

1991-09-01

Distribution system ... ......... 4 2. Architechture of an Expert system .. .............. 66 vi List of Tables Table Page 1. Prototype Component Model...expert system to properly process work requests Ln civil engineering (8:23). Electric Power Research Institute (EPRI). EPRI is a private organization ...used (51) Training Level. The level of training shop technicians receive, and the resulting proficiency, are important in all organizations . Experts 1

Research on an expert system for database operation of simulation-emulation math models. Volume 2, Phase 1: Results

NASA Technical Reports Server (NTRS)

Kawamura, K.; Beale, G. O.; Schaffer, J. D.; Hsieh, B. J.; Padalkar, S.; Rodriguez-Moscoso, J. J.

1985-01-01

A reference manual is provided for NESS, a simulation expert system. This manual gives user information regarding starting and operating NASA expert simulation system (NESS). This expert system provides an intelligent interface to a generic simulation program for spacecraft attitude control problems. A menu of the functions the system can perform is provided. Control repeated returns to this menu after executing each user request.

Development of a coupled expert system for the spacecraft attitude control problem

NASA Technical Reports Server (NTRS)

Kawamura, K.; Beale, G.; Schaffer, J.; Hsieh, B.-J.; Padalkar, S.; Rodriguezmoscoso, J.; Vinz, F.; Fernandez, K.

1987-01-01

A majority of the current expert systems focus on the symbolic-oriented logic and inference mechanisms of artificial intelligence (AI). Common rule-based systems employ empirical associations and are not well suited to deal with problems often arising in engineering. Described is a prototype expert system which combines both symbolic and numeric computing. The expert system's configuration is presented and its application to a spacecraft attitude control problem is discussed.

A Bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems

NASA Astrophysics Data System (ADS)

Farrell, Kathryn; Oden, J. Tinsley; Faghihi, Danial

2015-08-01

A general adaptive modeling algorithm for selection and validation of coarse-grained models of atomistic systems is presented. A Bayesian framework is developed to address uncertainties in parameters, data, and model selection. Algorithms for computing output sensitivities to parameter variances, model evidence and posterior model plausibilities for given data, and for computing what are referred to as Occam Categories in reference to a rough measure of model simplicity, make up components of the overall approach. Computational results are provided for representative applications.

An Expert System for Diagnosing Eye Diseases using Forward Chaining Method

NASA Astrophysics Data System (ADS)

Munaiseche, C. P. C.; Kaparang, D. R.; Rompas, P. T. D.

2018-02-01

Expert System is a system that seeks to adopt human knowledge to the computer, so that the computer can solve problems which are usually done by experts. The purpose of medical expert system is to support the diagnosis process of physicians. It considers facts and symptoms to provide diagnosis. This implies that a medical expert system uses knowledge about diseases and facts about the patients to suggest diagnosis. The aim of this research is to design an expert system application for diagnosing eye diseases using forward chaining method and to figure out user acceptance to this application through usability testing. Eye is selected because it is one of the five senses which is very sensitive and important. The scope of the work is extended to 16 types of eye diseases with 41 symptoms of the disease, arranged in 16 rules. The computer programming language employed was the PHP programming language and MySQL as the Relational Database Management System (RDBMS). The results obtained showed that the expert system was able to successfully diagnose eye diseases corresponding to the selected symptoms entered as query and the system evaluation through usability testing showed the expert system for diagnosis eye diseases had very good rate of usability, which includes learnability, efficiency, memorability, errors, and satisfaction so that the system can be received in the operational environment.

Expert Witness: A system for developing expert medical testimony

NASA Technical Reports Server (NTRS)

Lewandowski, Raymond; Perkins, David; Leasure, David

1994-01-01

Expert Witness in an expert system designed to assist attorneys and medical experts in determining the merit of medical malpractice claims in the area of obstetrics. It substitutes the time of the medical expert with the time of a paralegal assistant guided by the expert system during the initial investigation of the medical records and patient interviews. The product of the system is a narrative transcript containing important data, immediate conclusions from the data, and overall conclusions of the case that the attorney and medical expert use to make decisions about whether and how to proceed with the case. The transcript may also contain directives for gathering additional information needed for the case. The system is a modified heuristic classifier and is implemented using over 600 CLIPS rules together with a C-based user interface. The data abstraction and solution refinement are implemented directly using forward chaining production and matching. The use of CLIPS and C is essential to delivering a system that runs on a generic PC platform. The direct implementation in CLIPS together with locality of inference ensures that the system will scale gracefully. Two years of use has revealed no errors in the reasoning.

Development of a practical approach to expert elicitation for randomised controlled trials with missing health outcomes: Application to the IMPROVE trial.

PubMed

Mason, Alexina J; Gomes, Manuel; Grieve, Richard; Ulug, Pinar; Powell, Janet T; Carpenter, James

2017-08-01

The analyses of randomised controlled trials with missing data typically assume that, after conditioning on the observed data, the probability of missing data does not depend on the patient's outcome, and so the data are 'missing at random' . This assumption is usually implausible, for example, because patients in relatively poor health may be more likely to drop out. Methodological guidelines recommend that trials require sensitivity analysis, which is best informed by elicited expert opinion, to assess whether conclusions are robust to alternative assumptions about the missing data. A major barrier to implementing these methods in practice is the lack of relevant practical tools for eliciting expert opinion. We develop a new practical tool for eliciting expert opinion and demonstrate its use for randomised controlled trials with missing data. We develop and illustrate our approach for eliciting expert opinion with the IMPROVE trial (ISRCTN 48334791), an ongoing multi-centre randomised controlled trial which compares an emergency endovascular strategy versus open repair for patients with ruptured abdominal aortic aneurysm. In the IMPROVE trial at 3 months post-randomisation, 21% of surviving patients did not complete health-related quality of life questionnaires (assessed by EQ-5D-3L). We address this problem by developing a web-based tool that provides a practical approach for eliciting expert opinion about quality of life differences between patients with missing versus complete data. We show how this expert opinion can define informative priors within a fully Bayesian framework to perform sensitivity analyses that allow the missing data to depend upon unobserved patient characteristics. A total of 26 experts, of 46 asked to participate, completed the elicitation exercise. The elicited quality of life scores were lower on average for the patients with missing versus complete data, but there was considerable uncertainty in these elicited values. The missing at random analysis found that patients randomised to the emergency endovascular strategy versus open repair had higher average (95% credible interval) quality of life scores of 0.062 (-0.005 to 0.130). Our sensitivity analysis that used the elicited expert information as pooled priors found that the gain in average quality of life for the emergency endovascular strategy versus open repair was 0.076 (-0.054 to 0.198). We provide and exemplify a practical tool for eliciting the expert opinion required by recommended approaches to the sensitivity analyses of randomised controlled trials. We show how this approach allows the trial analysis to fully recognise the uncertainty that arises from making alternative, plausible assumptions about the reasons for missing data. This tool can be widely used in the design, analysis and interpretation of future trials, and to facilitate this, materials are available for download.

[Development of expert diagnostic system for common respiratory diseases].

PubMed

Xu, Wei-hua; Chen, You-ling; Yan, Zheng

2014-03-01

To develop an internet-based expert diagnostic system for common respiratory diseases. SaaS system was used to build architecture; pattern of forward reasoning was applied for inference engine design; ASP.NET with C# from the tool pack of Microsoft Visual Studio 2005 was used for website-interview medical expert system.The database of the system was constructed with Microsoft SQL Server 2005. The developed expert system contained large data memory and high efficient function of data interview and data analysis for diagnosis of various diseases.The users were able to perform this system to obtain diagnosis for common respiratory diseases via internet. The developed expert system may be used for internet-based diagnosis of various respiratory diseases,particularly in telemedicine setting.

Knowledge-based systems for power management

NASA Technical Reports Server (NTRS)

Lollar, L. F.

1992-01-01

NASA-Marshall's Electrical Power Branch has undertaken the development of expert systems in support of further advancements in electrical power system automation. Attention is given to the features (1) of the Fault Recovery and Management Expert System, (2) a resource scheduler or Master of Automated Expert Scheduling Through Resource Orchestration, and (3) an adaptive load-priority manager, or Load Priority List Management System. The characteristics of an advisory battery manager for the Hubble Space Telescope, designated the 'nickel-hydrogen expert system', are also noted.

Reference standards, judges, and comparison subjects: roles for experts in evaluating system performance.

PubMed

Hripcsak, George; Wilcox, Adam

2002-01-01

Medical informatics systems are often designed to perform at the level of human experts. Evaluation of the performance of these systems is often constrained by lack of reference standards, either because the appropriate response is not known or because no simple appropriate response exists. Even when performance can be assessed, it is not always clear whether the performance is sufficient or reasonable. These challenges can be addressed if an evaluator enlists the help of clinical domain experts. 1) The experts can carry out the same tasks as the system, and then their responses can be combined to generate a reference standard. 2)The experts can judge the appropriateness of system output directly. 3) The experts can serve as comparison subjects with which the system can be compared. These are separate roles that have different implications for study design, metrics, and issues of reliability and validity. Diagrams help delineate the roles of experts in complex study designs.

Call-duration and triage decisions in out of hours cooperatives with and without the use of an expert system.

PubMed

Ong, Rob S G; Post, Johan; van Rooij, Harry; de Haan, Jan

2008-02-13

Cooperatives delivering out of hours care in the Netherlands are hesitant about the use of expert systems during triage. Apart from the extra costs, cooperatives are not sure that quality of triage is sufficiently enhanced by these systems and believe that call duration will be prolonged drastically. No figures about the influence of the use of an expert system during triage on call duration and triage decisions in out of hours care in the Netherlands are available. Electronically registered data concerning call duration and triage decisions were collected in two cooperatives. One in Tilburg, a cooperative in a Southern city of the Netherlands using an expert system, and one in Groningen, a cooperative in a Northern city not using an expert system. Some other relevant information about the care process was collected additionally. Data about call duration was compared using an independent sample t-test. Data about call decisions was compared using Chi Square. The mean call time in the cooperative using the TAS expert system is 4.6 minutes, in the cooperative not using the expert system 3.9 minutes. A significant difference of 0.7 minutes (0.4 - 1.0, 95% CI) minutes. In the cooperative with an expert system a larger percentage of patients is handled by the assistant, patients are less often referred to a telephone consultation with the GP and are less likely to be offered a visit by the GP.A quick interpretation of the impact of the difference in triage decisions, show that these may be large enough to support the hypothesis that longer call duration is compensated for by less contacts with the GP (by telephone or face-to-face). There is no proof, however, that these differences are caused by the use of the triage system. The larger amount of calls handled by the assistant may be partly caused by the fact that the assistants in the cooperative with an expert system more often consult the GP during triage. And it is not likely that the larger amount of home visits in Groningen can be attributed to the absence of an expert system. The expert system only offers advice whether a GP should be seen, not in which way (by consultation in the office or by home visit). The differences in call times between a cooperative using an expert system and a cooperative not using an expert system are small; 0.4 - 1.0 min. Differences in triage decisions were found, but it is not proven that these can be contributed to the use of an expert system.

Call-duration and triage decisions in out of hours cooperatives with and without the use of an expert system

PubMed Central

Ong, Rob SG; Post, Johan; van Rooij, Harry; de Haan, Jan

2008-01-01

Background Cooperatives delivering out of hours care in the Netherlands are hesitant about the use of expert systems during triage. Apart from the extra costs, cooperatives are not sure that quality of triage is sufficiently enhanced by these systems and believe that call duration will be prolonged drastically. No figures about the influence of the use of an expert system during triage on call duration and triage decisions in out of hours care in the Netherlands are available. Methods Electronically registered data concerning call duration and triage decisions were collected in two cooperatives. One in Tilburg, a cooperative in a Southern city of the Netherlands using an expert system, and one in Groningen, a cooperative in a Northern city not using an expert system. Some other relevant information about the care process was collected additionally. Data about call duration was compared using an independent sample t-test. Data about call decisions was compared using Chi Square. Results The mean call time in the cooperative using the TAS expert system is 4.6 minutes, in the cooperative not using the expert system 3.9 minutes. A significant difference of 0.7 minutes (0.4 – 1.0, 95% CI) minutes. In the cooperative with an expert system a larger percentage of patients is handled by the assistant, patients are less often referred to a telephone consultation with the GP and are less likely to be offered a visit by the GP. A quick interpretation of the impact of the difference in triage decisions, show that these may be large enough to support the hypothesis that longer call duration is compensated for by less contacts with the GP (by telephone or face-to-face). There is no proof, however, that these differences are caused by the use of the triage system. The larger amount of calls handled by the assistant may be partly caused by the fact that the assistants in the cooperative with an expert system more often consult the GP during triage. And it is not likely that the larger amount of home visits in Groningen can be attributed to the absence of an expert system. The expert system only offers advice whether a GP should be seen, not in which way (by consultation in the office or by home visit). Conclusion The differences in call times between a cooperative using an expert system and a cooperative not using an expert system are small; 0.4 – 1.0 min. Differences in triage decisions were found, but it is not proven that these can be contributed to the use of an expert system. PMID:18271970

The Potential of Computer-Based Expert Systems for Special Educators in Rural Settings.

ERIC Educational Resources Information Center

Parry, James D.; Ferrara, Joseph M.

Knowledge-based expert computer systems are addressing issues relevant to all special educators, but are particularly relevant in rural settings where human experts are less available because of distance and cost. An expert system is an application of artificial intelligence (AI) that typically engages the user in a dialogue resembling the…

Fire Effects, Education, and Expert Systems

Treesearch

Robert E. Martin

1987-01-01

Predicting the effects of fires in the year 2000 and beyond will be enhanced by the use of expert systems. Although our predictions may have broad confidence limits, expert systems should help us to improve the predictions and to focus on the areas where improved knowledge is most needed. The knowledge of experts can be incorporated into previously existing knowledge...

Expert system development methodology and the transition from prototyping to operations: FIESTA, a case study

NASA Technical Reports Server (NTRS)

Happell, Nadine; Miksell, Steve; Carlisle, Candace

1989-01-01

A major barrier in taking expert systems from prototype to operational status involves instilling end user confidence in the operational system. The software of different life cycle models is examined and the advantages and disadvantages of each when applied to expert system development are explored. The Fault Isolation Expert System for Tracking and data relay satellite system Applications (FIESTA) is presented as a case study of development of an expert system. The end user confidence necessary for operational use of this system is accentuated by the fact that it will handle real-time data in a secure environment, allowing little tolerance for errors. How FIESTA is dealing with transition problems as it moves from an off-line standalone prototype to an on-line real-time system is discussed.

Expert system development methodology and the transition from prototyping to operations - Fiesta, a case study

NASA Technical Reports Server (NTRS)

Happell, Nadine; Miksell, Steve; Carlisle, Candace

1989-01-01

A major barrier in taking expert systems from prototype to operational status involves instilling end user confidence in the operational system. The software of different life cycle models is examined and the advantages and disadvantages of each when applied to expert system development are explored. The Fault Isolation Expert System for Tracking and data relay satellite system Applications (FIESTA) is presented as a case study of development of an expert system. The end user confidence necessary for operational use of this system is accentuated by the fact that it will handle real-time data in a secure environment, allowing little tolerance for errors. How FIESTA is dealing with transition problems as it moves from an off-line standalone prototype to an on-line real-time system is discussed.

An Embedded Rule-Based Diagnostic Expert System in Ada

NASA Technical Reports Server (NTRS)

Jones, Robert E.; Liberman, Eugene M.

1992-01-01

Ada is becoming an increasingly popular programming language for large Government-funded software projects. Ada with it portability, transportability, and maintainability lends itself well to today's complex programming environment. In addition, expert systems have also assumed a growing role in providing human-like reasoning capability expertise for computer systems. The integration is discussed of expert system technology with Ada programming language, especially a rule-based expert system using an ART-Ada (Automated Reasoning Tool for Ada) system shell. NASA Lewis was chosen as a beta test site for ART-Ada. The test was conducted by implementing the existing Autonomous Power EXpert System (APEX), a Lisp-based power expert system, in ART-Ada. Three components, the rule-based expert systems, a graphics user interface, and communications software make up SMART-Ada (Systems fault Management with ART-Ada). The rules were written in the ART-Ada development environment and converted to Ada source code. The graphics interface was developed with the Transportable Application Environment (TAE) Plus, which generates Ada source code to control graphics images. SMART-Ada communicates with a remote host to obtain either simulated or real data. The Ada source code generated with ART-Ada, TAE Plus, and communications code was incorporated into an Ada expert system that reads the data from a power distribution test bed, applies the rule to determine a fault, if one exists, and graphically displays it on the screen. The main objective, to conduct a beta test on the ART-Ada rule-based expert system shell, was achieved. The system is operational. New Ada tools will assist in future successful projects. ART-Ada is one such tool and is a viable alternative to the straight Ada code when an application requires a rule-based or knowledge-based approach.

A Generic Expert Scheduling System Architecture and Toolkit: GUESS (Generically Used Expert Scheduling System)

NASA Technical Reports Server (NTRS)

Liebowitz, Jay; Krishnamurthy, Vijaya; Rodens, Ira; Houston, Chapman; Liebowitz, Alisa; Baek, Seung; Radko, Joe; Zeide, Janet

1996-01-01

Scheduling has become an increasingly important element in today's society and workplace. Within the NASA environment, scheduling is one of the most frequently performed and challenging functions. Towards meeting NASA's scheduling needs, a research version of a generic expert scheduling system architecture and toolkit has been developed. This final report describes the development and testing of GUESS (Generically Used Expert Scheduling System).

Mean Field Variational Bayesian Data Assimilation

NASA Astrophysics Data System (ADS)

Vrettas, M.; Cornford, D.; Opper, M.

2012-04-01

Current data assimilation schemes propose a range of approximate solutions to the classical data assimilation problem, particularly state estimation. Broadly there are three main active research areas: ensemble Kalman filter methods which rely on statistical linearization of the model evolution equations, particle filters which provide a discrete point representation of the posterior filtering or smoothing distribution and 4DVAR methods which seek the most likely posterior smoothing solution. In this paper we present a recent extension to our variational Bayesian algorithm which seeks the most probably posterior distribution over the states, within the family of non-stationary Gaussian processes. Our original work on variational Bayesian approaches to data assimilation sought the best approximating time varying Gaussian process to the posterior smoothing distribution for stochastic dynamical systems. This approach was based on minimising the Kullback-Leibler divergence between the true posterior over paths, and our Gaussian process approximation. So long as the observation density was sufficiently high to bring the posterior smoothing density close to Gaussian the algorithm proved very effective, on lower dimensional systems. However for higher dimensional systems, the algorithm was computationally very demanding. We have been developing a mean field version of the algorithm which treats the state variables at a given time as being independent in the posterior approximation, but still accounts for their relationships between each other in the mean solution arising from the original dynamical system. In this work we present the new mean field variational Bayesian approach, illustrating its performance on a range of classical data assimilation problems. We discuss the potential and limitations of the new approach. We emphasise that the variational Bayesian approach we adopt, in contrast to other variational approaches, provides a bound on the marginal likelihood of the observations given parameters in the model which also allows inference of parameters such as observation errors, and parameters in the model and model error representation, particularly if this is written as a deterministic form with small additive noise. We stress that our approach can address very long time window and weak constraint settings. However like traditional variational approaches our Bayesian variational method has the benefit of being posed as an optimisation problem. We finish with a sketch of the future directions for our approach.

Efficient detection of wound-bed and peripheral skin with statistical colour models.

PubMed

Veredas, Francisco J; Mesa, Héctor; Morente, Laura

2015-04-01

A pressure ulcer is a clinical pathology of localised damage to the skin and underlying tissue caused by pressure, shear or friction. Reliable diagnosis supported by precise wound evaluation is crucial in order to success on treatment decisions. This paper presents a computer-vision approach to wound-area detection based on statistical colour models. Starting with a training set consisting of 113 real wound images, colour histogram models are created for four different tissue types. Back-projections of colour pixels on those histogram models are used, from a Bayesian perspective, to get an estimate of the posterior probability of a pixel to belong to any of those tissue classes. Performance measures obtained from contingency tables based on a gold standard of segmented images supplied by experts have been used for model selection. The resulting fitted model has been validated on a training set consisting of 322 wound images manually segmented and labelled by expert clinicians. The final fitted segmentation model shows robustness and gives high mean performance rates [(AUC: .9426 (SD .0563); accuracy: .8777 (SD .0799); F-score: 0.7389 (SD .1550); Cohen's kappa: .6585 (SD .1787)] when segmenting significant wound areas that include healing tissues.

Impact of Bias-Correction Type and Conditional Training on Bayesian Model Averaging over the Northeast United States

Treesearch

Michael J. Erickson; Brian A. Colle; Joseph J. Charney

2012-01-01

The performance of a multimodel ensemble over the northeast United States is evaluated before and after applying bias correction and Bayesian model averaging (BMA). The 13-member Stony Brook University (SBU) ensemble at 0000 UTC is combined with the 21-member National Centers for Environmental Prediction (NCEP) Short-Range Ensemble Forecast (SREF) system at 2100 UTC....

Nowcasting Cloud Fields for U.S. Air Force Special Operations

DTIC Science & Technology

2017-03-01

application of Bayes’ Rule offers many advantages over Kernel Density Estimation (KDE) and other commonly used statistical post-processing methods...reflectance and probability of cloud. A statistical post-processing technique is applied using Bayesian estimation to train the system from a set of past...nowcasting, low cloud forecasting, cloud reflectance, ISR, Bayesian estimation, statistical post-processing, machine learning 15. NUMBER OF PAGES

Incorporation of diet information derived from Bayesian stable isotope mixing models into mass-balanced marine ecosystem models: A case study from the Marennes-Oleron Estuary, France

EPA Science Inventory

We investigated the use of output from Bayesian stable isotope mixing models as constraints for a linear inverse food web model of a temperate intertidal seagrass system in the Marennes-Oléron Bay, France. Linear inverse modeling (LIM) is a technique that estimates a complete net...

ATS displays: A reasoning visualization tool for expert systems

NASA Technical Reports Server (NTRS)

Selig, William John; Johannes, James D.

1990-01-01

Reasoning visualization is a useful tool that can help users better understand the inherently non-sequential logic of an expert system. While this is desirable in most all expert system applications, it is especially so for such critical systems as those destined for space-based operations. A hierarchical view of the expert system reasoning process and some characteristics of these various levels is presented. Also presented are Abstract Time Slice (ATS) displays, a tool to visualize the plethora of interrelated information available at the host inferencing language level of reasoning. The usefulness of this tool is illustrated with some examples from a prototype potable water expert system for possible use aboard Space Station Freedom.

Automated Bayesian model development for frequency detection in biological time series.

PubMed

Granqvist, Emma; Oldroyd, Giles E D; Morris, Richard J

2011-06-24

A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure.

Automated Bayesian model development for frequency detection in biological time series

PubMed Central

2011-01-01

Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure. PMID:21702910

COMPUTERIZED RISK AND BIOACCUMULATION SYSTEM (VERSION 1.0)

EPA Science Inventory

CRABS is a combination of a rule-based expert system and more traditional procedural programming techniques. ule-based expert systems attempt to emulate the decision making process of human experts within a clearly defined subject area. xpert systems consist of an "inference engi...

An Expert System for Environmental Data Management.

ERIC Educational Resources Information Center

Berka, Petr; Jirku, Petr

1995-01-01

Examines the possibility of using expert system tools for environmental data management. Describes the domain-independent expert system shell SAK and Knowledge EXplorer, a system that learns rules from data. Demonstrates the functionality of Knowledge EXplorer on an example of water quality evaluation. (LZ)

Data reduction expert assistant

NASA Technical Reports Server (NTRS)

Miller, Glenn E.; Johnston, Mark D.; Hanisch, Robert J.

1991-01-01

Viewgraphs on data reduction expert assistant are presented. Topics covered include: data analysis systems; philosophy of these systems; disadvantages; expert assistant; useful goals; and implementation considerations.

CLIPS: The C language integrated production system

NASA Technical Reports Server (NTRS)

Riley, Gary

1994-01-01

Expert systems are computer programs which emulate human expertise in well defined problem domains. The potential payoff from expert systems is high: valuable expertise can be captured and preserved, repetitive and/or mundane tasks requiring human expertise can be automated, and uniformity can be applied in decision making processes. The C Language Integrated Production System (CLIPS) is an expert system building tool, developed at the Johnson Space Center, which provides a complete environment for the development and delivery of rule and/or object based expert systems. CLIPS was specifically designed to provide a low cost option for developing and deploying expert system applications across a wide range of hardware platforms. The commercial potential of CLIPS is vast. Currently, CLIPS is being used by over 5,000 individuals throughout the public and private sector. Because the CLIPS source code is readily available, numerous groups have used CLIPS as the basis for their own expert system tools. To date, three commercially available tools have been derived from CLIPS. In general, the development of CLIPS has helped to improve the ability to deliver expert system technology throughout the public and private sectors for a wide range of applications and diverse computing environments.

The Paradox of Expertise.

ERIC Educational Resources Information Center

Hankins, George.

1987-01-01

Describes the novice-to-expert model of human learning and compares it to the recent advances in the areas of artificial intelligence and expert systems. Discusses some of the characteristics of experts, proposing connections between them with expert systems and theories of left-right brain functions. (TW)

Expert systems in clinical microbiology.

PubMed

Winstanley, Trevor; Courvalin, Patrice

2011-07-01

This review aims to discuss expert systems in general and how they may be used in medicine as a whole and clinical microbiology in particular (with the aid of interpretive reading). It considers rule-based systems, pattern-based systems, and data mining and introduces neural nets. A variety of noncommercial systems is described, and the central role played by the EUCAST is stressed. The need for expert rules in the environment of reset EUCAST breakpoints is also questioned. Commercial automated systems with on-board expert systems are considered, with emphasis being placed on the "big three": Vitek 2, BD Phoenix, and MicroScan. By necessity and in places, the review becomes a general review of automated system performances for the detection of specific resistance mechanisms rather than focusing solely on expert systems. Published performance evaluations of each system are drawn together and commented on critically.

Developing and using expert systems and neural networks in medicine: a review on benefits and challenges.

PubMed

Sheikhtaheri, Abbas; Sadoughi, Farahnaz; Hashemi Dehaghi, Zahra

2014-09-01

Complicacy of clinical decisions justifies utilization of information systems such as artificial intelligence (e.g. expert systems and neural networks) to achieve better decisions, however, application of these systems in the medical domain faces some challenges. We aimed at to review the applications of these systems in the medical domain and discuss about such challenges. Following a brief introduction of expert systems and neural networks by representing few examples, the challenges of these systems in the medical domain are discussed. We found that the applications of expert systems and artificial neural networks have been increased in the medical domain. These systems have shown many advantages such as utilization of experts' knowledge, gaining rare knowledge, more time for assessment of the decision, more consistent decisions, and shorter decision-making process. In spite of all these advantages, there are challenges ahead of developing and using such systems including maintenance, required experts, inputting patients' data into the system, problems for knowledge acquisition, problems in modeling medical knowledge, evaluation and validation of system performance, wrong recommendations and responsibility, limited domains of such systems and necessity of integrating such systems into the routine work flows. We concluded that expert systems and neural networks can be successfully used in medicine; however, there are many concerns and questions to be answered through future studies and discussions.

a Study on Satellite Diagnostic Expert Systems Using Case-Based Approach

NASA Astrophysics Data System (ADS)

Park, Young-Tack; Kim, Jae-Hoon; Park, Hyun-Soo

1997-06-01

Many research works are on going to monitor and diagnose diverse malfunctions of satellite systems as the complexity and number of satellites increase. Currently, many works on monitoring and diagnosis are carried out by human experts but there are needs to automate much of the routine works of them. Hence, it is necessary to study on using expert systems which can assist human experts routine work by doing automatically, thereby allow human experts devote their expertise more critical and important areas of monitoring and diagnosis. In this paper, we are employing artificial intelligence techniques to model human experts' knowledge and inference the constructed knowledge. Especially, case-based approaches are used to construct a knowledge base to model human expert capabilities which use previous typical exemplars. We have designed and implemented a prototype case-based system for diagnosing satellite malfunctions using cases. Our system remembers typical failure cases and diagnoses a current malfunction by indexing the case base. Diverse methods are used to build a more user friendly interface which allows human experts can build a knowledge base in as easy way.

Bayesian Inference for Signal-Based Seismic Monitoring

NASA Astrophysics Data System (ADS)

Moore, D.

2015-12-01

Traditional seismic monitoring systems rely on discrete detections produced by station processing software, discarding significant information present in the original recorded signal. SIG-VISA (Signal-based Vertically Integrated Seismic Analysis) is a system for global seismic monitoring through Bayesian inference on seismic signals. By modeling signals directly, our forward model is able to incorporate a rich representation of the physics underlying the signal generation process, including source mechanisms, wave propagation, and station response. This allows inference in the model to recover the qualitative behavior of recent geophysical methods including waveform matching and double-differencing, all as part of a unified Bayesian monitoring system that simultaneously detects and locates events from a global network of stations. We demonstrate recent progress in scaling up SIG-VISA to efficiently process the data stream of global signals recorded by the International Monitoring System (IMS), including comparisons against existing processing methods that show increased sensitivity from our signal-based model and in particular the ability to locate events (including aftershock sequences that can tax analyst processing) precisely from waveform correlation effects. We also provide a Bayesian analysis of an alleged low-magnitude event near the DPRK test site in May 2010 [1] [2], investigating whether such an event could plausibly be detected through automated processing in a signal-based monitoring system. [1] Zhang, Miao and Wen, Lianxing. "Seismological Evidence for a Low-Yield Nuclear Test on 12 May 2010 in North Korea". Seismological Research Letters, January/February 2015. [2] Richards, Paul. "A Seismic Event in North Korea on 12 May 2010". CTBTO SnT 2015 oral presentation, video at https://video-archive.ctbto.org/index.php/kmc/preview/partner_id/103/uiconf_id/4421629/entry_id/0_ymmtpps0/delivery/http

The First Expert CAI System

PubMed Central

Feurzeig, Wallace

1984-01-01

The first expert instructional system, the Socratic System, was developed in 1964. One of the earliest applications of this system was in the area of differential diagnosis in clinical medicine. The power of the underlying instructional paradigm was demonstrated and the potential of the approach for valuably supplementing medical instruction was recognized. Twenty years later, despite further educationally significant advances in expert systems technology and enormous reductions in the cost of computers, expert instructional methods have found very little application in medical schools.

Projects in an expert system class

NASA Technical Reports Server (NTRS)

Whitson, George M.

1991-01-01

Many universities now teach courses in expert systems. In these courses students study the architecture of an expert system, knowledge acquisition techniques, methods of implementing systems and verification and validation techniques. A major component of any such course is a class project consisting of the design and implementation of an expert system. Discussed here are a number of techniques that we have used at the University of Texas at Tyler to develop meaningful projects that could be completed in a semester course.

Program maintenance manual for nickel cadmium battery expert system, version 1

NASA Technical Reports Server (NTRS)

1986-01-01

The Nickel-Cadmium Battery Expert System (NICBES) is an expert system for fault diagnosis and advice of the nickel-cadmium batteries found in the Hubble Space Telescope (HST). The system application and security, equipment environment, and the program maintenance procedures are examined.

A Logic Basis for Information Retrieval.

ERIC Educational Resources Information Center

Watters, C. R.; Shepherd, M. A.

1987-01-01

Discusses the potential of recent work in artificial intelligence, especially expert systems, for the development of more effective information retrieval systems. Highlights include the role of an expert bibliographic retrieval system and a prototype expert retrieval system, PROBIB-2, that uses MicroProlog to provide deductive reasoning…

An expert system for the quantification of fault rates in construction fall accidents.

PubMed

Talat Birgonul, M; Dikmen, Irem; Budayan, Cenk; Demirel, Tuncay

2016-01-01

Expert witness reports, prepared with the aim of quantifying fault rates among parties, play an important role in a court's final decision. However, conflicting fault rates assigned by different expert witness boards lead to iterative objections raised by the related parties. This unfavorable situation mainly originates due to the subjectivity of expert judgments and unavailability of objective information about the causes of accidents. As a solution to this shortcoming, an expert system based on a rule-based system was developed for the quantification of fault rates in construction fall accidents. The aim of developing DsSafe is decreasing the subjectivity inherent in expert witness reports. Eighty-four inspection reports prepared by the official and authorized inspectors were examined and root causes of construction fall accidents in Turkey were identified. Using this information, an evaluation form was designed and submitted to the experts. Experts were asked to evaluate the importance level of the factors that govern fall accidents and determine the fault rates under different scenarios. Based on expert judgments, a rule-based expert system was developed. The accuracy and reliability of DsSafe were tested with real data as obtained from finalized court cases. DsSafe gives satisfactory results.

Ground terminal expert (GTEX). Part 2: Expert system diagnostics for a 30/20 Gigahertz satellite transponder

NASA Technical Reports Server (NTRS)

Durkin, John; Schlegelmilch, Richard; Tallo, Donald

1992-01-01

A research effort was undertaken to investigate how expert system technology could be applied to a satellite communications system. The focus of the expert system is the satellite earth station. A proof of concept expert system called the Ground Terminal Expert (GTEX) was developed at the University of Akron in collaboration with the NASA Lewis Research Center. With the increasing demand for satellite earth stations, maintenance is becoming a vital issue. Vendors of such systems will be looking for cost effective means of maintaining such systems. The objective of GTEX is to aid in diagnosis of faults occurring with the digital earth station. GTEX was developed on a personal computer using the Automated Reasoning Tool for Information Management (ART-IM) developed by the Inference Corporation. Developed for the Phase 2 digital earth station, GTEX is a part of the Systems Integration Test and Evaluation (SITE) facility located at the NASA Lewis Research Center.

System and method for creating expert systems

NASA Technical Reports Server (NTRS)

Hughes, Peter M. (Inventor); Luczak, Edward C. (Inventor)

1998-01-01

A system and method provides for the creation of a highly graphical expert system without the need for programming in code. An expert system is created by initially building a data interface, defining appropriate Mission, User-Defined, Inferred, and externally-generated GenSAA (EGG) data variables whose data values will be updated and input into the expert system. Next, rules of the expert system are created by building appropriate conditions of the rules which must be satisfied and then by building appropriate actions of rules which are to be executed upon corresponding conditions being satisfied. Finally, an appropriate user interface is built which can be highly graphical in nature and which can include appropriate message display and/or modification of display characteristics of a graphical display object, to visually alert a user of the expert system of varying data values, upon conditions of a created rule being satisfied. The data interface building, rule building, and user interface building are done in an efficient manner and can be created without the need for programming in code.

Threat expert system technology advisor

NASA Technical Reports Server (NTRS)

Kurrasch, E. R.; Tripp, L. R.

1987-01-01

A prototype expert system was developed to determine the feasibility of using expert system technology to enhance the performance and survivability of helicopter pilots in a combat threat environment while flying NOE (Nap of the Earth) missions. The basis for the concept is the potential of using an Expert System Advisor to reduce the extreme overloading of the pilot who flies NOE mission below treetop level at approximately 40 knots while performing several other functions. The ultimate goal is to develop a Threat Expert System Advisor which provides threat information and advice that are better than even a highly experienced copilot. The results clearly show that the NOE pilot needs all the help in decision aiding and threat situation awareness that he can get. It clearly shows that heuristics are important and that an expert system for combat NOE helicopter missions can be of great help to the pilot in complex threat situations and in making decisions.

Learning a Health Knowledge Graph from Electronic Medical Records.

PubMed

Rotmensch, Maya; Halpern, Yoni; Tlimat, Abdulhakim; Horng, Steven; Sontag, David

2017-07-20

Demand for clinical decision support systems in medicine and self-diagnostic symptom checkers has substantially increased in recent years. Existing platforms rely on knowledge bases manually compiled through a labor-intensive process or automatically derived using simple pairwise statistics. This study explored an automated process to learn high quality knowledge bases linking diseases and symptoms directly from electronic medical records. Medical concepts were extracted from 273,174 de-identified patient records and maximum likelihood estimation of three probabilistic models was used to automatically construct knowledge graphs: logistic regression, naive Bayes classifier and a Bayesian network using noisy OR gates. A graph of disease-symptom relationships was elicited from the learned parameters and the constructed knowledge graphs were evaluated and validated, with permission, against Google's manually-constructed knowledge graph and against expert physician opinions. Our study shows that direct and automated construction of high quality health knowledge graphs from medical records using rudimentary concept extraction is feasible. The noisy OR model produces a high quality knowledge graph reaching precision of 0.85 for a recall of 0.6 in the clinical evaluation. Noisy OR significantly outperforms all tested models across evaluation frameworks (p < 0.01).

Modelling the Longevity of Dental Restorations by means of a CBR System

PubMed Central

Aliaga, Ignacio J.; Vera, Vicente; García, Alvaro E.

2015-01-01

The lifespan of dental restorations is limited. Longevity depends on the material used and the different characteristics of the dental piece. However, it is not always the case that the best and longest lasting material is used since patients may prefer different treatments according to how noticeable the material is. Over the last 100 years, the most commonly used material has been silver amalgam, which, while very durable, is somewhat aesthetically displeasing. Our study is based on the collection of data from the charts, notes, and radiographic information of restorative treatments performed by Dr. Vera in 1993, the analysis of the information by computer artificial intelligence to determine the most appropriate restoration, and the monitoring of the evolution of the dental restoration. The data will be treated confidentially according to the Organic Law 15/1999 on 13 December on the Protection of Personal Data. This paper also presents a clustering technique capable of identifying the most significant cases with which to instantiate the case-base. In order to classify the cases, a mixture of experts is used which incorporates a Bayesian network and a multilayer perceptron; the combination of both classifiers is performed with a neural network. PMID:25866792

[The application and development of artificial intelligence in medical diagnosis systems].

PubMed

Chen, Zhencheng; Jiang, Yong; Xu, Mingyu; Wang, Hongyan; Jiang, Dazong

2002-09-01

This paper has reviewed the development of artificial intelligence in medical practice and medical diagnostic expert systems, and has summarized the application of artificial neural network. It explains that a source of difficulty in medical diagnostic system is the co-existence of multiple diseases--the potentially inter-related diseases. However, the difficulty of image expert systems is inherent in high-level vision. And it increases the complexity of expert system in medical image. At last, the prospect for the development of artificial intelligence in medical image expert systems is made.

Development of an expert system for assessing trumpeter swan breeding habitat in the Northern Rocky Mountains.

USGS Publications Warehouse

Sojda, Richard S.; Cornely, John E.; Howe, Adele E.

2002-01-01

A decision support system for the management of the Rocky Mountain Population of Trumpeter Swans (Cygnus buccinators) is being developed. As part of this, three expert systems are also in development: one for assessing the quality of Trumpeter Swan breeding habitat; one for making water level recommendations in montane, palustrine wetlands; and one for assessing the contribution a particular site can make towards meeting objectives from as flyway perspective. The focus of this paper is the development of the breeding habitat expert system, which currently consists of 157 rules. Out purpose is to provide decision support for issues that appear to be beyond the capability of a single persons to conceptualize and solve. We propose that by involving multiple experts in the development and use of the systems, management will be significantly improved. The knowledge base for the expert system has been developed using standard knowledge engineering techniques with a small team of ecological experts. Knowledge was then coded using production rules organized in decision trees using a commercial expert system development shell. The final system has been deployed on the world wide web.

Evaluation of a Multivariate Syndromic Surveillance System for West Nile Virus.

PubMed

Faverjon, Céline; Andersson, M Gunnar; Decors, Anouk; Tapprest, Jackie; Tritz, Pierre; Sandoz, Alain; Kutasi, Orsolya; Sala, Carole; Leblond, Agnès

2016-06-01

Various methods are currently used for the early detection of West Nile virus (WNV) but their outputs are not quantitative and/or do not take into account all available information. Our study aimed to test a multivariate syndromic surveillance system to evaluate if the sensitivity and the specificity of detection of WNV could be improved. Weekly time series data on nervous syndromes in horses and mortality in both horses and wild birds were used. Baselines were fitted to the three time series and used to simulate 100 years of surveillance data. WNV outbreaks were simulated and inserted into the baselines based on historical data and expert opinion. Univariate and multivariate syndromic surveillance systems were tested to gauge how well they detected the outbreaks; detection was based on an empirical Bayesian approach. The systems' performances were compared using measures of sensitivity, specificity, and area under receiver operating characteristic curve (AUC). When data sources were considered separately (i.e., univariate systems), the best detection performance was obtained using the data set of nervous symptoms in horses compared to those of bird and horse mortality (AUCs equal to 0.80, 0.75, and 0.50, respectively). A multivariate outbreak detection system that used nervous symptoms in horses and bird mortality generated the best performance (AUC = 0.87). The proposed approach is suitable for performing multivariate syndromic surveillance of WNV outbreaks. This is particularly relevant, given that a multivariate surveillance system performed better than a univariate approach. Such a surveillance system could be especially useful in serving as an alert for the possibility of human viral infections. This approach can be also used for other diseases for which multiple sources of evidence are available.

A Fast Surrogate-facilitated Data-driven Bayesian Approach to Uncertainty Quantification of a Regional Groundwater Flow Model with Structural Error

NASA Astrophysics Data System (ADS)

Xu, T.; Valocchi, A. J.; Ye, M.; Liang, F.

2016-12-01

Due to simplification and/or misrepresentation of the real aquifer system, numerical groundwater flow and solute transport models are usually subject to model structural error. During model calibration, the hydrogeological parameters may be overly adjusted to compensate for unknown structural error. This may result in biased predictions when models are used to forecast aquifer response to new forcing. In this study, we extend a fully Bayesian method [Xu and Valocchi, 2015] to calibrate a real-world, regional groundwater flow model. The method uses a data-driven error model to describe model structural error and jointly infers model parameters and structural error. In this study, Bayesian inference is facilitated using high performance computing and fast surrogate models. The surrogate models are constructed using machine learning techniques to emulate the response simulated by the computationally expensive groundwater model. We demonstrate in the real-world case study that explicitly accounting for model structural error yields parameter posterior distributions that are substantially different from those derived by the classical Bayesian calibration that does not account for model structural error. In addition, the Bayesian with error model method gives significantly more accurate prediction along with reasonable credible intervals.

Bayesian logistic regression approaches to predict incorrect DRG assignment.

PubMed

Suleiman, Mani; Demirhan, Haydar; Boyd, Leanne; Girosi, Federico; Aksakalli, Vural

2018-05-07

Episodes of care involving similar diagnoses and treatments and requiring similar levels of resource utilisation are grouped to the same Diagnosis-Related Group (DRG). In jurisdictions which implement DRG based payment systems, DRGs are a major determinant of funding for inpatient care. Hence, service providers often dedicate auditing staff to the task of checking that episodes have been coded to the correct DRG. The use of statistical models to estimate an episode's probability of DRG error can significantly improve the efficiency of clinical coding audits. This study implements Bayesian logistic regression models with weakly informative prior distributions to estimate the likelihood that episodes require a DRG revision, comparing these models with each other and to classical maximum likelihood estimates. All Bayesian approaches had more stable model parameters than maximum likelihood. The best performing Bayesian model improved overall classification per- formance by 6% compared to maximum likelihood, with a 34% gain compared to random classification, respectively. We found that the original DRG, coder and the day of coding all have a significant effect on the likelihood of DRG error. Use of Bayesian approaches has improved model parameter stability and classification accuracy. This method has already lead to improved audit efficiency in an operational capacity.

Intelligent systems for human resources.

PubMed

Kline, K B

1988-11-01

An intelligent system contains knowledge about some domain; it has sophisticated decision-making processes and the ability to explain its actions. The most important aspect of an intelligent system is its ability to effectively interact with humans to teach or assist complex information processing. Two intelligent systems are Intelligent Tutoring Systems (ITs) and Expert Systems. The ITSs provide instruction to a student similar to a human tutor. The ITSs capture individual performance and tutor deficiencies. These systems consist of an expert module, which contains the knowledge or material to be taught; the student module, which contains a representation of the knowledge the student knows and does not know about the domain; and the instructional or teaching module, which selects specific knowledge to teach, the instructional strategy, and provides assistance to the student to tutor deficiencies. Expert systems contain an expert's knowledge about some domain and perform specialized tasks or aid a novice in the performance of certain tasks. The most important part of an expert system is the knowledge base. This knowledge base contains all the specialized and technical knowledge an expert possesses. For an expert system to interact effectively with humans, it must have the ability to explain its actions. Use of intelligent systems can have a profound effect on human resources. The ITSs can provide better training by tutoring on an individual basis, and the expert systems can make better use of human resources through job aiding and performing complex tasks. With increasing training requirements and "doing more with less," intelligent systems can have a positive effect on human resources.

Artificial Intelligence and Expert Systems Research and Their Possible Impact on Information Science.

ERIC Educational Resources Information Center

Borko, Harold

1985-01-01

Defines artificial intelligence (AI) and expert systems; describes library applications utilizing AI to automate creation of document representations, request formulations, and design and modify search strategies for information retrieval systems; discusses expert system development for information services; and reviews impact of these…

Enhanced use of CLIPS at the Los Alamos National Laboratory

NASA Technical Reports Server (NTRS)

Duerre, K. H.; Parkinson, W. J.; Osowski, J. J.

1991-01-01

Early efforts for producing expert systems for engineering applications used a limited subset of C Language Integrated Production System (CLIPS) features. The implementation details of previous expert systems and of the current expert system, which is used for training operators in the control of the Isotope Separation System, are discussed.

Third CLIPS Conference Proceedings, volume 2

NASA Technical Reports Server (NTRS)

Riley, Gary (Editor)

1994-01-01

Expert systems are computer programs which emulate human expertise in well defined problem domains. The C Language Integrated Production System (CLIPS) is an expert system building tool, developed at the Johnson Space Center, which provides a complete environment for the development and delivery of rule and/or object based expert systems. CLIPS was specifically designed to provide a low cost option for developing and deploying expert system applications across a wide range of hardware platforms. The development of CLIPS has helped to improve the ability to deliver expert system technology throughout the public and private sectors for a wide range of applications and diverse computing environments. The Third Conference on CLIPS provided a forum for CLIPS users to present and discuss papers relating to CLIPS applications, uses, and extensions.

SigmaCLIPSE = presentation management + NASA CLI PS + SQL

NASA Technical Reports Server (NTRS)

Weiss, Bernard P., Jr.

1990-01-01

SigmaCLIPSE provides an expert systems and 'intelligent' data base development program for diverse systems integration environments that require support for automated reasoning and expert systems technology, presentation management, and access to 'intelligent' SQL data bases. The SigmaCLIPSE technology and and its integrated ability to access 4th generation application development and decision support tools through a portable SQL interface, comprises a sophisticated software development environment for solving knowledge engineering and expert systems development problems in information intensive commercial environments -- financial services, health care, and distributed process control -- where the expert system must be extendable -- a major architectural advantage of NASA CLIPS. SigmaCLIPSE is a research effort intended to test the viability of merging SQL data bases with expert systems technology.

A diagnostic expert system for aircraft generator control unit (GCU)

NASA Astrophysics Data System (ADS)

Ho, Ting-Long; Bayles, Robert A.; Havlicsek, Bruce L.

The modular VSCF (variable-speed constant-frequency) generator families are described as using standard modules to reduce the maintenance cost and to improve the product's testability. A general diagnostic expert system shell that guides troubleshooting of modules or line replaceable units (LRUs) is introduced. An application of the diagnostic system to a particular LRU, the generator control unit (GCU) is reported. The approach to building the diagnostic expert system is first to capture general diagnostic strategy in an expert system shell. This shell can be easily applied to different devices or LRUs by writing rules to capture only additional device-specific diagnostic information from expert repair personnel. The diagnostic system has the necessary knowledge embedded in its programs and exhibits expertise to troubleshoot the GCU.

A rule-based expert system for generating control displays at the Advanced Photon Source

NASA Astrophysics Data System (ADS)

Coulter, Karen J.

1994-12-01

The integration of a rule-based expert system for generating screen displays for controlling and monitoring instrumentation under the Experimental Physics and Industrial Control System (EPICS) is presented. The expert system is implemented using CLIPS, an expert system shell from the Software Technology Branch at Lyndon B. Johnson Space Center. The user selects the hardware input and output to be displayed and the expert system constructs a graphical control screen appropriate for the data. Such a system provides a method for implementing a common look and feel for displays created by several different users and reduces the amount of time required to create displays for new hardware configurations. Users are able to modify the displays as needed using the EPICS display editor tool.

The potential of expert systems for remote sensing application

NASA Technical Reports Server (NTRS)

Mooneyhan, D. W.

1983-01-01

An overview of the status and potential of artificial intelligence-driven expert systems in the role of image data analysis is presented. An expert system is defined and its structure is summarized. Three such systems designed for image interpretation are outlined. The use of an expert system to detect changes on the earth's surface is discussed, and the components of a knowledge-based image interpretation system and their make-up are outlined. An example of how such a system should work for an area in the tropics where deforestation has occurred is presented as a sequence of situation/action decisions.

A CLIPS-based expert system for the evaluation and selection of robots

NASA Technical Reports Server (NTRS)

Nour, Mohamed A.; Offodile, Felix O.; Madey, Gregory R.

1994-01-01

This paper describes the development of a prototype expert system for intelligent selection of robots for manufacturing operations. The paper first develops a comprehensive, three-stage process to model the robot selection problem. The decisions involved in this model easily lend themselves to an expert system application. A rule-based system, based on the selection model, is developed using the CLIPS expert system shell. Data about actual robots is used to test the performance of the prototype system. Further extensions to the rule-based system for data handling and interfacing capabilities are suggested.

Reliability of a Bayesian network to predict an elevated aldosterone-to-renin ratio.

PubMed

Ducher, Michel; Mounier-Véhier, Claire; Lantelme, Pierre; Vaisse, Bernard; Baguet, Jean-Philippe; Fauvel, Jean-Pierre

2015-05-01

Resistant hypertension is common, mainly idiopathic, but sometimes related to primary aldosteronism. Thus, most hypertension specialists recommend screening for primary aldosteronism. To optimize the selection of patients whose aldosterone-to-renin ratio (ARR) is elevated from simple clinical and biological characteristics. Data from consecutive patients referred between 1 June 2008 and 30 May 2009 were collected retrospectively from five French 'European excellence hypertension centres' institutional registers. Patients were included if they had at least one of: onset of hypertension before age 40 years, resistant hypertension, history of hypokalaemia, efficient treatment by spironolactone, and potassium supplementation. An ARR>32 ng/L and aldosterone>160 ng/L in patients treated without agents altering the renin-angiotensin system was considered as elevated. Bayesian network and stepwise logistic regression were used to predict an elevated ARR. Of 334 patients, 89 were excluded (31 for incomplete data, 32 for taking agents that alter the renin-angiotensin system and 26 for other reasons). Among 245 included patients, 110 had an elevated ARR. Sensitivity reached 100% or 63.3% using Bayesian network or logistic regression, respectively, and specificity reached 89.6% or 67.2%, respectively. The area under the receiver-operating-characteristic curve obtained with the Bayesian network was significantly higher than that obtained by stepwise regression (0.93±0.02 vs. 0.70±0.03; P<0.001). In hypertension centres, Bayesian network efficiently detected patients with an elevated ARR. An external validation study is required before use in primary clinical settings. Copyright © 2015 Elsevier Masson SAS. All rights reserved.