Probabilities in implicit learning.
Tseng, Philip; Hsu, Tzu-Yu; Tzeng, Ovid J L; Hung, Daisy L; Juan, Chi-Hung
2011-01-01
The visual system possesses a remarkable ability in learning regularities from the environment. In the case of contextual cuing, predictive visual contexts such as spatial configurations are implicitly learned, retained, and used to facilitate visual search-all without one's subjective awareness and conscious effort. Here we investigated whether implicit learning and its facilitatory effects are sensitive to the statistical property of such implicit knowledge. In other words, are highly probable events learned better than less probable ones even when such learning is implicit? We systematically varied the frequencies of context repetition to alter the degrees of learning. Our results showed that search efficiency increased consistently as contextual probabilities increased. Thus, the visual contexts, along with their probability of occurrences, were both picked up by the visual system. Furthermore, even when the total number of exposures was held constant between each probability, the highest probability still enjoyed a greater cuing effect, suggesting that the temporal aspect of implicit learning is also an important factor to consider in addition to the effect of mere frequency. Together, these findings suggest that implicit learning, although bypassing observers' conscious encoding and retrieval effort, behaves much like explicit learning in the sense that its facilitatory effect also varies as a function of its associative strengths.
WPE: A Mathematical Microworld for Learning Probability
ERIC Educational Resources Information Center
Kiew, Su Ding; Sam, Hong Kian
2006-01-01
In this study, the researchers developed the Web-based Probability Explorer (WPE), a mathematical microworld and investigated the effectiveness of the microworld's constructivist learning environment in enhancing the learning of probability and improving students' attitudes toward mathematics. This study also determined the students' satisfaction…
Location probability learning requires focal attention.
Kabata, Takashi; Yokoyama, Takemasa; Noguchi, Yasuki; Kita, Shinichi
2014-01-01
Target identification is related to the frequency with which targets appear at a given location, with greater frequency enhancing identification. This phenomenon suggests that location probability learned through repeated experience with the target modulates cognitive processing. However, it remains unclear whether attentive processing of the target is required to learn location probability. Here, we used a dual-task paradigm to test the location probability effect of attended and unattended stimuli. Observers performed an attentionally demanding central-letter task and a peripheral-bar discrimination task in which location probability was manipulated. Thus, we were able to compare performance on the peripheral task when attention was fully engaged to the target (single-task condition) versus when attentional resources were drawn away by the central task (dual-task condition). The location probability effect occurred only in the single-task condition, when attention resources were fully available. This suggests that location probability learning requires attention to the target stimuli.
Learning a Probability Distribution Efficiently and Reliably
NASA Technical Reports Server (NTRS)
Laird, Philip; Gamble, Evan
1988-01-01
A new algorithm, called the CDF-Inversion Algorithm, is described. Using it, one can efficiently learn a probability distribution over a finite set to a specified accuracy and confidence. The algorithm can be extended to learn joint distributions over a vector space. Some implementation results are described.
Probability detection mechanisms and motor learning.
Lungu, O V; Wächter, T; Liu, T; Willingham, D T; Ashe, J
2004-11-01
The automatic detection of patterns or regularities in the environment is central to certain forms of motor learning, which are largely procedural and implicit. The rules underlying the detection and use of probabilistic information in the perceptual-motor domain are largely unknown. We conducted two experiments involving a motor learning task with direct and crossed mapping of motor responses in which probabilities were present at the stimulus set level, the response set level, and at the level of stimulus-response (S-R) mapping. We manipulated only one level at a time, while controlling for the other two. The results show that probabilities were detected only when present at the S-R mapping and motor levels, but not at the perceptual one (experiment 1), unless the perceptual features have a dimensional overlap with the S-R mapping rule (experiment 2). The effects of probability detection were mostly facilitatory at the S-R mapping, both facilitatory and inhibitory at the perceptual level, and predominantly inhibitory at the response-set level. The facilitatory effects were based on learning the absolute frequencies first and transitional probabilities later (for the S-R mapping rule) or both types of information at the same time (for perceptual level), whereas the inhibitory effects were based on learning first the transitional probabilities. Our data suggest that both absolute frequencies and transitional probabilities are used in motor learning, but in different temporal orders, according to the probabilistic properties of the environment. The results support the idea that separate neural circuits may be involved in detecting absolute frequencies as compared to transitional probabilities.
Rethinking the learning of belief network probabilities
Musick, R.
1996-03-01
Belief networks are a powerful tool for knowledge discovery that provide concise, understandable probabilistic models of data. There are methods grounded in probability theory to incrementally update the relationships described by the belief network when new information is seen, to perform complex inferences over any set of variables in the data, to incorporate domain expertise and prior knowledge into the model, and to automatically learn the model from data. This paper concentrates on part of the belief network induction problem, that of learning the quantitative structure (the conditional probabilities), given the qualitative structure. In particular, the current practice of rote learning the probabilities in belief networks can be significantly improved upon. We advance the idea of applying any learning algorithm to the task of conditional probability learning in belief networks, discuss potential benefits, and show results of applying neural networks and other algorithms to a medium sized car insurance belief network. The results demonstrate from 10 to 100% improvements in model error rates over the current approaches.
Implicit learning of fifth- and sixth-order sequential probabilities.
Remillard, Gilbert
2010-10-01
Serial reaction time (SRT) task studies have established that people can implicitly learn sequential contingencies as complex as fourth-order probabilities. The present study examined people's ability to learn fifth-order (Experiment 1) and sixth-order (Experiment 2) probabilities. Remarkably, people learned fifth- and sixth-order probabilities. This suggests that the implicit sequence learning mechanism can operate over a range of at least seven sequence elements.
Probability learning and Piagetian probability conceptions in children 5 to 12 years old.
Kreitler, S; Zigler, E; Kreitler, H
1989-11-01
This study focused on the relations between performance on a three-choice probability-learning task and conceptions of probability as outlined by Piaget concerning mixture, normal distribution, random selection, odds estimation, and permutations. The probability-learning task and four Piagetian tasks were administered randomly to 100 male and 100 female, middle SES, average IQ children in three age groups (5 to 6, 8 to 9, and 11 to 12 years old) from different schools. Half the children were from Middle Eastern backgrounds, and half were from European or American backgrounds. As predicted, developmental level of probability thinking was related to performance on the probability-learning task. The more advanced the child's probability thinking, the higher his or her level of maximization and hypothesis formulation and testing and the lower his or her level of systematically patterned responses. The results suggest that the probability-learning and Piagetian tasks assess similar cognitive skills and that performance on the probability-learning task reflects a variety of probability concepts.
Choice strategies in multiple-cue probability learning.
White, Chris M; Koehler, Derek J
2007-07-01
Choice strategies for selecting among outcomes in multiple-cue probability learning were investigated using a simulated medical diagnosis task. Expected choice probabilities (the proportion of times each outcome was selected given each cue pattern) under alternative choice strategies were constructed from corresponding observed judged probabilities (of each outcome given each cue pattern) and compared with observed choice probabilities. Most of the participants were inferred to have responded by using a deterministic strategy, in which the outcome with the higher judged probability is consistently chosen, rather than a probabilistic strategy, in which an outcome is chosen with a probability equal to its judged probability. Extended practice in the learning environment did not affect choice strategy selection, contrary to reports from previous studies, results of which may instead be attributable to changes with practice in the variability and extremity of the perceived probabilities on which the choices were based.
Probability & Statistics: Modular Learning Exercises. Student Edition
ERIC Educational Resources Information Center
Actuarial Foundation, 2012
2012-01-01
The purpose of these modules is to provide an introduction to the world of probability and statistics to accelerated mathematics students at the high school level. The materials are centered on the fictional town of Happy Shores, a coastal community which is at risk for hurricanes. Actuaries at an insurance company figure out the risks and…
Probability & Statistics: Modular Learning Exercises. Teacher Edition
ERIC Educational Resources Information Center
Actuarial Foundation, 2012
2012-01-01
The purpose of these modules is to provide an introduction to the world of probability and statistics to accelerated mathematics students at the high school level. The modules also introduce students to real world math concepts and problems that property and casualty actuaries come across in their work. They are designed to be used by teachers and…
Overcoming Challenges in Learning Probability Vocabulary
ERIC Educational Resources Information Center
Groth, Randall E.; Butler, Jaime; Nelson, Delmar
2016-01-01
Students can struggle to understand and use terms that describe probabilities. Such struggles lead to difficulties comprehending classroom conversations. In this article, we describe some specific misunderstandings a group of students (ages 11-12) held in regard to vocabulary such as "certain", "likely" and…
Differentiating Phonotactic Probability and Neighborhood Density in Adult Word Learning
ERIC Educational Resources Information Center
Storkel, Holly L.; Armbruster, Jonna; Hogan, Tiffany P.
2006-01-01
Purpose: The purpose of this study was to differentiate effects of phonotactic probability, the likelihood of occurrence of a sound sequence, and neighborhood density, the number of words that sound similar to a given word, on adult word learning. A second purpose was to determine what aspect of word learning (viz., triggering learning, formation…
Probability Modeling and Thinking: What Can We Learn from Practice?
ERIC Educational Resources Information Center
Pfannkuch, Maxine; Budgett, Stephanie; Fewster, Rachel; Fitch, Marie; Pattenwise, Simeon; Wild, Chris; Ziedins, Ilze
2016-01-01
Because new learning technologies are enabling students to build and explore probability models, we believe that there is a need to determine the big enduring ideas that underpin probabilistic thinking and modeling. By uncovering the elements of the thinking modes of expert users of probability models we aim to provide a base for the setting of…
Choice Strategies in Multiple-Cue Probability Learning
ERIC Educational Resources Information Center
White, Chris M.; Koehler, Derek J.
2007-01-01
Choice strategies for selecting among outcomes in multiple-cue probability learning were investigated using a simulated medical diagnosis task. Expected choice probabilities (the proportion of times each outcome was selected given each cue pattern) under alternative choice strategies were constructed from corresponding observed judged…
Configural Effect in Multiple-Cue Probability Learning
ERIC Educational Resources Information Center
Edgell, Stephen E.; Castellan, N. John, Jr.
1973-01-01
In a nonmetric multiple-cue probability learning task involving 2 binary cue dimensions, it was found that Ss can learn to use configural or pattern information (a) when only the configural information is relevant, and in addition to the configural information, one or both of the cue dimensions are relevant. (Author/RK)
Fostering Positive Attitude in Probability Learning Using Graphing Calculator
ERIC Educational Resources Information Center
Tan, Choo-Kim; Harji, Madhubala Bava; Lau, Siong-Hoe
2011-01-01
Although a plethora of research evidence highlights positive and significant outcomes of the incorporation of the Graphing Calculator (GC) in mathematics education, its use in the teaching and learning process appears to be limited. The obvious need to revisit the teaching and learning of Probability has resulted in this study, i.e. to incorporate…
Feedback valence affects auditory perceptual learning independently of feedback probability.
Amitay, Sygal; Moore, David R; Molloy, Katharine; Halliday, Lorna F
2015-01-01
Previous studies have suggested that negative feedback is more effective in driving learning than positive feedback. We investigated the effect on learning of providing varying amounts of negative and positive feedback while listeners attempted to discriminate between three identical tones; an impossible task that nevertheless produces robust learning. Four feedback conditions were compared during training: 90% positive feedback or 10% negative feedback informed the participants that they were doing equally well, while 10% positive or 90% negative feedback informed them they were doing equally badly. In all conditions the feedback was random in relation to the listeners' responses (because the task was to discriminate three identical tones), yet both the valence (negative vs. positive) and the probability of feedback (10% vs. 90%) affected learning. Feedback that informed listeners they were doing badly resulted in better post-training performance than feedback that informed them they were doing well, independent of valence. In addition, positive feedback during training resulted in better post-training performance than negative feedback, but only positive feedback indicating listeners were doing badly on the task resulted in learning. As we have previously speculated, feedback that better reflected the difficulty of the task was more effective in driving learning than feedback that suggested performance was better than it should have been given perceived task difficulty. But contrary to expectations, positive feedback was more effective than negative feedback in driving learning. Feedback thus had two separable effects on learning: feedback valence affected motivation on a subjectively difficult task, and learning occurred only when feedback probability reflected the subjective difficulty. To optimize learning, training programs need to take into consideration both feedback valence and probability.
Cheng, Peter C-H
2011-07-01
The representational epistemic approach to the design of visual displays and notation systems advocates encoding the fundamental conceptual structure of a knowledge domain directly in the structure of a representational system. It is claimed that representations so designed will benefit from greater semantic transparency, which enhances comprehension and ease of learning, and plastic generativity, which makes the meaningful manipulation of the representation easier and less error prone. Epistemic principles for encoding fundamental conceptual structures directly in representational schemes are described. The diagrammatic recodification of probability theory is undertaken to demonstrate how the fundamental conceptual structure of a knowledge domain can be analyzed, how the identified conceptual structure may be encoded in a representational system, and the cognitive benefits that follow. An experiment shows the new probability space diagrams are superior to the conventional approach for learning this conceptually challenging topic.
Visual search and location probability learning from variable perspectives.
Jiang, Yuhong V; Swallow, Khena M; Capistrano, Christian G
2013-05-28
Do moving observers code attended locations relative to the external world or relative to themselves? To address this question we asked participants to conduct visual search on a tabletop. The search target was more likely to occur in some locations than others. Participants walked to different sides of the table from trial to trial, changing their perspective. The high-probability locations were stable on the tabletop but variable relative to the viewer. When participants were informed of the high-probability locations, search was faster when the target was in those locations, demonstrating probability cuing. However, in the absence of explicit instructions and awareness, participants failed to acquire an attentional bias toward the high-probability locations even when the search items were displayed over an invariant natural scene. Additional experiments showed that locomotion did not interfere with incidental learning, but the lack of a consistent perspective prevented participants from acquiring probability cuing incidentally. We conclude that spatial biases toward target-rich locations are directed by two mechanisms: incidental learning and goal-driven attention. Incidental learning codes attended locations in a viewer-centered reference frame and is not updated with viewer movement. Goal-driven attention can be deployed to prioritize an environment-rich region.
Learning about Posterior Probability: Do Diagrams and Elaborative Interrogation Help?
ERIC Educational Resources Information Center
Clinton, Virginia; Alibali, Martha W.; Nathan, Mitchell J.
2016-01-01
To learn from a text, students must make meaningful connections among related ideas in that text. This study examined the effectiveness of two methods of improving connections--elaborative interrogation and diagrams--in written lessons about posterior probability. Undergraduate students (N = 198) read a lesson in one of three questioning…
Statistical learning of action: the role of conditional probability.
Meyer, Meredith; Baldwin, Dare
2011-12-01
Identification of distinct units within a continuous flow of human action is fundamental to action processing. Such segmentation may rest in part on statistical learning. In a series of four experiments, we examined what types of statistics people can use to segment a continuous stream involving many brief, goal-directed action elements. The results of Experiment 1 showed no evidence for sensitivity to conditional probability, whereas Experiment 2 displayed learning based on joint probability. In Experiment 3, we demonstrated that additional exposure to the input failed to engender sensitivity to conditional probability. However, the results of Experiment 4 showed that a subset of adults-namely, those more successful at identifying actions that had been seen more frequently than comparison sequences-were also successful at learning conditional-probability statistics. These experiments help to clarify the mechanisms subserving processing of intentional action, and they highlight important differences from, as well as similarities to, prior studies of statistical learning in other domains, including language.
Computational Modelling and Simulation Fostering New Approaches in Learning Probability
ERIC Educational Resources Information Center
Kuhn, Markus; Hoppe, Ulrich; Lingnau, Andreas; Wichmann, Astrid
2006-01-01
Discovery learning in mathematics in the domain of probability based on hands-on experiments is normally limited because of the difficulty in providing sufficient materials and data volume in terms of repetitions of the experiments. Our cooperative, computational modelling and simulation environment engages students and teachers in composing and…
Probability Learning: Changes in Behavior Across Time and Development.
Plate, Rista C; Fulvio, Jacqueline M; Shutts, Kristin; Green, C Shawn; Pollak, Seth D
2017-01-25
Individuals track probabilities, such as associations between events in their environments, but less is known about the degree to which experience-within a learning session and over development-influences people's use of incoming probabilistic information to guide behavior in real time. In two experiments, children (4-11 years) and adults searched for rewards hidden in locations with predetermined probabilities. In Experiment 1, children (n = 42) and adults (n = 32) changed strategies to maximize reward receipt over time. However, adults demonstrated greater strategy change efficiency. Making the predetermined probabilities more difficult to learn (Experiment 2) delayed effective strategy change for children (n = 39) and adults (n = 33). Taken together, these data characterize how children and adults alike react flexibly and change behavior according to incoming information.
Reduced reward-related probability learning in schizophrenia patients.
Yılmaz, Alpaslan; Simsek, Fatma; Gonul, Ali Saffet
2012-01-01
Although it is known that individuals with schizophrenia demonstrate marked impairment in reinforcement learning, the details of this impairment are not known. The aim of this study was to test the hypothesis that reward-related probability learning is altered in schizophrenia patients. Twenty-five clinically stable schizophrenia patients and 25 age- and gender-matched controls participated in the study. A simple gambling paradigm was used in which five different cues were associated with different reward probabilities (50%, 67%, and 100%). Participants were asked to make their best guess about the reward probability of each cue. Compared with controls, patients had significant impairment in learning contingencies on the basis of reward-related feedback. The correlation analyses revealed that the impairment of patients partially correlated with the severity of negative symptoms as measured on the Positive and Negative Syndrome Scale but that it was not related to antipsychotic dose. In conclusion, the present study showed that the schizophrenia patients had impaired reward-based learning and that this was independent from their medication status.
ERIC Educational Resources Information Center
Rolison, Jonathan J.; Evans, Jonathan St. B. T.; Dennis, Ian; Walsh, Clare R.
2012-01-01
Multiple cue probability learning (MCPL) involves learning to predict a criterion based on a set of novel cues when feedback is provided in response to each judgment made. But to what extent does MCPL require controlled attention and explicit hypothesis testing? The results of two experiments show that this depends on cue polarity. Learning about…
Supervised learning of probability distributions by neural networks
NASA Technical Reports Server (NTRS)
Baum, Eric B.; Wilczek, Frank
1988-01-01
Supervised learning algorithms for feedforward neural networks are investigated analytically. The back-propagation algorithm described by Werbos (1974), Parker (1985), and Rumelhart et al. (1986) is generalized by redefining the values of the input and output neurons as probabilities. The synaptic weights are then varied to follow gradients in the logarithm of likelihood rather than in the error. This modification is shown to provide a more rigorous theoretical basis for the algorithm and to permit more accurate predictions. A typical application involving a medical-diagnosis expert system is discussed.
Online Reinforcement Learning Using a Probability Density Estimation.
Agostini, Alejandro; Celaya, Enric
2017-01-01
Function approximation in online, incremental, reinforcement learning needs to deal with two fundamental problems: biased sampling and nonstationarity. In this kind of task, biased sampling occurs because samples are obtained from specific trajectories dictated by the dynamics of the environment and are usually concentrated in particular convergence regions, which in the long term tend to dominate the approximation in the less sampled regions. The nonstationarity comes from the recursive nature of the estimations typical of temporal difference methods. This nonstationarity has a local profile, varying not only along the learning process but also along different regions of the state space. We propose to deal with these problems using an estimation of the probability density of samples represented with a gaussian mixture model. To deal with the nonstationarity problem, we use the common approach of introducing a forgetting factor in the updating formula. However, instead of using the same forgetting factor for the whole domain, we make it dependent on the local density of samples, which we use to estimate the nonstationarity of the function at any given input point. To address the biased sampling problem, the forgetting factor applied to each mixture component is modulated according to the new information provided in the updating, rather than forgetting depending only on time, thus avoiding undesired distortions of the approximation in less sampled regions.
Poletiek, Fenna H; Wolters, Gezinus
2009-05-01
Learning local regularities in sequentially structured materials is typically assumed to be based on encoding of the frequencies of these regularities. We explore the view that transitional probabilities between elements of chunks, rather than frequencies of chunks, may be the primary factor in artificial grammar learning (AGL). The transitional probability model (TPM) that we propose is argued to provide an adaptive and parsimonious strategy for encoding local regularities in order to induce sequential structure from an input set of exemplars of the grammar. In a variant of the AGL procedure, in which participants estimated the frequencies of bigrams occurring in a set of exemplars they had been exposed to previously, participants were shown to be more sensitive to local transitional probability information than to mere pattern frequencies.
Not All Probabilities Are Equivalent: Evidence From Orientation Versus Spatial Probability Learning.
Jabar, Syaheed B; Anderson, Britt
2017-02-23
Frequently targets are detected faster, probable locations searched earlier, and likely orientations estimated more precisely. Are these all consequences of a single, domain-general "attentional" effect? To examine this issue, participants were shown brief instances of spatial gratings, and were tasked to draw their location and orientation. Unknown to participants, either the location or orientation probability of these gratings were manipulated. While orientation probability affected the precision of orientation reports, spatial probability did not. Further, utilising lowered stimulus contrast (via a staircase procedure) and a combination of behavioral precision and confidence self-report, we clustered trials with perceived stimuli from trials where the target was not detected: Spatial probability only modulated the likelihood of stimulus detection, but not did not modulate perceptual precision. Even when no physical attentional cues are present, acquired probabilistic information on space versus orientation leads to separable 'attention-like' effects on behaviour. We discuss how this could be linked to distinct underlying neural mechanisms. (PsycINFO Database Record
Judgments of learning index relative confidence, not subjective probability.
Zawadzka, Katarzyna; Higham, Philip A
2015-11-01
The underconfidence-with-practice (UWP) effect is a common finding in calibration studies concerned with judgments of learning (JOLs) elicited on a percentage scale. The UWP pattern is present when, in a procedure consisting of multiple study-test cycles, the mean scale JOLs underestimate the mean recall performance on Cycle 2 and beyond. Although this pattern is present both for items recalled and unrecalled on the preceding cycle, to date research has concentrated mostly on the sources of UWP for the latter type of items. In the present study, we aimed to bridge this gap. In three experiments, we examined calibration on the third of three cycles. The results of Experiment 1 demonstrated the typical pattern of higher recall and scale JOLs for previously recalled items than for unrecalled ones. More importantly, they also revealed that even though the UWP effect was found for items previously recalled both once and twice, its magnitude was greater for the former class of items. Experiments 2 and 3, which employed a binary betting task and a binary 0 %/100 % JOL task, respectively, demonstrated that people can accurately predict future recall for previously recalled items with binary decisions. In both experiments, the UWP effect was absent for both items recalled once and twice. We suggest that the sensitivity of scale JOLs, but not binary judgments, to the number of previous recall successes strengthens the claim of Hanczakowski, Zawadzka, Pasek, and Higham (Journal of Memory and Language 69:429-444, 2013) that scale JOLs reflect confidence in, rather than the subjective probability of, future recall.
A Mathematical Microworld for Students to Learn Introductory Probability.
ERIC Educational Resources Information Center
Jiang, Zhonghong; Potter, Walter D.
1993-01-01
Describes the Microworld Chance, a simulation-oriented computer environment that allows students to explore probability concepts in five subenvironments: coins, dice, spinners, thumbtacks, and marbles. Results of a teaching experiment to examine the effectiveness of the microworld in changing students' misconceptions about probability are…
Paradoxes and Counterexamples in Teaching and Learning of Probability at University
ERIC Educational Resources Information Center
Klymchuk, Sergiy; Kachapova, Farida
2012-01-01
This article is devoted to practical aspects of teaching and learning of probability at university. It presents the difficulties and attitudes of first-year university science and engineering students towards using paradoxes and counterexamples as a pedagogical strategy in teaching and learning of probability. It also presents a student's point of…
Franceschetti, Donald R; Gire, Elizabeth
2013-06-01
Quantum probability theory offers a viable alternative to classical probability, although there are some ambiguities inherent in transferring the quantum formalism to a less determined realm. A number of physicists are now looking at the applicability of quantum ideas to the assessment of physics learning, an area particularly suited to quantum probability ideas.
[Stochastic simulation of the instrumental reflex in probability learning].
Saltykov, A B; Smirnov, I V; Starshov, V P
1989-01-01
A method of computer imitation modelling is worked out of the process of instrumental reflex elaboration allowing to prognosticate the rate of learning at various combinations of probabilistic medium parameters and individual properties of the learning subject. By means of imitating modelling it is easy to find out the localization of the optima and pessima zones in the space of parameters influencing the learning. For building the model the empiric data are not required--they are used only for checking the obtained results. Therefore the conformity obtained with the literature data and the results of own studies allows to suggest that firstly, the worked out model possesses a good forecasting power and secondly, it can be used for studying such conditions of learning for which the appropriate experimental material is not yet collected.
The feedback-related negativity is modulated by feedback probability in observational learning.
Kobza, Stefan; Thoma, Patrizia; Daum, Irene; Bellebaum, Christian
2011-12-01
The feedback-related negativity (FRN), an event-related potentials (ERPs) component reflecting activity of the anterior cingulate cortex (ACC), has been shown to be modulated by feedback expectancy following active choices in feedback-based learning tasks. A general reduction of FRN amplitude has been described in observational feedback learning, raising the question whether FRN amplitude is modulated in a similar way in this type of learning. The present study investigated whether the FRN and the P300 - a second ERP component related to feedback processing - are modulated by feedback probability in observational learning. Thirty-two subjects participated in the experiment. They observed a virtual person choosing between two symbols and receiving positive or negative feedback. Learning about stimulus-specific feedback probabilities was assessed in active test trials without feedback. In addition, the bias to learn from positive or negative feedback and - in a subsample of 17 subjects - empathy scores were obtained. General FRN and P300 modulations by feedback probability were found across all subjects. Only for the FRN in learners, an interaction between probability and valence was observed. Larger FRN amplitudes for negative relative to positive feedback only emerged for the lowest outcome probability. The results show that feedback expectancy modulates FRN amplitude also in observational learning, suggesting a similar ACC function as in active learning. On the other hand, the modulation is only seen for very low feedback expectancy, which suggests that brain regions other than those of the reward system contribute to feedback processing in an observation setting.
Illustrating Probability in Genetics with Hands-On Learning: Making the Math Real
ERIC Educational Resources Information Center
Pierce, Benjamin A.; Honeycutt, Brenda B.
2007-01-01
Probability is an essential tool for understanding heredity and modern genetics, yet many students have difficulty with this topic due to the abstract and quantitative nature of the subject. To facilitate student learning of probability in genetics, we have developed a set of hands-on, cooperative activities that allow students to determine…
ERIC Educational Resources Information Center
Marcovitch, Stuart; Lewkowicz, David J.
2009-01-01
The ability to perceive sequences is fundamental to cognition. Previous studies have shown that infants can learn visual sequences as early as 2 months of age and it has been suggested that this ability is mediated by sensitivity to conditional probability information. Typically, conditional probability information has covaried with frequency…
ERIC Educational Resources Information Center
Koparan, Timur; Yilmaz, Gül Kaleli
2015-01-01
The effect of simulation-based probability teaching on the prospective teachers' inference skills has been examined with this research. In line with this purpose, it has been aimed to examine the design, implementation and efficiency of a learning environment for experimental probability. Activities were built on modeling, simulation and the…
Probability and Statistics in Astronomical Machine Learning and Data Minin
NASA Astrophysics Data System (ADS)
Scargle, Jeffrey
2012-03-01
Statistical issues peculiar to astronomy have implications for machine learning and data mining. It should be obvious that statistics lies at the heart of machine learning and data mining. Further it should be no surprise that the passive observational nature of astronomy, the concomitant lack of sampling control, and the uniqueness of its realm (the whole universe!) lead to some special statistical issues and problems. As described in the Introduction to this volume, data analysis technology is largely keeping up with major advances in astrophysics and cosmology, even driving many of them. And I realize that there are many scientists with good statistical knowledge and instincts, especially in the modern era I like to call the Age of Digital Astronomy. Nevertheless, old impediments still lurk, and the aim of this chapter is to elucidate some of them. Many experiences with smart people doing not-so-smart things (cf. the anecdotes collected in the Appendix here) have convinced me that the cautions given here need to be emphasized. Consider these four points: 1. Data analysis often involves searches of many cases, for example, outcomes of a repeated experiment, for a feature of the data. 2. The feature comprising the goal of such searches may not be defined unambiguously until the search is carried out, or perhaps vaguely even then. 3. The human visual system is very good at recognizing patterns in noisy contexts. 4. People are much easier to convince of something they want to believe, or already believe, as opposed to unpleasant or surprising facts. One can argue that all four are good things during the initial, exploratory phases of most data analysis. They represent the curiosity and creativity of the scientific process, especially during the exploration of data collections from new observational programs such as all-sky surveys in wavelengths not accessed before or sets of images of a planetary surface not yet explored. On the other hand, confirmatory scientific
Word Learning by Preschoolers with SLI: Effect of Phonotactic Probability and Object Familiarity
ERIC Educational Resources Information Center
Gray, Shelley; Brinkley, Shara; Svetina, Dubravka
2012-01-01
Purpose: In this study, the authors investigated whether previous findings of a low phonotactic probability/unfamiliar object word-learning advantage in preschoolers could be replicated, whether this advantage would be apparent at different "stages" of word learning, and whether findings would differ for preschoolers with specific language…
Learning Probabilities in Computer Engineering by Using a Competency- and Problem-Based Approach
ERIC Educational Resources Information Center
Khoumsi, Ahmed; Hadjou, Brahim
2005-01-01
Our department has redesigned its electrical and computer engineering programs by adopting a learning methodology based on competence development, problem solving, and the realization of design projects. In this article, we show how this pedagogical approach has been successfully used for learning probabilities and their application to computer…
Pure perceptual-based learning of second-, third-, and fourth-order sequential probabilities.
Remillard, Gilbert
2011-07-01
There is evidence that sequence learning in the traditional serial reaction time task (SRTT), where target location is the response dimension, and sequence learning in the perceptual SRTT, where target location is not the response dimension, are handled by different mechanisms. The ability of the latter mechanism to learn sequential contingencies that can be learned by the former mechanism was examined. Prior research has established that people can learn second-, third-, and fourth-order probabilities in the traditional SRTT. The present study reveals that people can learn such probabilities in the perceptual SRTT. This suggests that the two mechanisms may have similar architectures. A possible neural basis of the two mechanisms is discussed.
van Lamsweerde, Amanda E; Beck, Melissa R
2011-12-01
Statistical properties in the visual environment can be used to improve performance on visual working memory (VWM) tasks. The current study examined the ability to incidentally learn that a change is more likely to occur to a particular feature dimension (shape, color, or location) and use this information to improve change detection performance for that dimension (the change probability effect). Participants completed a change detection task in which one change type was more probable than others. Change probability effects were found for color and shape changes, but not location changes, and intentional strategies did not improve the effect. Furthermore, the change probability effect developed and adapted to new probability information quickly. Finally, in some conditions, an improvement in change detection performance for a probable change led to an impairment in change detection for improbable changes.
Kruppa, Jochen; Liu, Yufeng; Biau, Gérard; Kohler, Michael; König, Inke R; Malley, James D; Ziegler, Andreas
2014-07-01
Probability estimation for binary and multicategory outcome using logistic and multinomial logistic regression has a long-standing tradition in biostatistics. However, biases may occur if the model is misspecified. In contrast, outcome probabilities for individuals can be estimated consistently with machine learning approaches, including k-nearest neighbors (k-NN), bagged nearest neighbors (b-NN), random forests (RF), and support vector machines (SVM). Because machine learning methods are rarely used by applied biostatisticians, the primary goal of this paper is to explain the concept of probability estimation with these methods and to summarize recent theoretical findings. Probability estimation in k-NN, b-NN, and RF can be embedded into the class of nonparametric regression learning machines; therefore, we start with the construction of nonparametric regression estimates and review results on consistency and rates of convergence. In SVMs, outcome probabilities for individuals are estimated consistently by repeatedly solving classification problems. For SVMs we review classification problem and then dichotomous probability estimation. Next we extend the algorithms for estimating probabilities using k-NN, b-NN, and RF to multicategory outcomes and discuss approaches for the multicategory probability estimation problem using SVM. In simulation studies for dichotomous and multicategory dependent variables we demonstrate the general validity of the machine learning methods and compare it with logistic regression. However, each method fails in at least one simulation scenario. We conclude with a discussion of the failures and give recommendations for selecting and tuning the methods. Applications to real data and example code are provided in a companion article (doi:10.1002/bimj.201300077).
van Lamsweerde, Amanda E; Beck, Melissa R
2015-12-01
In this study, we investigated whether the ability to learn probability information is affected by the type of representation held in visual working memory. Across 4 experiments, participants detected changes to displays of coloured shapes. While participants detected changes in 1 dimension (e.g., colour), a feature from a second, nonchanging dimension (e.g., shape) predicted which object was most likely to change. In Experiments 1 and 3, items could be grouped by similarity in the changing dimension across items (e.g., colours and shapes were repeated in the display), while in Experiments 2 and 4 items could not be grouped by similarity (all features were unique). Probability information from the predictive dimension was learned and used to increase performance, but only when all of the features within a display were unique (Experiments 2 and 4). When it was possible to group by feature similarity in the changing dimension (e.g., 2 blue objects appeared within an array), participants were unable to learn probability information and use it to improve performance (Experiments 1 and 3). The results suggest that probability information can be learned in a dimension that is not explicitly task-relevant, but only when the probability information is represented with the changing dimension in visual working memory.
ERIC Educational Resources Information Center
Hoover, Jill R.; Storkel, Holly L.; Hogan, Tiffany P.
2010-01-01
Two experiments examined the effects of phonotactic probability and neighborhood density on word learning by 3-, 4-, and 5-year-old children. Nonwords orthogonally varying in probability and density were taught with learning and retention measured via picture naming. Experiment 1 used a within story probability/across story density exposure…
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Storkel, Holly L.; Hoover, Jill R.
2010-01-01
This study examined the ability of 20 preschool children with functional phonological delays and 34 age- and vocabulary-matched typical children to learn words differing in phonotactic probability (i.e., the likelihood of occurrence of a sound sequence) and neighborhood density (i.e., the number of words that differ from a target by one phoneme).…
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Heisler, Lori; Goffman, Lisa
2016-01-01
A word learning paradigm was used to teach children novel words that varied in phonotactic probability and neighborhood density. The effects of frequency and density on speech production were examined when phonetic forms were nonreferential (i.e., when no referent was attached) and when phonetic forms were referential (i.e., when a referent was…
Blind Students' Learning of Probability through the Use of a Tactile Model
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Vita, Aida Carvalho; Kataoka, Verônica Yumi
2014-01-01
The objective of this paper is to discuss how blind students learn basic concepts of probability using the tactile model proposed by Vita (2012). Among the activities were part of the teaching sequence "Jefferson's Random Walk", in which students built a tree diagram (using plastic trays, foam cards, and toys), and pictograms in 3D…
Influence of Phonotactic Probability/Neighbourhood Density on Lexical Learning in Late Talkers
ERIC Educational Resources Information Center
MacRoy-Higgins, Michelle; Schwartz, Richard G.; Shafer, Valerie L.; Marton, Klara
2013-01-01
Background: Toddlers who are late talkers demonstrate delays in phonological and lexical skills. However, the influence of phonological factors on lexical acquisition in toddlers who are late talkers has not been examined directly. Aims: To examine the influence of phonotactic probability/neighbourhood density on word learning in toddlers who were…
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Fong, Soon Fook; Por, Fei Ping; Tang, Ai Ling
2012-01-01
The purpose of this study was to investigate the effects of multiple simulation presentation in interactive multimedia are on the achievement of students with different levels of anxiety in the learning of Probability. The interactive multimedia courseware was developed in two different modes, which were Multiple Simulation Presentation (MSP) and…
Teaching Probability to Pre-Service Teachers with Argumentation Based Science Learning Approach
ERIC Educational Resources Information Center
Can, Ömer Sinan; Isleyen, Tevfik
2016-01-01
The aim of this study is to explore the effects of the argumentation based science learning (ABSL) approach on the teaching probability to pre-service teachers. The sample of the study included 41 students studying at the Department of Elementary School Mathematics Education in a public university during the 2014-2015 academic years. The study is…
Goschy, Harriet; Bakos, Sarolta; Müller, Hermann J; Zehetleitner, Michael
2014-01-01
Targets in a visual search task are detected faster if they appear in a probable target region as compared to a less probable target region, an effect which has been termed "probability cueing." The present study investigated whether probability cueing cannot only speed up target detection, but also minimize distraction by distractors in probable distractor regions as compared to distractors in less probable distractor regions. To this end, three visual search experiments with a salient, but task-irrelevant, distractor ("additional singleton") were conducted. Experiment 1 demonstrated that observers can utilize uneven spatial distractor distributions to selectively reduce interference by distractors in frequent distractor regions as compared to distractors in rare distractor regions. Experiments 2 and 3 showed that intertrial facilitation, i.e., distractor position repetitions, and statistical learning (independent of distractor position repetitions) both contribute to the probability cueing effect for distractor locations. Taken together, the present results demonstrate that probability cueing of distractor locations has the potential to serve as a strong attentional cue for the shielding of likely distractor locations.
Deep learning of support vector machines with class probability output networks.
Kim, Sangwook; Yu, Zhibin; Kil, Rhee Man; Lee, Minho
2015-04-01
Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically is increasingly important as the volume of data and range of applications of machine learning methods continues to grow. This paper proposes a new deep architecture that uses support vector machines (SVMs) with class probability output networks (CPONs) to provide better generalization power for pattern classification problems. As a result, deep features are extracted without additional feature engineering steps, using multiple layers of the SVM classifiers with CPONs. The proposed structure closely approaches the ideal Bayes classifier as the number of layers increases. Using a simulation of classification problems, the effectiveness of the proposed method is demonstrated.
Heisler, Lori; Goffman, Lisa
2016-01-01
A word learning paradigm was used to teach children novel words that varied in phonotactic probability and neighborhood density. The effects of frequency and density on speech production were examined when phonetic forms were non-referential (i.e., when no referent was attached) and when phonetic forms were referential (i.e., when a referent was attached through fast mapping). Two methods of analysis were included: (1) kinematic variability of speech movement patterning; and (2) measures of segmental accuracy. Results showed that phonotactic frequency influenced the stability of movement patterning whereas neighborhood density influenced phoneme accuracy. Motor learning was observed in both non-referential and referential novel words. Forms with low phonotactic probability and low neighborhood density showed a word learning effect when a referent was assigned during fast mapping. These results elaborate on and specify the nature of interactivity observed across lexical, phonological, and articulatory domains. PMID:27284274
Heisler, Lori; Goffman, Lisa
A word learning paradigm was used to teach children novel words that varied in phonotactic probability and neighborhood density. The effects of frequency and density on speech production were examined when phonetic forms were non-referential (i.e., when no referent was attached) and when phonetic forms were referential (i.e., when a referent was attached through fast mapping). Two methods of analysis were included: (1) kinematic variability of speech movement patterning; and (2) measures of segmental accuracy. Results showed that phonotactic frequency influenced the stability of movement patterning whereas neighborhood density influenced phoneme accuracy. Motor learning was observed in both non-referential and referential novel words. Forms with low phonotactic probability and low neighborhood density showed a word learning effect when a referent was assigned during fast mapping. These results elaborate on and specify the nature of interactivity observed across lexical, phonological, and articulatory domains.
Bogaerts, Louisa; Siegelman, Noam; Frost, Ram
2016-08-01
What determines individuals' efficacy in detecting regularities in visual statistical learning? Our theoretical starting point assumes that the variance in performance of statistical learning (SL) can be split into the variance related to efficiency in encoding representations within a modality and the variance related to the relative computational efficiency of detecting the distributional properties of the encoded representations. Using a novel methodology, we dissociated encoding from higher-order learning factors, by independently manipulating exposure duration and transitional probabilities in a stream of visual shapes. Our results show that the encoding of shapes and the retrieving of their transitional probabilities are not independent and additive processes, but interact to jointly determine SL performance. The theoretical implications of these findings for a mechanistic explanation of SL are discussed.
Gruber, Susan; Logan, Roger W; Jarrín, Inmaculada; Monge, Susana; Hernán, Miguel A
2015-01-15
Inverse probability weights used to fit marginal structural models are typically estimated using logistic regression. However, a data-adaptive procedure may be able to better exploit information available in measured covariates. By combining predictions from multiple algorithms, ensemble learning offers an alternative to logistic regression modeling to further reduce bias in estimated marginal structural model parameters. We describe the application of two ensemble learning approaches to estimating stabilized weights: super learning (SL), an ensemble machine learning approach that relies on V-fold cross validation, and an ensemble learner (EL) that creates a single partition of the data into training and validation sets. Longitudinal data from two multicenter cohort studies in Spain (CoRIS and CoRIS-MD) were analyzed to estimate the mortality hazard ratio for initiation versus no initiation of combined antiretroviral therapy among HIV positive subjects. Both ensemble approaches produced hazard ratio estimates further away from the null, and with tighter confidence intervals, than logistic regression modeling. Computation time for EL was less than half that of SL. We conclude that ensemble learning using a library of diverse candidate algorithms offers an alternative to parametric modeling of inverse probability weights when fitting marginal structural models. With large datasets, EL provides a rich search over the solution space in less time than SL with comparable results.
Web-based experiments controlled by JavaScript: an example from probability learning.
Birnbaum, Michael H; Wakcher, Sandra V
2002-05-01
JavaScript programs can be used to control Web experiments. This technique is illustrated by an experiment that tested the effects of advice on performance in the classic probability-learning paradigm. Previous research reported that people tested via the Web or in the lab tended to match the probabilities of their responses to the probabilities that those responses would be reinforced. The optimal strategy, however, is to consistently choose the more frequent event; probability matching produces suboptimal performance. We investigated manipulations we reasoned should improve performance. A horse race scenario in which participants predicted the winner in each of a series of races between two horses was compared with an abstract scenario used previously. Ten groups of learners received different amounts of advice, including all combinations of (1) explicit instructions concerning the optimal strategy, (2) explicit instructions concerning a monetary sum to maximize, and (3) accurate information concerning the probabilities of events. The results showed minimal effects of horse race versus abstract scenario. Both advice concerning the optimal strategy and probability information contributed significantly to performance in the task. This paper includes a brief tutorial on JavaScript, explaining with simple examples how to assemble a browser-based experiment.
ERIC Educational Resources Information Center
Mirman, Daniel; Estes, Katharine Graf; Magnuson, James S.
2010-01-01
Statistical learning mechanisms play an important role in theories of language acquisition and processing. Recurrent neural network models have provided important insights into how these mechanisms might operate. We examined whether such networks capture two key findings in human statistical learning. In Simulation 1, a simple recurrent network…
Human brainstem plasticity: the interaction of stimulus probability and auditory learning.
Skoe, Erika; Chandrasekaran, Bharath; Spitzer, Emily R; Wong, Patrick C M; Kraus, Nina
2014-03-01
Two forms of brainstem plasticity are known to occur: an immediate stimulus probability-based and learning-dependent plasticity. Whether these kinds of plasticity interact is unknown. We examined this question in a training experiment involving three phases: (1) an initial baseline measurement, (2) a 9-session training paradigm, and (3) a retest measurement. At the outset of the experiment, auditory brainstem responses (ABR) were recorded to two unfamiliar pitch patterns presented in an oddball paradigm. Then half the participants underwent sound-to-meaning training where they learned to match these pitch patterns to novel words, with the remaining participants serving as controls who received no auditory training. Nine days after the baseline measurement, the pitch patterns were re-presented to all participants using the same oddball paradigm. Analysis of the baseline recordings revealed an effect of probability: when a sound was presented infrequently, the pitch contour was represented less accurately in the ABR than when it was presented frequently. After training, pitch tracking was more accurate for infrequent sounds, particularly for the pitch pattern that was encoded more poorly pre-training. However, the control group was stable over the same interval. Our results provide evidence that probability-based and learning-dependent plasticity interact in the brainstem.
More than words: Adults learn probabilities over categories and relationships between them
Hudson Kam, Carla L.
2009-01-01
This study examines whether human learners can acquire statistics over abstract categories and their relationships to each other. Adult learners were exposed to miniature artificial languages containing variation in the ordering of the Subject, Object, and Verb constituents. Different orders (e.g. SOV, VSO) occurred in the input with different frequencies, but the occurrence of one order versus another was not predictable. Importantly, the language was constructed such that participants could only match the overall input probabilities if they were tracking statistics over abstract categories, not over individual words. At test, participants reproduced the probabilities present in the input with a high degree of accuracy. Closer examination revealed that learner’s were matching the probabilities associated with individual verbs rather than the category as a whole. However, individual nouns had no impact on word orders produced. Thus, participants learned the probabilities of a particular ordering of the abstract grammatical categories Subject and Object associated with each verb. Results suggest that statistical learning mechanisms are capable of tracking relationships between abstract linguistic categories in addition to individual items. PMID:20161375
Hall-McMaster, Sam; Millar, Jessica; Ruan, Ming; Ward, Ryan D
2017-02-01
It has recently been recognized that orbitofrontal cortex has 2 subdivisions that are anatomically and functionally distinct. Most rodent research has focused on the lateral subdivision, leaving the medial subdivision (mOFC) relatively unexplored. We recently showed that inhibiting mOFC neurons eliminated the differential impact of reward probability cues on discrimination accuracy in a sustained attention task. In the present study, we tested whether increasing mOFC neuronal activity in rats would accelerate acquisition of reward contingencies. mOFC neuronal activity was increased using the DREADD (Designer Receptors Exclusively Activated by Designer Drugs) method, in which clozapine-N-oxide administration leads to neuronal modulation by acting on synthetic receptors not normally expressed in the rat brain. We predicted that rats with neuronal activation in mOFC would require fewer sessions than controls for acquisition of a task in which visual cues signal the probability of reward for correct discrimination performance. Contrary to this prediction, mOFC neuronal activation impaired task acquisition, suggesting mOFC may play a role in learning relationships between environmental cues and reward probability or for using that information in adaptive decision-making. In addition, disrupted mOFC activity may contribute to psychiatric conditions in which learning associations between environmental cues and reward probability is impaired. (PsycINFO Database Record
Xu Chengjian; Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van't
2012-03-15
Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.
Choice as a function of reinforcer "hold": from probability learning to concurrent reinforcement.
Jensen, Greg; Neuringer, Allen
2008-10-01
Two procedures commonly used to study choice are concurrent reinforcement and probability learning. Under concurrent-reinforcement procedures, once a reinforcer is scheduled, it remains available indefinitely until collected. Therefore reinforcement becomes increasingly likely with passage of time or responses on other operanda. Under probability learning, reinforcer probabilities are constant and independent of passage of time or responses. Therefore a particular reinforcer is gained or not, on the basis of a single response, and potential reinforcers are not retained, as when betting at a roulette wheel. In the "real" world, continued availability of reinforcers often lies between these two extremes, with potential reinforcers being lost owing to competition, maturation, decay, and random scatter. The authors parametrically manipulated the likelihood of continued reinforcer availability, defined as hold, and examined the effects on pigeons' choices. Choices varied as power functions of obtained reinforcers under all values of hold. Stochastic models provided generally good descriptions of choice emissions with deviations from stochasticity systematically related to hold. Thus, a single set of principles accounted for choices across hold values that represent a wide range of real-world conditions.
Kruppa, Jochen; Liu, Yufeng; Diener, Hans-Christian; Holste, Theresa; Weimar, Christian; König, Inke R; Ziegler, Andreas
2014-07-01
Machine learning methods are applied to three different large datasets, all dealing with probability estimation problems for dichotomous or multicategory data. Specifically, we investigate k-nearest neighbors, bagged nearest neighbors, random forests for probability estimation trees, and support vector machines with the kernels of Bessel, linear, Laplacian, and radial basis type. Comparisons are made with logistic regression. The dataset from the German Stroke Study Collaboration with dichotomous and three-category outcome variables allows, in particular, for temporal and external validation. The other two datasets are freely available from the UCI learning repository and provide dichotomous outcome variables. One of them, the Cleveland Clinic Foundation Heart Disease dataset, uses data from one clinic for training and from three clinics for external validation, while the other, the thyroid disease dataset, allows for temporal validation by separating data into training and test data by date of recruitment into study. For dichotomous outcome variables, we use receiver operating characteristics, areas under the curve values with bootstrapped 95% confidence intervals, and Hosmer-Lemeshow-type figures as comparison criteria. For dichotomous and multicategory outcomes, we calculated bootstrap Brier scores with 95% confidence intervals and also compared them through bootstrapping. In a supplement, we provide R code for performing the analyses and for random forest analyses in Random Jungle, version 2.1.0. The learning machines show promising performance over all constructed models. They are simple to apply and serve as an alternative approach to logistic or multinomial logistic regression analysis.
A Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.
Geng, Yuan; Lu, Wenbin; Zhang, Hao Helen
2014-01-01
Risk classification and survival probability prediction are two major goals in survival data analysis since they play an important role in patients' risk stratification, long-term diagnosis, and treatment selection. In this article, we propose a new model-free machine learning framework for risk classification and survival probability prediction based on weighted support vector machines. The new procedure does not require any specific parametric or semiparametric model assumption on data, and is therefore capable of capturing nonlinear covariate effects. We use numerous simulation examples to demonstrate finite sample performance of the proposed method under various settings. Applications to a glioma tumor data and a breast cancer gene expression survival data are shown to illustrate the new methodology in real data analysis.
Koelsch, Stefan; Busch, Tobias; Jentschke, Sebastian; Rohrmeier, Martin
2016-01-01
Within the framework of statistical learning, many behavioural studies investigated the processing of unpredicted events. However, surprisingly few neurophysiological studies are available on this topic, and no statistical learning experiment has investigated electroencephalographic (EEG) correlates of processing events with different transition probabilities. We carried out an EEG study with a novel variant of the established statistical learning paradigm. Timbres were presented in isochronous sequences of triplets. The first two sounds of all triplets were equiprobable, while the third sound occurred with either low (10%), intermediate (30%), or high (60%) probability. Thus, the occurrence probability of the third item of each triplet (given the first two items) was varied. Compared to high-probability triplet endings, endings with low and intermediate probability elicited an early anterior negativity that had an onset around 100 ms and was maximal at around 180 ms. This effect was larger for events with low than for events with intermediate probability. Our results reveal that, when predictions are based on statistical learning, events that do not match a prediction evoke an early anterior negativity, with the amplitude of this mismatch response being inversely related to the probability of such events. Thus, we report a statistical mismatch negativity (sMMN) that reflects statistical learning of transitional probability distributions that go beyond auditory sensory memory capabilities. PMID:26830652
Koelsch, Stefan; Busch, Tobias; Jentschke, Sebastian; Rohrmeier, Martin
2016-02-02
Within the framework of statistical learning, many behavioural studies investigated the processing of unpredicted events. However, surprisingly few neurophysiological studies are available on this topic, and no statistical learning experiment has investigated electroencephalographic (EEG) correlates of processing events with different transition probabilities. We carried out an EEG study with a novel variant of the established statistical learning paradigm. Timbres were presented in isochronous sequences of triplets. The first two sounds of all triplets were equiprobable, while the third sound occurred with either low (10%), intermediate (30%), or high (60%) probability. Thus, the occurrence probability of the third item of each triplet (given the first two items) was varied. Compared to high-probability triplet endings, endings with low and intermediate probability elicited an early anterior negativity that had an onset around 100 ms and was maximal at around 180 ms. This effect was larger for events with low than for events with intermediate probability. Our results reveal that, when predictions are based on statistical learning, events that do not match a prediction evoke an early anterior negativity, with the amplitude of this mismatch response being inversely related to the probability of such events. Thus, we report a statistical mismatch negativity (sMMN) that reflects statistical learning of transitional probability distributions that go beyond auditory sensory memory capabilities.
Storkel, Holly L.; Bontempo, Daniel E.; Aschenbrenner, Andrew J.; Maekawa, Junko; Lee, Su-Yeon
2013-01-01
Purpose Phonotactic probability or neighborhood density have predominately been defined using gross distinctions (i.e., low vs. high). The current studies examined the influence of finer changes in probability (Experiment 1) and density (Experiment 2) on word learning. Method The full range of probability or density was examined by sampling five nonwords from each of four quartiles. Three- and 5-year-old children received training on nonword-nonobject pairs. Learning was measured in a picture-naming task immediately following training and 1-week after training. Results were analyzed using multi-level modeling. Results A linear spline model best captured nonlinearities in phonotactic probability. Specifically word learning improved as probability increased in the lowest quartile, worsened as probability increased in the midlow quartile, and then remained stable and poor in the two highest quartiles. An ordinary linear model sufficiently described neighborhood density. Here, word learning improved as density increased across all quartiles. Conclusion Given these different patterns, phonotactic probability and neighborhood density appear to influence different word learning processes. Specifically, phonotactic probability may affect recognition that a sound sequence is an acceptable word in the language and is a novel word for the child, whereas neighborhood density may influence creation of a new representation in long-term memory. PMID:23882005
Binder, Harald
2014-07-01
This is a discussion of the following papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler.
ERIC Educational Resources Information Center
Storkel, Holly L.; Bontempo, Daniel E.; Aschenbrenner, Andrew J.; Maekawa, Junko; Lee, Su-Yeon
2013-01-01
Purpose: Phonotactic probability or neighborhood density has predominately been defined through the use of gross distinctions (i.e., low vs. high). In the current studies, the authors examined the influence of finer changes in probability (Experiment 1) and density (Experiment 2) on word learning. Method: The authors examined the full range of…
Curiosity and demographic factors as determinants of children's probability-learning strategies.
Kreitler, S; Zigler, E; Kreitler, H
1984-09-01
Children's curiosity, gender, activity level, and socioeconomic status (SES) were related to their performance on a partially reinforced discrimination-learning task. The 38 boys and 37 girls were in the first grade and were all white. Three factors of curiosity (manipulatory, conceptual, and about the complex) were assessed. Performance on the learning task was scored for the number of correct responses (maximizing) and for the frequency of three-step sequences reflecting variability, systematic patterning, and perseveration. In general, the three curiosity factors related negatively to maximizing and perseveration and positively to variability. (The same effects were found for activity level.) Systematic patterning related positively to one curiosity type and negatively to another. Girls used less maximizing and more systematic patterning than boys. The response choices of girls were affected more by differences in conceptual curiosity and those of boys by differences in curiosity about the complex. Activity level was unrelated to gender but differed with SES. The findings demonstrate the role of different curiosity factors in shaping response sequences and suggest some reasons for children's choice of probability-learning strategies.
NASA Astrophysics Data System (ADS)
Cavuoti, S.; Amaro, V.; Brescia, M.; Vellucci, C.; Tortora, C.; Longo, G.
2017-02-01
A variety of fundamental astrophysical science topics require the determination of very accurate photometric redshifts (photo-z). A wide plethora of methods have been developed, based either on template models fitting or on empirical explorations of the photometric parameter space. Machine-learning-based techniques are not explicitly dependent on the physical priors and able to produce accurate photo-z estimations within the photometric ranges derived from the spectroscopic training set. These estimates, however, are not easy to characterize in terms of a photo-z probability density function (PDF), due to the fact that the analytical relation mapping the photometric parameters on to the redshift space is virtually unknown. We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method designed to provide a reliable PDF of the error distribution for empirical techniques. The method is implemented as a modular workflow, whose internal engine for photo-z estimation makes use of the MLPQNA neural network (Multi Layer Perceptron with Quasi Newton learning rule), with the possibility to easily replace the specific machine-learning model chosen to predict photo-z. We present a summary of results on SDSS-DR9 galaxy data, used also to perform a direct comparison with PDFs obtained by the LE PHARE spectral energy distribution template fitting. We show that METAPHOR is capable to estimate the precision and reliability of photometric redshifts obtained with three different self-adaptive techniques, i.e. MLPQNA, Random Forest and the standard K-Nearest Neighbors models.
Fall risk probability estimation based on supervised feature learning using public fall datasets.
Koshmak, Gregory A; Linden, Maria; Loutfi, Amy
2016-08-01
Risk of falling is considered among major threats for elderly population and therefore started to play an important role in modern healthcare. With recent development of sensor technology, the number of studies dedicated to reliable fall detection system has increased drastically. However, there is still a lack of universal approach regarding the evaluation of developed algorithms. In the following study we make an attempt to find publicly available fall datasets and analyze similarities among them using supervised learning. After preforming similarity assessment based on multidimensional scaling we indicate the most representative feature vector corresponding to each specific dataset. This vector obtained from a real-life data is subsequently deployed to estimate fall risk probabilities for a statistical fall detection model. Finally, we conclude with some observations regarding the similarity assessment results and provide suggestions towards an efficient approach for evaluation of fall detection studies.
Learning concepts of fractals and probability by “doing science”
NASA Astrophysics Data System (ADS)
Stanley, H. Eugene
1989-09-01
Very recent advances in computer technology provide the power of mainframe systems in relatively compact and inexpensive personal computers; soon the computing power of even a supercomputer will be available on a desktop at a price comparable to today's personal computers. Over the next decade this tremendous computing power can and probably will become available in schools throughout the world. Here we discuss the possibility of harnessing this new technological resource as a teaching tool for specific topics in mathematics and science, focusing on random processes in nature and their deep connection to concepts in probability and fractal geometry. Such natural phenomena as the growth of snowflakes via random aggregation and the disordered geometric configurations of polymer chains demonstrate that fundamentally random microscopic processes can give rise to predictable macroscopic behaviors. They also give rise to random fractal structures of inherent interest and great beauty. Because it is impossible to view the underlying processes directly, computer simulation and visualization is an indispensable tool for understanding and studying these phenomena. In the process of “doing science” with both hands-on experiments and computer simulations, students would learn abstract mathematical concepts in a context which is at once concrete and inherently motivating. Furthermore, the techniques they could employ would mirror in most respects those in current use by researchers, thus forging an unprecedented link between this curriculum and the professional worlds of science and mathematics.
Luftig, R L
1982-01-01
A pervasive problem for educators of the severely mentally retarded is language training. In spite of extensive oral language training, many severely mentally retarded individuals never acquire functional oral language. Many of these clients, however, are able to acquire sign language communication skills. The present article discusses sign language learning in terms of learner attributes, production variables in sign, and the referential concepts which the signs represent. More specifically, it is hypothesized that by taking into account variables such as sign translucency, referential concreteness, learning readiness, and by externally organizing the signs to be learned along visual continuums, the probability of sign learning by severely mentally retarded individuals can be increased.
d'Acremont, Mathieu; Fornari, Eleonora; Bossaerts, Peter
2013-01-01
In an uncertain environment, probabilities are key to predicting future events and making adaptive choices. However, little is known about how humans learn such probabilities and where and how they are encoded in the brain, especially when they concern more than two outcomes. During functional magnetic resonance imaging (fMRI), young adults learned the probabilities of uncertain stimuli through repetitive sampling. Stimuli represented payoffs and participants had to predict their occurrence to maximize their earnings. Choices indicated loss and risk aversion but unbiased estimation of probabilities. BOLD response in medial prefrontal cortex and angular gyri increased linearly with the probability of the currently observed stimulus, untainted by its value. Connectivity analyses during rest and task revealed that these regions belonged to the default mode network. The activation of past outcomes in memory is evoked as a possible mechanism to explain the engagement of the default mode network in probability learning. A BOLD response relating to value was detected only at decision time, mainly in striatum. It is concluded that activity in inferior parietal and medial prefrontal cortex reflects the amount of evidence accumulated in favor of competing and uncertain outcomes.
Decision forests for learning prostate cancer probability maps from multiparametric MRI
NASA Astrophysics Data System (ADS)
Ehrenberg, Henry R.; Cornfeld, Daniel; Nawaf, Cayce B.; Sprenkle, Preston C.; Duncan, James S.
2016-03-01
Objectives: Advances in multiparametric magnetic resonance imaging (mpMRI) and ultrasound/MRI fusion imaging offer a powerful alternative to the typical undirected approach to diagnosing prostate cancer. However, these methods require the time and expertise needed to interpret mpMRI image scenes. In this paper, a machine learning framework for automatically detecting and localizing cancerous lesions within the prostate is developed and evaluated. Methods: Two studies were performed to gather MRI and pathology data. The 12 patients in the first study underwent an MRI session to obtain structural, diffusion-weighted, and dynamic contrast enhanced image vol- umes of the prostate, and regions suspected of being cancerous from the MRI data were manually contoured by radiologists. Whole-mount slices of the prostate were obtained for the patients in the second study, in addition to structural and diffusion-weighted MRI data, for pathology verification. A 3-D feature set for voxel-wise appear- ance description combining intensity data, textural operators, and zonal approximations was generated. Voxels in a test set were classified as normal or cancer using a decision forest-based model initialized using Gaussian discriminant analysis. A leave-one-patient-out cross-validation scheme was used to assess the predictions against the expert manual segmentations confirmed as cancer by biopsy. Results: We achieved an area under the average receiver-operator characteristic curve of 0.923 for the first study, and visual assessment of the probability maps showed 21 out of 22 tumors were identified while a high level of specificity was maintained. In addition to evaluating the model against related approaches, the effects of the individual MRI parameter types were explored, and pathological verification using whole-mount slices from the second study was performed. Conclusions: The results of this paper show that the
Schiebener, Johannes; Zamarian, Laura; Delazer, Margarete; Brand, Matthias
2011-11-01
In two experiments with healthy subjects, we used the Game of Dice Task (GDT), the Probability-Associated Gambling (PAG) task, the Iowa Gambling Task (IGT), and executive-function and logical thinking tasks to shed light on the underlying processes of decision making under risk. Results indicate that handling probabilities, as in the PAG task, is an important ingredient of GDT performance. Executive functions and logical thinking also play major roles in deciding in the GDT. Implicit feedback learning, as measured by the IGT, has little impact. Results suggest that good probability handling may compensate for the effects of weak executive functions in decisions under risk.
ANNz2: Photometric Redshift and Probability Distribution Function Estimation using Machine Learning
NASA Astrophysics Data System (ADS)
Sadeh, I.; Abdalla, F. B.; Lahav, O.
2016-10-01
We present ANNz2, a new implementation of the public software for photometric redshift (photo-z) estimation of Collister & Lahav, which now includes generation of full probability distribution functions (PDFs). ANNz2 utilizes multiple machine learning methods, such as artificial neural networks and boosted decision/regression trees. The objective of the algorithm is to optimize the performance of the photo-z estimation, to properly derive the associated uncertainties, and to produce both single-value solutions and PDFs. In addition, estimators are made available, which mitigate possible problems of non-representative or incomplete spectroscopic training samples. ANNz2 has already been used as part of the first weak lensing analysis of the Dark Energy Survey, and is included in the experiment's first public data release. Here we illustrate the functionality of the code using data from the tenth data release of the Sloan Digital Sky Survey and the Baryon Oscillation Spectroscopic Survey. The code is available for download at http://github.com/IftachSadeh/ANNZ.
ERIC Educational Resources Information Center
Kaplan, Danielle E.; Wu, Erin Chia-ling
2006-01-01
Our research suggests static and animated graphics can lead to more animated thinking and more correct problem solving in computer-based probability learning. Pilot software modules were developed for graduate online statistics courses and representation research. A study with novice graduate student statisticians compared problem solving in five…
ERIC Educational Resources Information Center
van der Kleij, Sanne W.; Rispens, Judith E.; Scheper, Annette R.
2016-01-01
The aim of this study was to examine the influence of phonotactic probability (PP) and neighbourhood density (ND) on pseudoword learning in 17 Dutch-speaking typically developing children (mean age 7;2). They were familiarized with 16 one-syllable pseudowords varying in PP (high vs low) and ND (high vs low) via a storytelling procedure. The…
Using Rasch Analysis to Explore What Students Learn about Probability Concepts
ERIC Educational Resources Information Center
Mahmud, Zamalia; Porter, Anne
2015-01-01
Students' understanding of probability concepts have been investigated from various different perspectives. This study was set out to investigate perceived understanding of probability concepts of forty-four students from the STAT131 Understanding Uncertainty and Variation course at the University of Wollongong, NSW. Rasch measurement which is…
NASA Astrophysics Data System (ADS)
Mahmud, Zamalia; Porter, Anne; Salikin, Masniyati; Ghani, Nor Azura Md
2015-12-01
Students' understanding of probability concepts have been investigated from various different perspectives. Competency on the other hand is often measured separately in the form of test structure. This study was set out to show that perceived understanding and competency can be calibrated and assessed together using Rasch measurement tools. Forty-four students from the STAT131 Understanding Uncertainty and Variation course at the University of Wollongong, NSW have volunteered to participate in the study. Rasch measurement which is based on a probabilistic model is used to calibrate the responses from two survey instruments and investigate the interactions between them. Data were captured from the e-learning platform Moodle where students provided their responses through an online quiz. The study shows that majority of the students perceived little understanding about conditional and independent events prior to learning about it but tend to demonstrate a slightly higher competency level afterward. Based on the Rasch map, there is indication of some increase in learning and knowledge about some probability concepts at the end of the two weeks lessons on probability concepts.
Value and probability coding in a feedback-based learning task utilizing food rewards.
Tricomi, Elizabeth; Lempert, Karolina M
2015-01-01
For the consequences of our actions to guide behavior, the brain must represent different types of outcome-related information. For example, an outcome can be construed as negative because an expected reward was not delivered or because an outcome of low value was delivered. Thus behavioral consequences can differ in terms of the information they provide about outcome probability and value. We investigated the role of the striatum in processing probability-based and value-based negative feedback by training participants to associate cues with food rewards and then employing a selective satiety procedure to devalue one food outcome. Using functional magnetic resonance imaging, we examined brain activity related to receipt of expected rewards, receipt of devalued outcomes, omission of expected rewards, omission of devalued outcomes, and expected omissions of an outcome. Nucleus accumbens activation was greater for rewarding outcomes than devalued outcomes, but activity in this region did not correlate with the probability of reward receipt. Activation of the right caudate and putamen, however, was largest in response to rewarding outcomes relative to expected omissions of reward. The dorsal striatum (caudate and putamen) at the time of feedback also showed a parametric increase correlating with the trialwise probability of reward receipt. Our results suggest that the ventral striatum is sensitive to the motivational relevance, or subjective value, of the outcome, while the dorsal striatum codes for a more complex signal that incorporates reward probability. Value and probability information may be integrated in the dorsal striatum, to facilitate action planning and allocation of effort.
Murakoshi, Kazushi; Noguchi, Takuya
2005-04-01
Brown and Wanger [Brown, R.T., Wanger, A.R., 1964. Resistance to punishment and extinction following training with shock or nonreinforcement. J. Exp. Psychol. 68, 503-507] investigated rat behaviors with the following features: (1) rats were exposed to reward and punishment at the same time, (2) environment changed and rats relearned, and (3) rats were stochastically exposed to reward and punishment. The results are that exposure to nonreinforcement produces resistance to the decremental effects of behavior after stochastic reward schedule and that exposure to both punishment and reinforcement produces resistance to the decremental effects of behavior after stochastic punishment schedule. This paper aims to simulate the rat behaviors by a reinforcement learning algorithm in consideration of appearance probabilities of reinforcement signals. The former algorithms of reinforcement learning were unable to simulate the behavior of the feature (3). We improve the former reinforcement learning algorithms by controlling learning parameters in consideration of the acquisition probabilities of reinforcement signals. The proposed algorithm qualitatively simulates the result of the animal experiment of Brown and Wanger.
Apel, Kenn; Wolter, Julie A; Masterson, Julie J
2006-01-01
The purpose of this study was to investigate the orthographic-processing skills of typically developing 5-year-old preschool children. Of interest was whether phonotactic probabilities and/or orthotactic probabilities affected their ability to quickly learn the orthographic forms of 12 novel words. Orthographic processing was measured by the children's ability to spell and identify spellings of the novel words. Specifically, we were interested in whether (a) children quickly stored or "fast mapped" orthographic information after minimal exposure to novel words during storybook readings, (b) phonotactic and orthotactic probabilities affected orthographic fast-mapping skills, and (c) orthographic processing explained unique variance on a measure of the children's early spelling abilities. The results of this study indicated that young children quickly fast mapped orthographic information after minimal exposure to novel words, and their spelling (generation or reproduction but not recognition) was influenced by phonotactic and orthotactic probabilities. The significance of this work is that it demonstrates that preschoolers can fast map orthographic words they see onto spoken words they hear while listening to storybooks read to them and that the spelling of preschoolers is influenced uniquely by both phonological and orthographic information (probability of frequent letter and sound sequences in English words).
Value and probability coding in a feedback-based learning task utilizing food rewards
Lempert, Karolina M.
2014-01-01
For the consequences of our actions to guide behavior, the brain must represent different types of outcome-related information. For example, an outcome can be construed as negative because an expected reward was not delivered or because an outcome of low value was delivered. Thus behavioral consequences can differ in terms of the information they provide about outcome probability and value. We investigated the role of the striatum in processing probability-based and value-based negative feedback by training participants to associate cues with food rewards and then employing a selective satiety procedure to devalue one food outcome. Using functional magnetic resonance imaging, we examined brain activity related to receipt of expected rewards, receipt of devalued outcomes, omission of expected rewards, omission of devalued outcomes, and expected omissions of an outcome. Nucleus accumbens activation was greater for rewarding outcomes than devalued outcomes, but activity in this region did not correlate with the probability of reward receipt. Activation of the right caudate and putamen, however, was largest in response to rewarding outcomes relative to expected omissions of reward. The dorsal striatum (caudate and putamen) at the time of feedback also showed a parametric increase correlating with the trialwise probability of reward receipt. Our results suggest that the ventral striatum is sensitive to the motivational relevance, or subjective value, of the outcome, while the dorsal striatum codes for a more complex signal that incorporates reward probability. Value and probability information may be integrated in the dorsal striatum, to facilitate action planning and allocation of effort. PMID:25339705
Learning in reverse: eight-month-old infants track backward transitional probabilities.
Pelucchi, Bruna; Hay, Jessica F; Saffran, Jenny R
2009-11-01
Numerous recent studies suggest that human learners, including both infants and adults, readily track sequential statistics computed between adjacent elements. One such statistic, transitional probability, is typically calculated as the likelihood that one element predicts another. However, little is known about whether listeners are sensitive to the directionality of this computation. To address this issue, we tested 8-month-old infants in a word segmentation task, using fluent speech drawn from an unfamiliar natural language. Critically, test items were distinguished solely by their backward transitional probabilities. The results provide the first evidence that infants track backward statistics in fluent speech.
Bayindir, Mustafa; Bolger, Fergus; Say, Bilge
2016-07-19
Making decisions using judgements of multiple non-deterministic indicators is an important task, both in everyday and professional life. Learning of such decision making has often been studied as the mapping of stimuli (cues) to an environmental variable (criterion); however, little attention has been paid to the effects of situation-by-person interactions on this learning. Accordingly, we manipulated cue and feedback presentation mode (graphic or numeric) and task difficulty, and measured individual differences in working memory capacity (WMC). We predicted that graphic presentation, fewer cues, and elevated WMC would facilitate learning, and that person and task characteristics would interact such that presentation mode compatible with the decision maker's cognitive capability (enhanced visual or verbal WMC) would assist learning, particularly for more difficult tasks. We found our predicted main effects, but no significant interactions, except that those with greater WMC benefited to a larger extent with graphic than with numeric presentation, regardless of which type of working memory was enhanced or number of cues. Our findings suggest that the conclusions of past research based predominantly on tasks using numeric presentation need to be reevaluated and cast light on how working memory helps us learn multiple cue-criterion relationships, with implications for dual-process theories of cognition.
Williams, Carrick C; Pollatsek, Alexander; Cave, Kyle R; Stroud, Michael J
2009-06-01
In 2 experiments, eye movements were examined during searches in which elements were grouped into four 9-item clusters. The target (a red or blue T) was known in advance, and each cluster contained different numbers of target-color elements. Rather than color composition of a cluster invariantly guiding the order of search though clusters, the use of color was determined by the probability that the target would appear in a cluster of a certain color type: When the target was equally likely to be in any cluster containing the target color, fixations were directed to those clusters approximately equally, but when targets were more likely to appear in clusters with more target-color items, those clusters were likely to be fixated sooner. (The target probabilities guided search without explicit instruction.) Once fixated, the time spent within a cluster depended on the number of target-color elements, consistent with a search of only those elements. Thus, between-cluster search was influenced by global target probabilities signaled by amount of color or color ratios, whereas within-cluster search was directly driven by presence of the target color.
Tosun, Tuğçe; Gür, Ezgi; Balcı, Fuat
2016-01-19
Animals can shape their timed behaviors based on experienced probabilistic relations in a nearly optimal fashion. On the other hand, it is not clear if they adopt these timed decisions by making computations based on previously learnt task parameters (time intervals, locations, and probabilities) or if they gradually develop their decisions based on trial and error. To address this question, we tested mice in the timed-switching task, which required them to anticipate when (after a short or long delay) and at which of the two delay locations a reward would be presented. The probability of short trials differed between test groups in two experiments. Critically, we first trained mice on relevant task parameters by signaling the active trial with a discriminative stimulus and delivered the corresponding reward after the associated delay without any response requirement (without inducing switching behavior). During the test phase, both options were presented simultaneously to characterize the emergence and temporal characteristics of the switching behavior. Mice exhibited timed-switching behavior starting from the first few test trials, and their performance remained stable throughout testing in the majority of the conditions. Furthermore, as the probability of the short trial increased, mice waited longer before switching from the short to long location (experiment 1). These behavioral adjustments were in directions predicted by reward maximization. These results suggest that rather than gradually adjusting their time-dependent choice behavior, mice abruptly adopted temporal decision strategies by directly integrating their previous knowledge of task parameters into their timed behavior, supporting the model-based representational account of temporal risk assessment.
Tosun, Tuğçe; Gür, Ezgi; Balcı, Fuat
2016-01-01
Animals can shape their timed behaviors based on experienced probabilistic relations in a nearly optimal fashion. On the other hand, it is not clear if they adopt these timed decisions by making computations based on previously learnt task parameters (time intervals, locations, and probabilities) or if they gradually develop their decisions based on trial and error. To address this question, we tested mice in the timed-switching task, which required them to anticipate when (after a short or long delay) and at which of the two delay locations a reward would be presented. The probability of short trials differed between test groups in two experiments. Critically, we first trained mice on relevant task parameters by signaling the active trial with a discriminative stimulus and delivered the corresponding reward after the associated delay without any response requirement (without inducing switching behavior). During the test phase, both options were presented simultaneously to characterize the emergence and temporal characteristics of the switching behavior. Mice exhibited timed-switching behavior starting from the first few test trials, and their performance remained stable throughout testing in the majority of the conditions. Furthermore, as the probability of the short trial increased, mice waited longer before switching from the short to long location (experiment 1). These behavioral adjustments were in directions predicted by reward maximization. These results suggest that rather than gradually adjusting their time-dependent choice behavior, mice abruptly adopted temporal decision strategies by directly integrating their previous knowledge of task parameters into their timed behavior, supporting the model-based representational account of temporal risk assessment. PMID:26733674
Unification of field theory and maximum entropy methods for learning probability densities.
Kinney, Justin B
2015-09-01
The need to estimate smooth probability distributions (a.k.a. probability densities) from finite sampled data is ubiquitous in science. Many approaches to this problem have been described, but none is yet regarded as providing a definitive solution. Maximum entropy estimation and Bayesian field theory are two such approaches. Both have origins in statistical physics, but the relationship between them has remained unclear. Here I unify these two methods by showing that every maximum entropy density estimate can be recovered in the infinite smoothness limit of an appropriate Bayesian field theory. I also show that Bayesian field theory estimation can be performed without imposing any boundary conditions on candidate densities, and that the infinite smoothness limit of these theories recovers the most common types of maximum entropy estimates. Bayesian field theory thus provides a natural test of the maximum entropy null hypothesis and, furthermore, returns an alternative (lower entropy) density estimate when the maximum entropy hypothesis is falsified. The computations necessary for this approach can be performed rapidly for one-dimensional data, and software for doing this is provided.
ERIC Educational Resources Information Center
Storkel, Holly L.; Hoover, Jill R.
2011-01-01
The goal of this study was to examine the influence of part-word phonotactic probability/neighborhood density on word learning by preschool children with normal vocabularies that varied in size. Ninety-eight children (age 2 ; 11-6 ; 0) were taught consonant-vowel-consonant (CVC) nonwords orthogonally varying in the probability/density of the CV…
Christou, Nicolas; Dinov, Ivo D.
2011-01-01
Many modern technological advances have direct impact on the format, style and efficacy of delivery and consumption of educational content. For example, various novel communication and information technology tools and resources enable efficient, timely, interactive and graphical demonstrations of diverse scientific concepts. In this manuscript, we report on a meta-study of 3 controlled experiments of using the Statistics Online Computational Resources in probability and statistics courses. Web-accessible SOCR applets, demonstrations, simulations and virtual experiments were used in different courses as treatment and compared to matched control classes utilizing traditional pedagogical approaches. Qualitative and quantitative data we collected for all courses included Felder-Silverman-Soloman index of learning styles, background assessment, pre and post surveys of attitude towards the subject, end-point satisfaction survey, and varieties of quiz, laboratory and test scores. Our findings indicate that students' learning styles and attitudes towards a discipline may be important confounds of their final quantitative performance. The observed positive effects of integrating information technology with established pedagogical techniques may be valid across disciplines within the broader spectrum courses in the science education curriculum. The two critical components of improving science education via blended instruction include instructor training, and development of appropriate activities, simulations and interactive resources. PMID:21603097
Saltykov, A B; Toloknov, A V; Khitrov, N K
1990-01-01
Learning of rats in the process of elaboration of instrumental conditioned reflex takes place much faster at the probability of random fulfillment of correct reaction (PRCR) equal to 0.25 and 0.125 than at PRCR 0.5 and 0.05. Decrease of the frequency of positive reinforcements of correct reactions from 100 to 25% does not influence the speed of learning at PRCR 0.25 and 0.125, however significantly retards the process of learning at PRCR 0.05. There exist optimum and pessimum for learning PRCR values at different regimes of reinforcement.
Storkel, Holly L; Hoover, Jill R
2011-06-01
The goal of this study was to examine the influence of part-word phonotactic probability/neighborhood density on word learning by preschool children with normal vocabularies that varied in size. Ninety-eight children (age 2 ; 11-6 ; 0) were taught consonant-vowel-consonant (CVC) nonwords orthogonally varying in the probability/density of the CV (i.e. body) and VC (i.e. rhyme). Learning was measured via picture naming. Children with the lowest expressive vocabulary scores showed no effect of either CV or VC probability/density, although floor effects could not be ruled out. In contrast, children with low or high expressive vocabulary scores demonstrated sensitivity to part-word probability/density with the nature of the effect varying by group. Children with the highest expressive vocabulary scores displayed yet a third pattern of part-word probability/density effects. Taken together, word learning by preschool children was influenced by part-word probability/density but the nature of this influence appeared to depend on the size of the lexicon.
ERIC Educational Resources Information Center
Gray, Shelley; Pittman, Andrea; Weinhold, Juliet
2014-01-01
Purpose: In this study, the authors assessed the effects of phonotactic probability and neighborhood density on word-learning configuration by preschoolers with specific language impairment (SLI) and typical language development (TD). Method: One hundred thirty-one children participated: 48 with SLI, 44 with TD matched on age and gender, and 39…
Haeri, Narges-Al-Sadat; Palizvan, Mohammad Reza; Sadegh, Mehdi; Aghaei, Zohre; Rafiei, Mohammad
2016-07-01
Epilepsy is a common neurological disease characterized by periodic seizures. Cognitive deficits and impairments in learning and memory are also associated with epilepsy. Neuronal changes and synaptic modifications in kindling model of epilepsy are similar to those occur during the learning procedure and memory formation. Herein we investigated whether seizure susceptibility in pentylenetetrazol (PTZ) model of kindling is predictable based on the learning ability in the Morris water maze (MWM) task in male and female rats. Allocentric learning was tested using MWM in present of light while egocentric learning was evaluated by MWM in dark room. The results indicated no significant differences in allocentric learning abilities between male and female rats. However, male rats were able to memorize the location of the platform more effectively compared to females in egocentric test. In addition, a statistically significant negative correlation between learning abilities (working memory) and seizure susceptibility in male rats was found while this correlation was positive in female rats. On the other hand, although there was no significant correlation between retrieval (reference memory) of spatial memories and seizure parameters in male rats, female rats showed a significant negative correlation. These findings may provide some evidences for prediction of seizure susceptibility according to learning ability and memory retention.
NASA Astrophysics Data System (ADS)
Jaynes, E. T.; Bretthorst, G. Larry
2003-04-01
Foreword; Preface; Part I. Principles and Elementary Applications: 1. Plausible reasoning; 2. The quantitative rules; 3. Elementary sampling theory; 4. Elementary hypothesis testing; 5. Queer uses for probability theory; 6. Elementary parameter estimation; 7. The central, Gaussian or normal distribution; 8. Sufficiency, ancillarity, and all that; 9. Repetitive experiments, probability and frequency; 10. Physics of 'random experiments'; Part II. Advanced Applications: 11. Discrete prior probabilities, the entropy principle; 12. Ignorance priors and transformation groups; 13. Decision theory: historical background; 14. Simple applications of decision theory; 15. Paradoxes of probability theory; 16. Orthodox methods: historical background; 17. Principles and pathology of orthodox statistics; 18. The Ap distribution and rule of succession; 19. Physical measurements; 20. Model comparison; 21. Outliers and robustness; 22. Introduction to communication theory; References; Appendix A. Other approaches to probability theory; Appendix B. Mathematical formalities and style; Appendix C. Convolutions and cumulants.
Lexicographic Probability, Conditional Probability, and Nonstandard Probability
2009-11-11
the following conditions: CP1. µ(U |U) = 1 if U ∈ F ′. CP2 . µ(V1 ∪ V2 |U) = µ(V1 |U) + µ(V2 |U) if V1 ∩ V2 = ∅, U ∈ F ′, and V1, V2 ∈ F . CP3. µ(V |U...µ(V |X)× µ(X |U) if V ⊆ X ⊆ U , U,X ∈ F ′, V ∈ F . Note that it follows from CP1 and CP2 that µ(· |U) is a probability measure on (W,F) (and, in... CP2 hold. This is easily seen to determine µ. Moreover, µ vaciously satisfies CP3, since there do not exist distinct sets U and X in F ′ such that U
Confidence Probability versus Detection Probability
Axelrod, M
2005-08-18
In a discovery sampling activity the auditor seeks to vet an inventory by measuring (or inspecting) a random sample of items from the inventory. When the auditor finds every sample item in compliance, he must then make a confidence statement about the whole inventory. For example, the auditor might say: ''We believe that this inventory of 100 items contains no more than 5 defectives with 95% confidence.'' Note this is a retrospective statement in that it asserts something about the inventory after the sample was selected and measured. Contrast this to the prospective statement: ''We will detect the existence of more than 5 defective items in this inventory with 95% probability.'' The former uses confidence probability while the latter uses detection probability. For a given sample size, the two probabilities need not be equal, indeed they could differ significantly. Both these probabilities critically depend on the auditor's prior belief about the number of defectives in the inventory and how he defines non-compliance. In other words, the answer strongly depends on how the question is framed.
Probability workshop to be better in probability topic
NASA Astrophysics Data System (ADS)
Asmat, Aszila; Ujang, Suriyati; Wahid, Sharifah Norhuda Syed
2015-02-01
The purpose of the present study was to examine whether statistics anxiety and attitudes towards probability topic among students in higher education level have an effect on their performance. 62 fourth semester science students were given statistics anxiety questionnaires about their perception towards probability topic. Result indicated that students' performance in probability topic is not related to anxiety level, which means that the higher level in statistics anxiety will not cause lower score in probability topic performance. The study also revealed that motivated students gained from probability workshop ensure that their performance in probability topic shows a positive improvement compared before the workshop. In addition there exists a significance difference in students' performance between genders with better achievement among female students compared to male students. Thus, more initiatives in learning programs with different teaching approaches is needed to provide useful information in improving student learning outcome in higher learning institution.
Betrie, Getnet D; Sadiq, Rehan; Morin, Kevin A; Tesfamariam, Solomon
2014-08-15
Acid rock drainage (ARD) is a major pollution problem globally that has adversely impacted the environment. Identification and quantification of uncertainties are integral parts of ARD assessment and risk mitigation, however previous studies on predicting ARD drainage chemistry have not fully addressed issues of uncertainties. In this study, artificial neural networks (ANN) and support vector machine (SVM) are used for the prediction of ARD drainage chemistry and their predictive uncertainties are quantified using probability bounds analysis. Furthermore, the predictions of ANN and SVM are integrated using four aggregation methods to improve their individual predictions. The results of this study showed that ANN performed better than SVM in enveloping the observed concentrations. In addition, integrating the prediction of ANN and SVM using the aggregation methods improved the predictions of individual techniques.
Vock, David M; Wolfson, Julian; Bandyopadhyay, Sunayan; Adomavicius, Gediminas; Johnson, Paul E; Vazquez-Benitez, Gabriela; O'Connor, Patrick J
2016-06-01
Models for predicting the probability of experiencing various health outcomes or adverse events over a certain time frame (e.g., having a heart attack in the next 5years) based on individual patient characteristics are important tools for managing patient care. Electronic health data (EHD) are appealing sources of training data because they provide access to large amounts of rich individual-level data from present-day patient populations. However, because EHD are derived by extracting information from administrative and clinical databases, some fraction of subjects will not be under observation for the entire time frame over which one wants to make predictions; this loss to follow-up is often due to disenrollment from the health system. For subjects without complete follow-up, whether or not they experienced the adverse event is unknown, and in statistical terms the event time is said to be right-censored. Most machine learning approaches to the problem have been relatively ad hoc; for example, common approaches for handling observations in which the event status is unknown include (1) discarding those observations, (2) treating them as non-events, (3) splitting those observations into two observations: one where the event occurs and one where the event does not. In this paper, we present a general-purpose approach to account for right-censored outcomes using inverse probability of censoring weighting (IPCW). We illustrate how IPCW can easily be incorporated into a number of existing machine learning algorithms used to mine big health care data including Bayesian networks, k-nearest neighbors, decision trees, and generalized additive models. We then show that our approach leads to better calibrated predictions than the three ad hoc approaches when applied to predicting the 5-year risk of experiencing a cardiovascular adverse event, using EHD from a large U.S. Midwestern healthcare system.
NASA Astrophysics Data System (ADS)
Ekonomou, L.; Karampelas, P.; Vita, V.; Chatzarakis, G. E.
2011-04-01
One of the most popular methods of protecting high voltage transmission lines against lightning strikes and internal overvoltages is the use of arresters. The installation of arresters in high voltage transmission lines can prevent or even reduce the lines' failure rate. Several studies based on simulation tools have been presented in order to estimate the critical currents that exceed the arresters' rated energy stress and to specify the arresters' installation interval. In this work artificial intelligence, and more specifically a Q-learning artificial neural network (ANN) model, is addressed for evaluating the arresters' failure probability. The aims of the paper are to describe in detail the developed Q-learning ANN model and to compare the results obtained by its application in operating 150 kV Greek transmission lines with those produced using a simulation tool. The satisfactory and accurate results of the proposed ANN model can make it a valuable tool for designers of electrical power systems seeking more effective lightning protection, reducing operational costs and better continuity of service.
Information Processing Using Quantum Probability
NASA Astrophysics Data System (ADS)
Behera, Laxmidhar
2006-11-01
This paper presents an information processing paradigm that introduces collective response of multiple agents (computational units) while the level of intelligence associated with the information processing has been increased manifold. It is shown that if the potential field of the Schroedinger wave equation is modulated using a self-organized learning scheme, then the probability density function associated with the stochastic data is transferred to the probability amplitude function which is the response of the Schroedinger wave equation. This approach illustrates that information processing of data with stochastic behavior can be efficiently done using quantum probability instead of classical probability. The proposed scheme has been demonstrated through two applications: denoising and adaptive control.
ERIC Educational Resources Information Center
Koo, Reginald; Jones, Martin L.
2011-01-01
Quite a number of interesting problems in probability feature an event with probability equal to 1/e. This article discusses three such problems and attempts to explain why this probability occurs with such frequency.
Risk estimation using probability machines
2014-01-01
Background Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. Results We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. Conclusions The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a “risk machine”, will share properties from the statistical machine that it is derived from. PMID:24581306
Class probability estimation for medical studies.
Simon, Richard
2014-07-01
I provide a commentary on two papers "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler. Those papers provide an up-to-date review of some popular machine learning methods for class probability estimation and compare those methods to logistic regression modeling in real and simulated datasets.
ERIC Educational Resources Information Center
Tinungki, Georgina Maria
2015-01-01
The importance of learning mathematics can not be separated from its role in all aspects of life. Communicating ideas by using mathematics language is even more practical, systematic, and efficient. In order to overcome the difficulties of students who have insufficient understanding of mathematics material, good communications should be built in…
Experience matters: information acquisition optimizes probability gain.
Nelson, Jonathan D; McKenzie, Craig R M; Cottrell, Garrison W; Sejnowski, Terrence J
2010-07-01
Deciding which piece of information to acquire or attend to is fundamental to perception, categorization, medical diagnosis, and scientific inference. Four statistical theories of the value of information-information gain, Kullback-Liebler distance, probability gain (error minimization), and impact-are equally consistent with extant data on human information acquisition. Three experiments, designed via computer optimization to be maximally informative, tested which of these theories best describes human information search. Experiment 1, which used natural sampling and experience-based learning to convey environmental probabilities, found that probability gain explained subjects' information search better than the other statistical theories or the probability-of-certainty heuristic. Experiments 1 and 2 found that subjects behaved differently when the standard method of verbally presented summary statistics (rather than experience-based learning) was used to convey environmental probabilities. Experiment 3 found that subjects' preference for probability gain is robust, suggesting that the other models contribute little to subjects' search behavior.
Probability and Relative Frequency
NASA Astrophysics Data System (ADS)
Drieschner, Michael
2016-01-01
The concept of probability seems to have been inexplicable since its invention in the seventeenth century. In its use in science, probability is closely related with relative frequency. So the task seems to be interpreting that relation. In this paper, we start with predicted relative frequency and show that its structure is the same as that of probability. I propose to call that the `prediction interpretation' of probability. The consequences of that definition are discussed. The "ladder"-structure of the probability calculus is analyzed. The expectation of the relative frequency is shown to be equal to the predicted relative frequency. Probability is shown to be the most general empirically testable prediction.
The National Aquatic Resource Surveys (NARS) use probability-survey designs to assess the condition of the nation’s waters. In probability surveys (also known as sample-surveys or statistical surveys), sampling sites are selected randomly.
ERIC Educational Resources Information Center
Edwards, William F.; Shiflett, Ray C.; Shultz, Harris
2008-01-01
The mathematical model used to describe independence between two events in probability has a non-intuitive consequence called dependent spaces. The paper begins with a very brief history of the development of probability, then defines dependent spaces, and reviews what is known about finite spaces with uniform probability. The study of finite…
Spatial Probability Cuing and Right Hemisphere Damage
ERIC Educational Resources Information Center
Shaqiri, Albulena; Anderson, Britt
2012-01-01
In this experiment we studied statistical learning, inter-trial priming, and visual attention. We assessed healthy controls and right brain damaged (RBD) patients with and without neglect, on a simple visual discrimination task designed to measure priming effects and probability learning. All participants showed a preserved priming effect for item…
Dynamical Simulation of Probabilities
NASA Technical Reports Server (NTRS)
Zak, Michail
1996-01-01
It has been demonstrated that classical probabilities, and in particular, probabilistic Turing machine, can be simulated by combining chaos and non-Lipschitz dynamics, without utilization of any man-made devices(such as random number generators). Self-orgainizing properties of systems coupling simulated and calculated probabilities and their link to quantum computations are discussed. Special attention was focused upon coupled stochastic processes, defined in terms of conditional probabilities, for which joint probability does not exist. Simulations of quantum probabilities are also discussed.
Probability and radical behaviorism
Espinosa, James M.
1992-01-01
The concept of probability appears to be very important in the radical behaviorism of Skinner. Yet, it seems that this probability has not been accurately defined and is still ambiguous. I give a strict, relative frequency interpretation of probability and its applicability to the data from the science of behavior as supplied by cumulative records. Two examples of stochastic processes are given that may model the data from cumulative records that result under conditions of continuous reinforcement and extinction, respectively. PMID:22478114
NASA Astrophysics Data System (ADS)
Laktineh, Imad
2010-04-01
This ourse constitutes a brief introduction to probability applications in high energy physis. First the mathematical tools related to the diferent probability conepts are introduced. The probability distributions which are commonly used in high energy physics and their characteristics are then shown and commented. The central limit theorem and its consequences are analysed. Finally some numerical methods used to produce diferent kinds of probability distribution are presented. The full article (17 p.) corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.
Probability of satellite collision
NASA Technical Reports Server (NTRS)
Mccarter, J. W.
1972-01-01
A method is presented for computing the probability of a collision between a particular artificial earth satellite and any one of the total population of earth satellites. The collision hazard incurred by the proposed modular Space Station is assessed using the technique presented. The results of a parametric study to determine what type of satellite orbits produce the greatest contribution to the total collision probability are presented. Collision probability for the Space Station is given as a function of Space Station altitude and inclination. Collision probability was also parameterized over miss distance and mission duration.
STATISTICAL ANALYSIS, REPORTS), (*PROBABILITY, REPORTS), INFORMATION THEORY, DIFFERENTIAL EQUATIONS, STATISTICAL PROCESSES, STOCHASTIC PROCESSES, MULTIVARIATE ANALYSIS, DISTRIBUTION THEORY , DECISION THEORY, MEASURE THEORY, OPTIMIZATION
Dean, Jamie A; Wong, Kee H; Welsh, Liam C; Jones, Ann-Britt; Schick, Ulrike; Newbold, Kate L; Bhide, Shreerang A; Harrington, Kevin J; Nutting, Christopher M; Gulliford, Sarah L
2016-01-01
Background and Purpose Severe acute mucositis commonly results from head and neck (chemo)radiotherapy. A predictive model of mucositis could guide clinical decision-making and inform treatment planning. We aimed to generate such a model using spatial dose metrics and machine learning. Material and Methods Predictive models of severe acute mucositis were generated using radiotherapy dose (dose-volume and spatial dose metrics) and clinical data. Penalised logistic regression, support vector classification and random forest classification (RFC) models were generated and compared. Internal validation was performed (with 100-iteration cross-validation), using multiple metrics, including area under the receiver operating characteristic curve (AUC) and calibration slope, to assess performance. Associations between covariates and severe mucositis were explored using the models. Results The dose-volume-based models (standard) performed equally to those incorporating spatial information. Discrimination was similar between models, but the RFCstandard had the best calibration. The mean AUC and calibration slope for this model were 0.71 (s.d.=0.09) and 3.9 (s.d.=2.2), respectively. The volumes of oral cavity receiving intermediate and high doses were associated with severe mucositis. Conclusions The RFCstandard model performance is modest-to-good, but should be improved, and requires external validation. Reducing the volumes of oral cavity receiving intermediate and high doses may reduce mucositis incidence. PMID:27240717
ERIC Educational Resources Information Center
Barnes, Bernis, Ed.; And Others
This teacher's guide to probability and statistics contains three major sections. The first section on elementary combinatorial principles includes activities, student problems, and suggested teaching procedures for the multiplication principle, permutations, and combinations. Section two develops an intuitive approach to probability through…
Teachers' Understandings of Probability
ERIC Educational Resources Information Center
Liu, Yan; Thompson, Patrick
2007-01-01
Probability is an important idea with a remarkably wide range of applications. However, psychological and instructional studies conducted in the last two decades have consistently documented poor understanding of probability among different populations across different settings. The purpose of this study is to develop a theoretical framework for…
ERIC Educational Resources Information Center
Wilson, Jason; Lawman, Joshua; Murphy, Rachael; Nelson, Marissa
2011-01-01
This article describes a probability project used in an upper division, one-semester probability course with third-semester calculus and linear algebra prerequisites. The student learning outcome focused on developing the skills necessary for approaching project-sized math/stat application problems. These skills include appropriately defining…
Estimation of State Transition Probabilities: A Neural Network Model
NASA Astrophysics Data System (ADS)
Saito, Hiroshi; Takiyama, Ken; Okada, Masato
2015-12-01
Humans and animals can predict future states on the basis of acquired knowledge. This prediction of the state transition is important for choosing the best action, and the prediction is only possible if the state transition probability has already been learned. However, how our brains learn the state transition probability is unknown. Here, we propose a simple algorithm for estimating the state transition probability by utilizing the state prediction error. We analytically and numerically confirmed that our algorithm is able to learn the probability completely with an appropriate learning rate. Furthermore, our learning rule reproduced experimentally reported psychometric functions and neural activities in the lateral intraparietal area in a decision-making task. Thus, our algorithm might describe the manner in which our brains learn state transition probabilities and predict future states.
NASA Technical Reports Server (NTRS)
Soneira, R. M.; Bahcall, J. N.
1981-01-01
Probabilities are calculated for acquiring suitable guide stars (GS) with the fine guidance system (FGS) of the space telescope. A number of the considerations and techniques described are also relevant for other space astronomy missions. The constraints of the FGS are reviewed. The available data on bright star densities are summarized and a previous error in the literature is corrected. Separate analytic and Monte Carlo calculations of the probabilities are described. A simulation of space telescope pointing is carried out using the Weistrop north galactic pole catalog of bright stars. Sufficient information is presented so that the probabilities of acquisition can be estimated as a function of position in the sky. The probability of acquiring suitable guide stars is greatly increased if the FGS can allow an appreciable difference between the (bright) primary GS limiting magnitude and the (fainter) secondary GS limiting magnitude.
Quantum computing and probability.
Ferry, David K
2009-11-25
Over the past two decades, quantum computing has become a popular and promising approach to trying to solve computationally difficult problems. Missing in many descriptions of quantum computing is just how probability enters into the process. Here, we discuss some simple examples of how uncertainty and probability enter, and how this and the ideas of quantum computing challenge our interpretations of quantum mechanics. It is found that this uncertainty can lead to intrinsic decoherence, and this raises challenges for error correction.
Rationalizing Hybrid Earthquake Probabilities
NASA Astrophysics Data System (ADS)
Gomberg, J.; Reasenberg, P.; Beeler, N.; Cocco, M.; Belardinelli, M.
2003-12-01
An approach to including stress transfer and frictional effects in estimates of the probability of failure of a single fault affected by a nearby earthquake has been suggested in Stein et al. (1997). This `hybrid' approach combines conditional probabilities, which depend on the time elapsed since the last earthquake on the affected fault, with Poissonian probabilities that account for friction and depend only on the time since the perturbing earthquake. The latter are based on the seismicity rate change model developed by Dieterich (1994) to explain the temporal behavior of aftershock sequences in terms of rate-state frictional processes. The model assumes an infinite population of nucleation sites that are near failure at the time of the perturbing earthquake. In the hybrid approach, assuming the Dieterich model can lead to significant transient increases in failure probability. We explore some of the implications of applying the Dieterich model to a single fault and its impact on the hybrid probabilities. We present two interpretations that we believe can rationalize the use of the hybrid approach. In the first, a statistical distribution representing uncertainties in elapsed and/or mean recurrence time on the fault serves as a proxy for Dieterich's population of nucleation sites. In the second, we imagine a population of nucleation patches distributed over the fault with a distribution of maturities. In both cases we find that the probability depends on the time since the last earthquake. In particular, the size of the transient probability increase may only be significant for faults already close to failure. Neglecting the maturity of a fault may lead to overestimated rate and probability increases.
Asteroidal collision probabilities
NASA Astrophysics Data System (ADS)
Bottke, W. F.; Greenberg, R.
1993-05-01
Several past calculations of collision probabilities between pairs of bodies on independent orbits have yielded inconsistent results. We review the methodologies and identify their various problems. Greenberg's (1982) collision probability formalism (now with a corrected symmetry assumption) is equivalent to Wetherill's (1967) approach, except that it includes a way to avoid singularities near apsides. That method shows that the procedure by Namiki and Binzel (1991) was accurate for those cases where singularities did not arise.
NASA Technical Reports Server (NTRS)
Bollenbacher, Gary; Guptill, James D.
1999-01-01
This report analyzes the probability of a launch vehicle colliding with one of the nearly 10,000 tracked objects orbiting the Earth, given that an object on a near-collision course with the launch vehicle has been identified. Knowledge of the probability of collision throughout the launch window can be used to avoid launching at times when the probability of collision is unacceptably high. The analysis in this report assumes that the positions of the orbiting objects and the launch vehicle can be predicted as a function of time and therefore that any tracked object which comes close to the launch vehicle can be identified. The analysis further assumes that the position uncertainty of the launch vehicle and the approaching space object can be described with position covariance matrices. With these and some additional simplifying assumptions, a closed-form solution is developed using two approaches. The solution shows that the probability of collision is a function of position uncertainties, the size of the two potentially colliding objects, and the nominal separation distance at the point of closest approach. ne impact of the simplifying assumptions on the accuracy of the final result is assessed and the application of the results to the Cassini mission, launched in October 1997, is described. Other factors that affect the probability of collision are also discussed. Finally, the report offers alternative approaches that can be used to evaluate the probability of collision.
The perception of probability.
Gallistel, C R; Krishan, Monika; Liu, Ye; Miller, Reilly; Latham, Peter E
2014-01-01
We present a computational model to explain the results from experiments in which subjects estimate the hidden probability parameter of a stepwise nonstationary Bernoulli process outcome by outcome. The model captures the following results qualitatively and quantitatively, with only 2 free parameters: (a) Subjects do not update their estimate after each outcome; they step from one estimate to another at irregular intervals. (b) The joint distribution of step widths and heights cannot be explained on the assumption that a threshold amount of change must be exceeded in order for them to indicate a change in their perception. (c) The mapping of observed probability to the median perceived probability is the identity function over the full range of probabilities. (d) Precision (how close estimates are to the best possible estimate) is good and constant over the full range. (e) Subjects quickly detect substantial changes in the hidden probability parameter. (f) The perceived probability sometimes changes dramatically from one observation to the next. (g) Subjects sometimes have second thoughts about a previous change perception, after observing further outcomes. (h) The frequency with which they perceive changes moves in the direction of the true frequency over sessions. (Explaining this finding requires 2 additional parametric assumptions.) The model treats the perception of the current probability as a by-product of the construction of a compact encoding of the experienced sequence in terms of its change points. It illustrates the why and the how of intermittent Bayesian belief updating and retrospective revision in simple perception. It suggests a reinterpretation of findings in the recent literature on the neurobiology of decision making.
Experimental Probability in Elementary School
ERIC Educational Resources Information Center
Andrew, Lane
2009-01-01
Concepts in probability can be more readily understood if students are first exposed to probability via experiment. Performing probability experiments encourages students to develop understandings of probability grounded in real events, as opposed to merely computing answers based on formulae.
Carr, D.B.; Tolley, H.D.
1982-12-01
This paper investigates procedures for univariate nonparametric estimation of tail probabilities. Extrapolated values for tail probabilities beyond the data are also obtained based on the shape of the density in the tail. Several estimators which use exponential weighting are described. These are compared in a Monte Carlo study to nonweighted estimators, to the empirical cdf, to an integrated kernel, to a Fourier series estimate, to a penalized likelihood estimate and a maximum likelihood estimate. Selected weighted estimators are shown to compare favorably to many of these standard estimators for the sampling distributions investigated.
A Unifying Probability Example.
ERIC Educational Resources Information Center
Maruszewski, Richard F., Jr.
2002-01-01
Presents an example from probability and statistics that ties together several topics including the mean and variance of a discrete random variable, the binomial distribution and its particular mean and variance, the sum of independent random variables, the mean and variance of the sum, and the central limit theorem. Uses Excel to illustrate these…
ERIC Educational Resources Information Center
Varga, Tamas
This booklet resulted from a 1980 visit by the author, a Hungarian mathematics educator, to the Teachers' Center Project at Southern Illinois University at Edwardsville. Included are activities and problems that make probablility concepts accessible to young children. The topics considered are: two probability games; choosing two beads; matching…
Univariate Probability Distributions
ERIC Educational Resources Information Center
Leemis, Lawrence M.; Luckett, Daniel J.; Powell, Austin G.; Vermeer, Peter E.
2012-01-01
We describe a web-based interactive graphic that can be used as a resource in introductory classes in mathematical statistics. This interactive graphic presents 76 common univariate distributions and gives details on (a) various features of the distribution such as the functional form of the probability density function and cumulative distribution…
Approximating Integrals Using Probability
ERIC Educational Resources Information Center
Maruszewski, Richard F., Jr.; Caudle, Kyle A.
2005-01-01
As part of a discussion on Monte Carlo methods, which outlines how to use probability expectations to approximate the value of a definite integral. The purpose of this paper is to elaborate on this technique and then to show several examples using visual basic as a programming tool. It is an interesting method because it combines two branches of…
Geometric Probability and the Areas of Leaves
ERIC Educational Resources Information Center
Hoiberg, Karen Bush; Sharp, Janet; Hodgson, Ted; Colbert, Jim
2005-01-01
This article describes how a group of fifth-grade mathematics students measured irregularly shaped objects using geometric probability theory. After learning how to apply a ratio procedure to find the areas of familiar shapes, students extended the strategy for use with irregularly shaped objects, in this case, leaves. (Contains 2 tables and 8…
Probability & Perception: The Representativeness Heuristic in Action
ERIC Educational Resources Information Center
Lu, Yun; Vasko, Francis J.; Drummond, Trevor J.; Vasko, Lisa E.
2014-01-01
If the prospective students of probability lack a background in mathematical proofs, hands-on classroom activities may work well to help them to learn to analyze problems correctly. For example, students may physically roll a die twice to count and compare the frequency of the sequences. Tools such as graphing calculators or Microsoft Excel®…
NASA Astrophysics Data System (ADS)
von der Linden, Wolfgang; Dose, Volker; von Toussaint, Udo
2014-06-01
Preface; Part I. Introduction: 1. The meaning of probability; 2. Basic definitions; 3. Bayesian inference; 4. Combinatrics; 5. Random walks; 6. Limit theorems; 7. Continuous distributions; 8. The central limit theorem; 9. Poisson processes and waiting times; Part II. Assigning Probabilities: 10. Transformation invariance; 11. Maximum entropy; 12. Qualified maximum entropy; 13. Global smoothness; Part III. Parameter Estimation: 14. Bayesian parameter estimation; 15. Frequentist parameter estimation; 16. The Cramer-Rao inequality; Part IV. Testing Hypotheses: 17. The Bayesian way; 18. The frequentist way; 19. Sampling distributions; 20. Bayesian vs frequentist hypothesis tests; Part V. Real World Applications: 21. Regression; 22. Inconsistent data; 23. Unrecognized signal contributions; 24. Change point problems; 25. Function estimation; 26. Integral equations; 27. Model selection; 28. Bayesian experimental design; Part VI. Probabilistic Numerical Techniques: 29. Numerical integration; 30. Monte Carlo methods; 31. Nested sampling; Appendixes; References; Index.
Superpositions of probability distributions
NASA Astrophysics Data System (ADS)
Jizba, Petr; Kleinert, Hagen
2008-09-01
Probability distributions which can be obtained from superpositions of Gaussian distributions of different variances v=σ2 play a favored role in quantum theory and financial markets. Such superpositions need not necessarily obey the Chapman-Kolmogorov semigroup relation for Markovian processes because they may introduce memory effects. We derive the general form of the smearing distributions in v which do not destroy the semigroup property. The smearing technique has two immediate applications. It permits simplifying the system of Kramers-Moyal equations for smeared and unsmeared conditional probabilities, and can be conveniently implemented in the path integral calculus. In many cases, the superposition of path integrals can be evaluated much easier than the initial path integral. Three simple examples are presented, and it is shown how the technique is extended to quantum mechanics.
Efficient Probability Sequences
2014-08-18
Ungar (2014), to produce a distinct forecasting system. The system consists of the method for eliciting individual subjective forecasts together with...E. Stone, and L. H. Ungar (2014). Two reasons to make aggregated probability forecasts more extreme. Decision Analysis 11 (2), 133–145. Bickel, J. E...Letters 91 (3), 425–429. Mellers, B., L. Ungar , J. Baron, J. Ramos, B. Gurcay, K. Fincher, S. E. Scott, D. Moore, P. Atanasov, S. A. Swift, et al. (2014
1983-07-26
DeGroot , Morris H. Probability and Statistic. Addison-Wesley Publishing Company, Reading, Massachusetts, 1975. [Gillogly 78] Gillogly, J.J. Performance...distribution [ DeGroot 751 has just begun. The beta distribution has several features that might make it a more reasonable choice. As with the normal-based...1982. [Cooley 65] Cooley, J.M. and Tukey, J.W. An algorithm for the machine calculation of complex Fourier series. Math. Comp. 19, 1965. [ DeGroot 75
Troutman, B.M.; Karlinger, M.R.
2003-01-01
The T-year annual maximum flood at a site is defined to be that streamflow, that has probability 1/T of being exceeded in any given year, and for a group of sites the corresponding regional flood probability (RFP) is the probability that at least one site will experience a T-year flood in any given year. The RFP depends on the number of sites of interest and on the spatial correlation of flows among the sites. We present a Monte Carlo method for obtaining the RFP and demonstrate that spatial correlation estimates used in this method may be obtained with rank transformed data and therefore that knowledge of the at-site peak flow distribution is not necessary. We examine the extent to which the estimates depend on specification of a parametric form for the spatial correlation function, which is known to be nonstationary for peak flows. It is shown in a simulation study that use of a stationary correlation function to compute RFPs yields satisfactory estimates for certain nonstationary processes. Application of asymptotic extreme value theory is examined, and a methodology for separating channel network and rainfall effects on RFPs is suggested. A case study is presented using peak flow data from the state of Washington. For 193 sites in the Puget Sound region it is estimated that a 100-year flood will occur on the average every 4,5 years.
ERIC Educational Resources Information Center
Glaser, Robert
A report on learning psychology and its relationship to the study of school learning emphasizes the increasing interaction between theorists and educational practitioners, particularly in attempting to learn which variables influence the instructional process and to find an appropriate methodology to measure and evaluate learning. "Learning…
Seismicity alert probabilities at Parkfield, California, revisited
Michael, A.J.; Jones, L.M.
1998-01-01
For a decade, the US Geological Survey has used the Parkfield Earthquake Prediction Experiment scenario document to estimate the probability that earthquakes observed on the San Andreas fault near Parkfield will turn out to be foreshocks followed by the expected magnitude six mainshock. During this time, we have learned much about the seismogenic process at Parkfield, about the long-term probability of the Parkfield mainshock, and about the estimation of these types of probabilities. The probabilities for potential foreshocks at Parkfield are reexamined and revised in light of these advances. As part of this process, we have confirmed both the rate of foreshocks before strike-slip earthquakes in the San Andreas physiographic province and the uniform distribution of foreshocks with magnitude proposed by earlier studies. Compared to the earlier assessment, these new estimates of the long-term probability of the Parkfield mainshock are lower, our estimate of the rate of background seismicity is higher, and we find that the assumption that foreshocks at Parkfield occur in a unique way is not statistically significant at the 95% confidence level. While the exact numbers vary depending on the assumptions that are made, the new alert probabilities are lower than previously estimated. Considering the various assumptions and the statistical uncertainties in the input parameters, we also compute a plausible range for the probabilities. The range is large, partly due to the extra knowledge that exists for the Parkfield segment, making us question the usefulness of these numbers.
Retrieve Tether Survival Probability
2007-11-02
cuts of the tether by meteorites and orbital debris , is calculated to be 99.934% for the planned experiment duration of six months or less. This is...due to the unlikely event of a strike by a large piece of orbital debris greater than 1 meter in size cutting all the lines of the tether at once. The...probability of the tether surviving multiple cuts by meteoroid and orbital debris impactors smaller than 5 cm in diameter is 99.9993% at six months
People's conditional probability judgments follow probability theory (plus noise).
Costello, Fintan; Watts, Paul
2016-09-01
A common view in current psychology is that people estimate probabilities using various 'heuristics' or rules of thumb that do not follow the normative rules of probability theory. We present a model where people estimate conditional probabilities such as P(A|B) (the probability of A given that B has occurred) by a process that follows standard frequentist probability theory but is subject to random noise. This model accounts for various results from previous studies of conditional probability judgment. This model predicts that people's conditional probability judgments will agree with a series of fundamental identities in probability theory whose form cancels the effect of noise, while deviating from probability theory in other expressions whose form does not allow such cancellation. Two experiments strongly confirm these predictions, with people's estimates on average agreeing with probability theory for the noise-cancelling identities, but deviating from probability theory (in just the way predicted by the model) for other identities. This new model subsumes an earlier model of unconditional or 'direct' probability judgment which explains a number of systematic biases seen in direct probability judgment (Costello & Watts, 2014). This model may thus provide a fully general account of the mechanisms by which people estimate probabilities.
Pointwise probability reinforcements for robust statistical inference.
Frénay, Benoît; Verleysen, Michel
2014-02-01
Statistical inference using machine learning techniques may be difficult with small datasets because of abnormally frequent data (AFDs). AFDs are observations that are much more frequent in the training sample that they should be, with respect to their theoretical probability, and include e.g. outliers. Estimates of parameters tend to be biased towards models which support such data. This paper proposes to introduce pointwise probability reinforcements (PPRs): the probability of each observation is reinforced by a PPR and a regularisation allows controlling the amount of reinforcement which compensates for AFDs. The proposed solution is very generic, since it can be used to robustify any statistical inference method which can be formulated as a likelihood maximisation. Experiments show that PPRs can be easily used to tackle regression, classification and projection: models are freed from the influence of outliers. Moreover, outliers can be filtered manually since an abnormality degree is obtained for each observation.
Probability state modeling theory.
Bagwell, C Bruce; Hunsberger, Benjamin C; Herbert, Donald J; Munson, Mark E; Hill, Beth L; Bray, Chris M; Preffer, Frederic I
2015-07-01
As the technology of cytometry matures, there is mounting pressure to address two major issues with data analyses. The first issue is to develop new analysis methods for high-dimensional data that can directly reveal and quantify important characteristics associated with complex cellular biology. The other issue is to replace subjective and inaccurate gating with automated methods that objectively define subpopulations and account for population overlap due to measurement uncertainty. Probability state modeling (PSM) is a technique that addresses both of these issues. The theory and important algorithms associated with PSM are presented along with simple examples and general strategies for autonomous analyses. PSM is leveraged to better understand B-cell ontogeny in bone marrow in a companion Cytometry Part B manuscript. Three short relevant videos are available in the online supporting information for both of these papers. PSM avoids the dimensionality barrier normally associated with high-dimensionality modeling by using broadened quantile functions instead of frequency functions to represent the modulation of cellular epitopes as cells differentiate. Since modeling programs ultimately minimize or maximize one or more objective functions, they are particularly amenable to automation and, therefore, represent a viable alternative to subjective and inaccurate gating approaches.
A "Virtual Spin" on the Teaching of Probability
ERIC Educational Resources Information Center
Beck, Shari A.; Huse, Vanessa E.
2007-01-01
This article, which describes integrating virtual manipulatives with the teaching of probability at the elementary level, puts a "virtual spin" on the teaching of probability to provide more opportunities for students to experience successful learning. The traditional use of concrete manipulatives is enhanced with virtual coins and spinners from…
Visualizing and Understanding Probability and Statistics: Graphical Simulations Using Excel
ERIC Educational Resources Information Center
Gordon, Sheldon P.; Gordon, Florence S.
2009-01-01
The authors describe a collection of dynamic interactive simulations for teaching and learning most of the important ideas and techniques of introductory statistics and probability. The modules cover such topics as randomness, simulations of probability experiments such as coin flipping, dice rolling and general binomial experiments, a simulation…
ERIC Educational Resources Information Center
Falk, Ruma; Kendig, Keith
2013-01-01
Two contestants debate the notorious probability problem of the sex of the second child. The conclusions boil down to explication of the underlying scenarios and assumptions. Basic principles of probability theory are highlighted.
Coherent Assessment of Subjective Probability
1981-03-01
known results of de Finetti (1937, 1972, 1974), Smith (1961), and Savage (1971) and some recent results of Lind- ley (1980) concerning the use of...provides the motivation for de Finettis definition of subjective probabilities as coherent bet prices. From the definition of the probability measure...subjective probability, the probability laws which are traditionally stated as axioms or definitions are obtained instead as theorems. (De Finetti F -7
Probabilities of transversions and transitions.
Vol'kenshtein, M V
1976-01-01
The values of the mean relative probabilities of transversions and transitions have been refined on the basis of the data collected by Jukes and found to be equal to 0.34 and 0.66, respectively. Evolutionary factors increase the probability of transversions to 0.44. The relative probabilities of individual substitutions have been determined, and a detailed classification of the nonsense mutations has been given. Such mutations are especially probable in the UGG (Trp) codon. The highest probability of AG, GA transitions correlates with the lowest mean change in the hydrophobic nature of the amino acids coded.
Unders and Overs: Using a Dice Game to Illustrate Basic Probability Concepts
ERIC Educational Resources Information Center
McPherson, Sandra Hanson
2015-01-01
In this paper, the dice game "Unders and Overs" is described and presented as an active learning exercise to introduce basic probability concepts. The implementation of the exercise is outlined and the resulting presentation of various probability concepts are described.
Propensity, Probability, and Quantum Theory
NASA Astrophysics Data System (ADS)
Ballentine, Leslie E.
2016-08-01
Quantum mechanics and probability theory share one peculiarity. Both have well established mathematical formalisms, yet both are subject to controversy about the meaning and interpretation of their basic concepts. Since probability plays a fundamental role in QM, the conceptual problems of one theory can affect the other. We first classify the interpretations of probability into three major classes: (a) inferential probability, (b) ensemble probability, and (c) propensity. Class (a) is the basis of inductive logic; (b) deals with the frequencies of events in repeatable experiments; (c) describes a form of causality that is weaker than determinism. An important, but neglected, paper by P. Humphreys demonstrated that propensity must differ mathematically, as well as conceptually, from probability, but he did not develop a theory of propensity. Such a theory is developed in this paper. Propensity theory shares many, but not all, of the axioms of probability theory. As a consequence, propensity supports the Law of Large Numbers from probability theory, but does not support Bayes theorem. Although there are particular problems within QM to which any of the classes of probability may be applied, it is argued that the intrinsic quantum probabilities (calculated from a state vector or density matrix) are most naturally interpreted as quantum propensities. This does not alter the familiar statistical interpretation of QM. But the interpretation of quantum states as representing knowledge is untenable. Examples show that a density matrix fails to represent knowledge.
The Probabilities of Unique Events
2012-08-30
probabilities into quantum mechanics, and some psychologists have argued that they have a role to play in accounting for errors in judgment [30]. But, in...Discussion The mechanisms underlying naive estimates of the probabilities of unique events are largely inaccessible to consciousness , but they...Can quantum probability provide a new direc- tion for cognitive modeling? Behavioral and Brain Sciences (in press). 31. Paolacci G, Chandler J
Probability Surveys, Conditional Probability, and Ecological Risk Assessment
We show that probability-based environmental resource monitoring programs, such as the U.S. Environmental Protection Agency’s (U.S. EPA) Environmental Monitoring and Assessment Program, and conditional probability analysis can serve as a basis for estimating ecological risk over ...
PROBABILITY SURVEYS, CONDITIONAL PROBABILITIES, AND ECOLOGICAL RISK ASSESSMENT
We show that probability-based environmental resource monitoring programs, such as U.S. Environmental Protection Agency's (U.S. EPA) Environmental Monitoring and Asscssment Program EMAP) can be analyzed with a conditional probability analysis (CPA) to conduct quantitative probabi...
PROBABILITY SURVEYS , CONDITIONAL PROBABILITIES AND ECOLOGICAL RISK ASSESSMENT
We show that probability-based environmental resource monitoring programs, such as the U.S. Environmental Protection Agency's (U.S. EPA) Environmental Monitoring and Assessment Program, and conditional probability analysis can serve as a basis for estimating ecological risk over ...
The relationship between species detection probability and local extinction probability
Alpizar-Jara, R.; Nichols, J.D.; Hines, J.E.; Sauer, J.R.; Pollock, K.H.; Rosenberry, C.S.
2004-01-01
In community-level ecological studies, generally not all species present in sampled areas are detected. Many authors have proposed the use of estimation methods that allow detection probabilities that are < 1 and that are heterogeneous among species. These methods can also be used to estimate community-dynamic parameters such as species local extinction probability and turnover rates (Nichols et al. Ecol Appl 8:1213-1225; Conserv Biol 12:1390-1398). Here, we present an ad hoc approach to estimating community-level vital rates in the presence of joint heterogeneity of detection probabilities and vital rates. The method consists of partitioning the number of species into two groups using the detection frequencies and then estimating vital rates (e.g., local extinction probabilities) for each group. Estimators from each group are combined in a weighted estimator of vital rates that accounts for the effect of heterogeneity. Using data from the North American Breeding Bird Survey, we computed such estimates and tested the hypothesis that detection probabilities and local extinction probabilities were negatively related. Our analyses support the hypothesis that species detection probability covaries negatively with local probability of extinction and turnover rates. A simulation study was conducted to assess the performance of vital parameter estimators as well as other estimators relevant to questions about heterogeneity, such as coefficient of variation of detection probabilities and proportion of species in each group. Both the weighted estimator suggested in this paper and the original unweighted estimator for local extinction probability performed fairly well and provided no basis for preferring one to the other.
The relationship between species detection probability and local extinction probability
Alpizar-Jara, R.; Nichols, J.D.; Hines, J.E.; Sauer, J.R.; Pollock, K.H.; Rosenberry, C.S.
2004-01-01
In community-level ecological studies, generally not all species present in sampled areas are detected. Many authors have proposed the use of estimation methods that allow detection probabilities that are <1 and that are heterogeneous among species. These methods can also be used to estimate community-dynamic parameters such as species local extinction probability and turnover rates (Nichols et al. Ecol Appl 8:1213-1225; Conserv Biol 12:1390-1398). Here, we present an ad hoc approach to estimating community-level vital rates in the presence of joint heterogeneity of detection probabilities and vital rates. The method consists of partitioning the number of species into two groups using the detection frequencies and then estimating vital rates (e.g., local extinction probabilities) for each group. Estimators from each group are combined in a weighted estimator of vital rates that accounts for the effect of heterogeneity. Using data from the North American Breeding Bird Survey, we computed such estimates and tested the hypothesis that detection probabilities and local extinction probabilities were negatively related. Our analyses support the hypothesis that species detection probability covaries negatively with local probability of extinction and turnover rates. A simulation study was conducted to assess the performance of vital parameter estimators as well as other estimators relevant to questions about heterogeneity, such as coefficient of variation of detection probabilities and proportion of species in each group. Both the weighted estimator suggested in this paper and the original unweighted estimator for local extinction probability performed fairly well and provided no basis for preferring one to the other.
Capture probabilities for secondary resonances
NASA Technical Reports Server (NTRS)
Malhotra, Renu
1990-01-01
A perturbed pendulum model is used to analyze secondary resonances, and it is shown that a self-similarity between secondary and primary resonances exists. Henrard's (1982) theory is used to obtain formulas for the capture probability into secondary resonances. The tidal evolution of Miranda and Umbriel is considered as an example, and significant probabilities of capture into secondary resonances are found.
Updating: learning versus supposing.
Zhao, Jiaying; Crupi, Vincenzo; Tentori, Katya; Fitelson, Branden; Osherson, Daniel
2012-09-01
Bayesian orthodoxy posits a tight relationship between conditional probability and updating. Namely, the probability of an event A after learning B should equal the conditional probability of A given B prior to learning B. We examine whether ordinary judgment conforms to the orthodox view. In three experiments we found substantial differences between the conditional probability of an event A supposing an event B compared to the probability of A after having learned B. Specifically, supposing B appears to have less impact on the credibility of A than learning that B is true.
Updating: Learning versus Supposing
ERIC Educational Resources Information Center
Zhao, Jiaying; Crupi, Vincenzo; Tentori, Katya; Fitelson, Branden; Osherson, Daniel
2012-01-01
Bayesian orthodoxy posits a tight relationship between conditional probability and updating. Namely, the probability of an event "A" after learning "B" should equal the conditional probability of "A" given "B" prior to learning "B". We examine whether ordinary judgment conforms to the orthodox view. In three experiments we found substantial…
Definition of the Neutrosophic Probability
NASA Astrophysics Data System (ADS)
Smarandache, Florentin
2014-03-01
Neutrosophic probability (or likelihood) [1995] is a particular case of the neutrosophic measure. It is an estimation of an event (different from indeterminacy) to occur, together with an estimation that some indeterminacy may occur, and the estimation that the event does not occur. The classical probability deals with fair dice, coins, roulettes, spinners, decks of cards, random works, while neutrosophic probability deals with unfair, imperfect such objects and processes. For example, if we toss a regular die on an irregular surface which has cracks, then it is possible to get the die stuck on one of its edges or vertices in a crack (indeterminate outcome). The sample space is in this case: {1, 2, 3, 4, 5, 6, indeterminacy}. So, the probability of getting, for example 1, is less than 1/6. Since there are seven outcomes. The neutrosophic probability is a generalization of the classical probability because, when the chance of determinacy of a stochastic process is zero, these two probabilities coincide. The Neutrosophic Probability that of an event A occurs is NP (A) = (ch (A) , ch (indetA) , ch (A ̲)) = (T , I , F) , where T , I , F are subsets of [0,1], and T is the chance that A occurs, denoted ch(A); I is the indeterminate chance related to A, ch(indetermA) ; and F is the chance that A does not occur, ch (A ̲) . So, NP is a generalization of the Imprecise Probability as well. If T, I, and F are crisp numbers then: - 0 <= T + I + F <=3+ . We used the same notations (T,I,F) as in neutrosophic logic and set.
Failure probability under parameter uncertainty.
Gerrard, R; Tsanakas, A
2011-05-01
In many problems of risk analysis, failure is equivalent to the event of a random risk factor exceeding a given threshold. Failure probabilities can be controlled if a decisionmaker is able to set the threshold at an appropriate level. This abstract situation applies, for example, to environmental risks with infrastructure controls; to supply chain risks with inventory controls; and to insurance solvency risks with capital controls. However, uncertainty around the distribution of the risk factor implies that parameter error will be present and the measures taken to control failure probabilities may not be effective. We show that parameter uncertainty increases the probability (understood as expected frequency) of failures. For a large class of loss distributions, arising from increasing transformations of location-scale families (including the log-normal, Weibull, and Pareto distributions), the article shows that failure probabilities can be exactly calculated, as they are independent of the true (but unknown) parameters. Hence it is possible to obtain an explicit measure of the effect of parameter uncertainty on failure probability. Failure probability can be controlled in two different ways: (1) by reducing the nominal required failure probability, depending on the size of the available data set, and (2) by modifying of the distribution itself that is used to calculate the risk control. Approach (1) corresponds to a frequentist/regulatory view of probability, while approach (2) is consistent with a Bayesian/personalistic view. We furthermore show that the two approaches are consistent in achieving the required failure probability. Finally, we briefly discuss the effects of data pooling and its systemic risk implications.
Cluster membership probability: polarimetric approach
NASA Astrophysics Data System (ADS)
Medhi, Biman J.; Tamura, Motohide
2013-04-01
Interstellar polarimetric data of the six open clusters Hogg 15, NGC 6611, NGC 5606, NGC 6231, NGC 5749 and NGC 6250 have been used to estimate the membership probability for the stars within them. For proper-motion member stars, the membership probability estimated using the polarimetric data is in good agreement with the proper-motion cluster membership probability. However, for proper-motion non-member stars, the membership probability estimated by the polarimetric method is in total disagreement with the proper-motion cluster membership probability. The inconsistencies in the determined memberships may be because of the fundamental differences between the two methods of determination: one is based on stellar proper motion in space and the other is based on selective extinction of the stellar output by the asymmetric aligned dust grains present in the interstellar medium. The results and analysis suggest that the scatter of the Stokes vectors q (per cent) and u (per cent) for the proper-motion member stars depends on the interstellar and intracluster differential reddening in the open cluster. It is found that this method could be used to estimate the cluster membership probability if we have additional polarimetric and photometric information for a star to identify it as a probable member/non-member of a particular cluster, such as the maximum wavelength value (λmax), the unit weight error of the fit (σ1), the dispersion in the polarimetric position angles (overline{ɛ }), reddening (E(B - V)) or the differential intracluster reddening (ΔE(B - V)). This method could also be used to estimate the membership probability of known member stars having no membership probability as well as to resolve disagreements about membership among different proper-motion surveys.
Holographic Probabilities in Eternal Inflation
NASA Astrophysics Data System (ADS)
Bousso, Raphael
2006-11-01
In the global description of eternal inflation, probabilities for vacua are notoriously ambiguous. The local point of view is preferred by holography and naturally picks out a simple probability measure. It is insensitive to large expansion factors or lifetimes and so resolves a recently noted paradox. Any cosmological measure must be complemented with the probability for observers to emerge in a given vacuum. In lieu of anthropic criteria, I propose to estimate this by the entropy that can be produced in a local patch. This allows for prior-free predictions.
Local estimation of posterior class probabilities to minimize classification errors.
Guerrero-Curieses, Alicia; Cid-Sueiro, Jesús; Alaiz-Rodríguez, Rocío; Figueiras-Vidal, Aníbal R
2004-03-01
Decision theory shows that the optimal decision is a function of the posterior class probabilities. More specifically, in binary classification, the optimal decision is based on the comparison of the posterior probabilities with some threshold. Therefore, the most accurate estimates of the posterior probabilities are required near these decision thresholds. This paper discusses the design of objective functions that provide more accurate estimates of the probability values, taking into account the characteristics of each decision problem. We propose learning algorithms based on the stochastic gradient minimization of these loss functions. We show that the performance of the classifier is improved when these algorithms behave like sample selectors: samples near the decision boundary are the most relevant during learning.
Logic, probability, and human reasoning.
Johnson-Laird, P N; Khemlani, Sangeet S; Goodwin, Geoffrey P
2015-04-01
This review addresses the long-standing puzzle of how logic and probability fit together in human reasoning. Many cognitive scientists argue that conventional logic cannot underlie deductions, because it never requires valid conclusions to be withdrawn - not even if they are false; it treats conditional assertions implausibly; and it yields many vapid, although valid, conclusions. A new paradigm of probability logic allows conclusions to be withdrawn and treats conditionals more plausibly, although it does not address the problem of vapidity. The theory of mental models solves all of these problems. It explains how people reason about probabilities and postulates that the machinery for reasoning is itself probabilistic. Recent investigations accordingly suggest a way to integrate probability and deduction.
Dinosaurs, Dinosaur Eggs, and Probability.
ERIC Educational Resources Information Center
Teppo, Anne R.; Hodgson, Ted
2001-01-01
Outlines several recommendations for teaching probability in the secondary school. Offers an activity that employs simulation by hand and using a programmable calculator in which geometry, analytical geometry, and discrete mathematics are explored. (KHR)
Joint probabilities and quantum cognition
NASA Astrophysics Data System (ADS)
de Barros, J. Acacio
2012-12-01
In this paper we discuss the existence of joint probability distributions for quantumlike response computations in the brain. We do so by focusing on a contextual neural-oscillator model shown to reproduce the main features of behavioral stimulus-response theory. We then exhibit a simple example of contextual random variables not having a joint probability distribution, and describe how such variables can be obtained from neural oscillators, but not from a quantum observable algebra.
A Quantum Probability Model of Causal Reasoning
Trueblood, Jennifer S.; Busemeyer, Jerome R.
2012-01-01
People can often outperform statistical methods and machine learning algorithms in situations that involve making inferences about the relationship between causes and effects. While people are remarkably good at causal reasoning in many situations, there are several instances where they deviate from expected responses. This paper examines three situations where judgments related to causal inference problems produce unexpected results and describes a quantum inference model based on the axiomatic principles of quantum probability theory that can explain these effects. Two of the three phenomena arise from the comparison of predictive judgments (i.e., the conditional probability of an effect given a cause) with diagnostic judgments (i.e., the conditional probability of a cause given an effect). The third phenomenon is a new finding examining order effects in predictive causal judgments. The quantum inference model uses the notion of incompatibility among different causes to account for all three phenomena. Psychologically, the model assumes that individuals adopt different points of view when thinking about different causes. The model provides good fits to the data and offers a coherent account for all three causal reasoning effects thus proving to be a viable new candidate for modeling human judgment. PMID:22593747
A quantum probability model of causal reasoning.
Trueblood, Jennifer S; Busemeyer, Jerome R
2012-01-01
People can often outperform statistical methods and machine learning algorithms in situations that involve making inferences about the relationship between causes and effects. While people are remarkably good at causal reasoning in many situations, there are several instances where they deviate from expected responses. This paper examines three situations where judgments related to causal inference problems produce unexpected results and describes a quantum inference model based on the axiomatic principles of quantum probability theory that can explain these effects. Two of the three phenomena arise from the comparison of predictive judgments (i.e., the conditional probability of an effect given a cause) with diagnostic judgments (i.e., the conditional probability of a cause given an effect). The third phenomenon is a new finding examining order effects in predictive causal judgments. The quantum inference model uses the notion of incompatibility among different causes to account for all three phenomena. Psychologically, the model assumes that individuals adopt different points of view when thinking about different causes. The model provides good fits to the data and offers a coherent account for all three causal reasoning effects thus proving to be a viable new candidate for modeling human judgment.
Normal probability plots with confidence.
Chantarangsi, Wanpen; Liu, Wei; Bretz, Frank; Kiatsupaibul, Seksan; Hayter, Anthony J; Wan, Fang
2015-01-01
Normal probability plots are widely used as a statistical tool for assessing whether an observed simple random sample is drawn from a normally distributed population. The users, however, have to judge subjectively, if no objective rule is provided, whether the plotted points fall close to a straight line. In this paper, we focus on how a normal probability plot can be augmented by intervals for all the points so that, if the population distribution is normal, then all the points should fall into the corresponding intervals simultaneously with probability 1-α. These simultaneous 1-α probability intervals provide therefore an objective mean to judge whether the plotted points fall close to the straight line: the plotted points fall close to the straight line if and only if all the points fall into the corresponding intervals. The powers of several normal probability plot based (graphical) tests and the most popular nongraphical Anderson-Darling and Shapiro-Wilk tests are compared by simulation. Based on this comparison, recommendations are given in Section 3 on which graphical tests should be used in what circumstances. An example is provided to illustrate the methods.
Detonation probabilities of high explosives
Eisenhawer, S.W.; Bott, T.F.; Bement, T.R.
1995-07-01
The probability of a high explosive violent reaction (HEVR) following various events is an extremely important aspect of estimating accident-sequence frequency for nuclear weapons dismantlement. In this paper, we describe the development of response curves for insults to PBX 9404, a conventional high-performance explosive used in US weapons. The insults during dismantlement include drops of high explosive (HE), strikes of tools and components on HE, and abrasion of the explosive. In the case of drops, we combine available test data on HEVRs and the results of flooring certification tests to estimate the HEVR probability. For other insults, it was necessary to use expert opinion. We describe the expert solicitation process and the methods used to consolidate the responses. The HEVR probabilities obtained from both approaches are compared.
Interference of probabilities in dynamics
Zak, Michail
2014-08-15
A new class of dynamical systems with a preset type of interference of probabilities is introduced. It is obtained from the extension of the Madelung equation by replacing the quantum potential with a specially selected feedback from the Liouville equation. It has been proved that these systems are different from both Newtonian and quantum systems, but they can be useful for modeling spontaneous collective novelty phenomena when emerging outputs are qualitatively different from the weighted sum of individual inputs. Formation of language and fast decision-making process as potential applications of the probability interference is discussed.
Knowledge typology for imprecise probabilities.
Wilson, G. D.; Zucker, L. J.
2002-01-01
When characterizing the reliability of a complex system there are often gaps in the data available for specific subsystems or other factors influencing total system reliability. At Los Alamos National Laboratory we employ ethnographic methods to elicit expert knowledge when traditional data is scarce. Typically, we elicit expert knowledge in probabilistic terms. This paper will explore how we might approach elicitation if methods other than probability (i.e., Dempster-Shafer, or fuzzy sets) prove more useful for quantifying certain types of expert knowledge. Specifically, we will consider if experts have different types of knowledge that may be better characterized in ways other than standard probability theory.
Dinov, Ivo D; Siegrist, Kyle; Pearl, Dennis K; Kalinin, Alexandr; Christou, Nicolas
2016-06-01
Probability distributions are useful for modeling, simulation, analysis, and inference on varieties of natural processes and physical phenomena. There are uncountably many probability distributions. However, a few dozen families of distributions are commonly defined and are frequently used in practice for problem solving, experimental applications, and theoretical studies. In this paper, we present a new computational and graphical infrastructure, the Distributome, which facilitates the discovery, exploration and application of diverse spectra of probability distributions. The extensible Distributome infrastructure provides interfaces for (human and machine) traversal, search, and navigation of all common probability distributions. It also enables distribution modeling, applications, investigation of inter-distribution relations, as well as their analytical representations and computational utilization. The entire Distributome framework is designed and implemented as an open-source, community-built, and Internet-accessible infrastructure. It is portable, extensible and compatible with HTML5 and Web2.0 standards (http://Distributome.org). We demonstrate two types of applications of the probability Distributome resources: computational research and science education. The Distributome tools may be employed to address five complementary computational modeling applications (simulation, data-analysis and inference, model-fitting, examination of the analytical, mathematical and computational properties of specific probability distributions, and exploration of the inter-distributional relations). Many high school and college science, technology, engineering and mathematics (STEM) courses may be enriched by the use of modern pedagogical approaches and technology-enhanced methods. The Distributome resources provide enhancements for blended STEM education by improving student motivation, augmenting the classical curriculum with interactive webapps, and overhauling the
Stretching Probability Explorations with Geoboards
ERIC Educational Resources Information Center
Wheeler, Ann; Champion, Joe
2016-01-01
Students are faced with many transitions in their middle school mathematics classes. To build knowledge, skills, and confidence in the key areas of algebra and geometry, students often need to practice using numbers and polygons in a variety of contexts. Teachers also want students to explore ideas from probability and statistics. Teachers know…
On probability-possibility transformations
NASA Technical Reports Server (NTRS)
Klir, George J.; Parviz, Behzad
1992-01-01
Several probability-possibility transformations are compared in terms of the closeness of preserving second-order properties. The comparison is based on experimental results obtained by computer simulation. Two second-order properties are involved in this study: noninteraction of two distributions and projections of a joint distribution.
Children's Understanding of Posterior Probability
ERIC Educational Resources Information Center
Girotto, Vittorio; Gonzalez, Michael
2008-01-01
Do young children have a basic intuition of posterior probability? Do they update their decisions and judgments in the light of new evidence? We hypothesized that they can do so extensionally, by considering and counting the various ways in which an event may or may not occur. The results reported in this paper showed that from the age of five,…
Comments on quantum probability theory.
Sloman, Steven
2014-01-01
Quantum probability theory (QP) is the best formal representation available of the most common form of judgment involving attribute comparison (inside judgment). People are capable, however, of judgments that involve proportions over sets of instances (outside judgment). Here, the theory does not do so well. I discuss the theory both in terms of descriptive adequacy and normative appropriateness.
Probability Simulation in Middle School.
ERIC Educational Resources Information Center
Lappan, Glenda; Winter, M. J.
1980-01-01
Two simulations designed to teach probability to middle-school age pupils are presented. The first simulates the one-on-one foul shot simulation in basketball; the second deals with collecting a set of six cereal box prizes by buying boxes containing one toy each. (MP)
GPS: Geometry, Probability, and Statistics
ERIC Educational Resources Information Center
Field, Mike
2012-01-01
It might be said that for most occupations there is now less of a need for mathematics than there was say fifty years ago. But, the author argues, geometry, probability, and statistics constitute essential knowledge for everyone. Maybe not the geometry of Euclid, but certainly geometrical ways of thinking that might enable us to describe the world…
Teaching Probability with the Support of the R Statistical Software
ERIC Educational Resources Information Center
dos Santos Ferreira, Robson; Kataoka, Verônica Yumi; Karrer, Monica
2014-01-01
The objective of this paper is to discuss aspects of high school students' learning of probability in a context where they are supported by the statistical software R. We report on the application of a teaching experiment, constructed using the perspective of Gal's probabilistic literacy and Papert's constructionism. The results show improvement…
Time-dependent earthquake probabilities
Gomberg, J.; Belardinelli, M.E.; Cocco, M.; Reasenberg, P.
2005-01-01
We have attempted to provide a careful examination of a class of approaches for estimating the conditional probability of failure of a single large earthquake, particularly approaches that account for static stress perturbations to tectonic loading as in the approaches of Stein et al. (1997) and Hardebeck (2004). We have loading as in the framework based on a simple, generalized rate change formulation and applied it to these two approaches to show how they relate to one another. We also have attempted to show the connection between models of seismicity rate changes applied to (1) populations of independent faults as in background and aftershock seismicity and (2) changes in estimates of the conditional probability of failures of different members of a the notion of failure rate corresponds to successive failures of different members of a population of faults. The latter application requires specification of some probability distribution (density function of PDF) that describes some population of potential recurrence times. This PDF may reflect our imperfect knowledge of when past earthquakes have occurred on a fault (epistemic uncertainty), the true natural variability in failure times, or some combination of both. We suggest two end-member conceptual single-fault models that may explain natural variability in recurrence times and suggest how they might be distinguished observationally. When viewed deterministically, these single-fault patch models differ significantly in their physical attributes, and when faults are immature, they differ in their responses to stress perturbations. Estimates of conditional failure probabilities effectively integrate over a range of possible deterministic fault models, usually with ranges that correspond to mature faults. Thus conditional failure probability estimates usually should not differ significantly for these models. Copyright 2005 by the American Geophysical Union.
Understanding Y haplotype matching probability.
Brenner, Charles H
2014-01-01
The Y haplotype population-genetic terrain is better explored from a fresh perspective rather than by analogy with the more familiar autosomal ideas. For haplotype matching probabilities, versus for autosomal matching probabilities, explicit attention to modelling - such as how evolution got us where we are - is much more important while consideration of population frequency is much less so. This paper explores, extends, and explains some of the concepts of "Fundamental problem of forensic mathematics - the evidential strength of a rare haplotype match". That earlier paper presented and validated a "kappa method" formula for the evidential strength when a suspect matches a previously unseen haplotype (such as a Y-haplotype) at the crime scene. Mathematical implications of the kappa method are intuitive and reasonable. Suspicions to the contrary raised in rest on elementary errors. Critical to deriving the kappa method or any sensible evidential calculation is understanding that thinking about haplotype population frequency is a red herring; the pivotal question is one of matching probability. But confusion between the two is unfortunately institutionalized in much of the forensic world. Examples make clear why (matching) probability is not (population) frequency and why uncertainty intervals on matching probabilities are merely confused thinking. Forensic matching calculations should be based on a model, on stipulated premises. The model inevitably only approximates reality, and any error in the results comes only from error in the model, the inexactness of the approximation. Sampling variation does not measure that inexactness and hence is not helpful in explaining evidence and is in fact an impediment. Alternative haplotype matching probability approaches that various authors have considered are reviewed. Some are based on no model and cannot be taken seriously. For the others, some evaluation of the models is discussed. Recent evidence supports the adequacy of
Probability summation--a critique.
Laming, Donald
2013-03-01
This Discussion Paper seeks to kill off probability summation, specifically the high-threshold assumption, as an explanatory idea in visual science. In combination with a Weibull function of a parameter of about 4, probability summation can accommodate, to within the limits of experimental error, the shape of the detectability function for contrast, the reduction in threshold that results from the combination of widely separated grating components, summation with respect to duration at threshold, and some instances, but not all, of spatial summation. But it has repeated difficulty with stimuli below threshold, because it denies the availability of input from such stimuli. All the phenomena listed above, and many more, can be accommodated equally accurately by signal-detection theory combined with an accelerated nonlinear transform of small, near-threshold, contrasts. This is illustrated with a transform that is the fourth power for the smallest contrasts, but tends to linear above threshold. Moreover, this particular transform can be derived from elementary properties of sensory neurons. Probability summation cannot be regarded as a special case of a more general theory, because it depends essentially on the 19th-century notion of a high fixed threshold. It is simply an obstruction to further progress.
A Lakatosian Encounter with Probability
ERIC Educational Resources Information Center
Chick, Helen
2010-01-01
There is much to be learned and pondered by reading "Proofs and Refutations" by Imre Lakatos (Lakatos, 1976). It highlights the importance of mathematical definitions, and how definitions evolve to capture the essence of the object they are defining. It also provides an exhilarating encounter with the ups and downs of the mathematical reasoning…
Objective Probability and Quantum Fuzziness
NASA Astrophysics Data System (ADS)
Mohrhoff, U.
2009-02-01
This paper offers a critique of the Bayesian interpretation of quantum mechanics with particular focus on a paper by Caves, Fuchs, and Schack containing a critique of the “objective preparations view” or OPV. It also aims to carry the discussion beyond the hardened positions of Bayesians and proponents of the OPV. Several claims made by Caves et al. are rebutted, including the claim that different pure states may legitimately be assigned to the same system at the same time, and the claim that the quantum nature of a preparation device cannot legitimately be ignored. Both Bayesians and proponents of the OPV regard the time dependence of a quantum state as the continuous dependence on time of an evolving state of some kind. This leads to a false dilemma: quantum states are either objective states of nature or subjective states of belief. In reality they are neither. The present paper views the aforesaid dependence as a dependence on the time of the measurement to whose possible outcomes the quantum state serves to assign probabilities. This makes it possible to recognize the full implications of the only testable feature of the theory, viz., the probabilities it assigns to measurement outcomes. Most important among these are the objective fuzziness of all relative positions and momenta and the consequent incomplete spatiotemporal differentiation of the physical world. The latter makes it possible to draw a clear distinction between the macroscopic and the microscopic. This in turn makes it possible to understand the special status of measurements in all standard formulations of the theory. Whereas Bayesians have written contemptuously about the “folly” of conjoining “objective” to “probability,” there are various reasons why quantum-mechanical probabilities can be considered objective, not least the fact that they are needed to quantify an objective fuzziness. But this cannot be appreciated without giving thought to the makeup of the world, which
Probability for Weather and Climate
NASA Astrophysics Data System (ADS)
Smith, L. A.
2013-12-01
Over the last 60 years, the availability of large-scale electronic computers has stimulated rapid and significant advances both in meteorology and in our understanding of the Earth System as a whole. The speed of these advances was due, in large part, to the sudden ability to explore nonlinear systems of equations. The computer allows the meteorologist to carry a physical argument to its conclusion; the time scales of weather phenomena then allow the refinement of physical theory, numerical approximation or both in light of new observations. Prior to this extension, as Charney noted, the practicing meteorologist could ignore the results of theory with good conscience. Today, neither the practicing meteorologist nor the practicing climatologist can do so, but to what extent, and in what contexts, should they place the insights of theory above quantitative simulation? And in what circumstances can one confidently estimate the probability of events in the world from model-based simulations? Despite solid advances of theory and insight made possible by the computer, the fidelity of our models of climate differs in kind from the fidelity of models of weather. While all prediction is extrapolation in time, weather resembles interpolation in state space, while climate change is fundamentally an extrapolation. The trichotomy of simulation, observation and theory which has proven essential in meteorology will remain incomplete in climate science. Operationally, the roles of probability, indeed the kinds of probability one has access too, are different in operational weather forecasting and climate services. Significant barriers to forming probability forecasts (which can be used rationally as probabilities) are identified. Monte Carlo ensembles can explore sensitivity, diversity, and (sometimes) the likely impact of measurement uncertainty and structural model error. The aims of different ensemble strategies, and fundamental differences in ensemble design to support of
The Black Hole Formation Probability
NASA Astrophysics Data System (ADS)
Clausen, Drew R.; Piro, Anthony; Ott, Christian D.
2015-01-01
A longstanding question in stellar evolution is which massive stars produce black holes (BHs) rather than neutron stars (NSs) upon death. It has been common practice to assume that a given zero-age main sequence (ZAMS) mass star (and perhaps a given metallicity) simply produces either an NS or a BH, but this fails to account for a myriad of other variables that may effect this outcome, such as spin, binarity, or even stochastic differences in the stellar structure near core collapse. We argue that instead a probabilistic description of NS versus BH formation may be better suited to account for the current uncertainties in understanding how massive stars die. Using the observed BH mass distribution from Galactic X-ray binaries, we investigate the probability that a star will make a BH as a function of its ZAMS mass. Although the shape of the black hole formation probability function is poorly constrained by current measurements, we believe that this framework is an important new step toward better understanding BH formation. We also consider some of the implications of this probability distribution, from its impact on the chemical enrichment from massive stars, to its connection with the structure of the core at the time of collapse, to the birth kicks that black holes receive. A probabilistic description of BH formation will be a useful input for future population synthesis studies that are interested in the formation of X-ray binaries, the nature and event rate of gravitational wave sources, and answering questions about chemical enrichment.
Lectures on probability and statistics
Yost, G.P.
1984-09-01
These notes are based on a set of statistics lectures delivered at Imperial College to the first-year postgraduate students in High Energy Physics. They are designed for the professional experimental scientist. We begin with the fundamentals of probability theory, in which one makes statements about the set of possible outcomes of an experiment, based upon a complete a priori understanding of the experiment. For example, in a roll of a set of (fair) dice, one understands a priori that any given side of each die is equally likely to turn up. From that, we can calculate the probability of any specified outcome. We finish with the inverse problem, statistics. Here, one begins with a set of actual data (e.g., the outcomes of a number of rolls of the dice), and attempts to make inferences about the state of nature which gave those data (e.g., the likelihood of seeing any given side of any given die turn up). This is a much more difficult problem, of course, and one's solutions often turn out to be unsatisfactory in one respect or another.
Modality, probability, and mental models.
Hinterecker, Thomas; Knauff, Markus; Johnson-Laird, P N
2016-10-01
We report 3 experiments investigating novel sorts of inference, such as: A or B or both. Therefore, possibly (A and B). Where the contents were sensible assertions, for example, Space tourism will achieve widespread popularity in the next 50 years or advances in material science will lead to the development of antigravity materials in the next 50 years, or both. Most participants accepted the inferences as valid, though they are invalid in modal logic and in probabilistic logic too. But, the theory of mental models predicts that individuals should accept them. In contrast, inferences of this sort—A or B but not both. Therefore, A or B or both—are both logically valid and probabilistically valid. Yet, as the model theory also predicts, most reasoners rejected them. The participants’ estimates of probabilities showed that their inferences tended not to be based on probabilistic validity, but that they did rate acceptable conclusions as more probable than unacceptable conclusions. We discuss the implications of the results for current theories of reasoning.
MSPI False Indication Probability Simulations
Dana Kelly; Kurt Vedros; Robert Youngblood
2011-03-01
This paper examines false indication probabilities in the context of the Mitigating System Performance Index (MSPI), in order to investigate the pros and cons of different approaches to resolving two coupled issues: (1) sensitivity to the prior distribution used in calculating the Bayesian-corrected unreliability contribution to the MSPI, and (2) whether (in a particular plant configuration) to model the fuel oil transfer pump (FOTP) as a separate component, or integrally to its emergency diesel generator (EDG). False indication probabilities were calculated for the following situations: (1) all component reliability parameters at their baseline values, so that the true indication is green, meaning that an indication of white or above would be false positive; (2) one or more components degraded to the extent that the true indication would be (mid) white, and “false” would be green (negative) or yellow (negative) or red (negative). In key respects, this was the approach taken in NUREG-1753. The prior distributions examined were the constrained noninformative (CNI) prior used currently by the MSPI, a mixture of conjugate priors, the Jeffreys noninformative prior, a nonconjugate log(istic)-normal prior, and the minimally informative prior investigated in (Kelly et al., 2010). The mid-white performance state was set at ?CDF = ?10 ? 10-6/yr. For each simulated time history, a check is made of whether the calculated ?CDF is above or below 10-6/yr. If the parameters were at their baseline values, and ?CDF > 10-6/yr, this is counted as a false positive. Conversely, if one or all of the parameters are set to values corresponding to ?CDF > 10-6/yr but that time history’s ?CDF < 10-6/yr, this is counted as a false negative indication. The false indication (positive or negative) probability is then estimated as the number of false positive or negative counts divided by the number of time histories (100,000). Results are presented for a set of base case parameter values
WITPO (What Is the Probability Of).
ERIC Educational Resources Information Center
Ericksen, Donna Bird; And Others
1991-01-01
Included in this probability board game are the requirements, the rules, the board, and 44 sample questions. This game can be used as a probability unit review for practice on basic skills and algorithms, such as computing compound probability and using Pascal's triangle to solve binomial probability problems. (JJK)
Associativity and normative credal probability.
Snow, P
2002-01-01
Cox's Theorem is a widely cited motivation for probabilistic models of uncertain belief. The theorem relates the associativity of the logical connectives to that of the arithmetic operations of probability. Recent questions about the correctness of Cox's Theorem have been resolved, but there are new questions about one functional equation used by Cox in 1946. This equation is missing from his later work. Advances in knowledge since 1946 and changes in Cox's research interests explain the equation's disappearance. Other associativity-based motivations avoid functional equations altogether, and so may be more transparently applied to finite domains and discrete beliefs. A discrete counterpart of Cox's Theorem can be assembled from results that have been in the literature since 1959.
Classifier calibration using splined empirical probabilities in clinical risk prediction.
Gaudoin, René; Montana, Giovanni; Jones, Simon; Aylin, Paul; Bottle, Alex
2015-06-01
The aims of supervised machine learning (ML) applications fall into three broad categories: classification, ranking, and calibration/probability estimation. Many ML methods and evaluation techniques relate to the first two. Nevertheless, there are many applications where having an accurate probability estimate is of great importance. Deriving accurate probabilities from the output of a ML method is therefore an active area of research, resulting in several methods to turn a ranking into class probability estimates. In this manuscript we present a method, splined empirical probabilities, based on the receiver operating characteristic (ROC) to complement existing algorithms such as isotonic regression. Unlike most other methods it works with a cumulative quantity, the ROC curve, and as such can be tagged onto an ROC analysis with minor effort. On a diverse set of measures of the quality of probability estimates (Hosmer-Lemeshow, Kullback-Leibler divergence, differences in the cumulative distribution function) using simulated and real health care data, our approach compares favourably with the standard calibration method, the pool adjacent violators algorithm used to perform isotonic regression.
Fusion probability in heavy nuclei
NASA Astrophysics Data System (ADS)
Banerjee, Tathagata; Nath, S.; Pal, Santanu
2015-03-01
Background: Fusion between two massive nuclei is a very complex process and is characterized by three stages: (a) capture inside the potential barrier, (b) formation of an equilibrated compound nucleus (CN), and (c) statistical decay of the CN leading to a cold evaporation residue (ER) or fission. The second stage is the least understood of the three and is the most crucial in predicting yield of superheavy elements (SHE) formed in complete fusion reactions. Purpose: A systematic study of average fusion probability,
Scene text detection based on probability map and hierarchical model
NASA Astrophysics Data System (ADS)
Zhou, Gang; Liu, Yuehu
2012-06-01
Scene text detection is an important step for the text-based information extraction system. This problem is challenging due to the variations of size, unknown colors, and background complexity. We present a novel algorithm to robustly detect text in scene images. To segment text candidate connected components (CC) from images, a text probability map consisting of the text position and scale information is estimated by a text region detector. To filter out the non-text CCs, a hierarchical model consisting of two classifiers in cascade is utilized. The first stage of the model estimates text probabilities with unary component features. The second stage classifier is trained with both probability features and similarity features. Since the proposed method is learning-based, there are very few manual parameters required. Experimental results on the public benchmark ICDAR dataset show that our algorithm outperforms other state-of-the-art methods.
Trajectory versus probability density entropy.
Bologna, M; Grigolini, P; Karagiorgis, M; Rosa, A
2001-07-01
We show that the widely accepted conviction that a connection can be established between the probability density entropy and the Kolmogorov-Sinai (KS) entropy is questionable. We adopt the definition of density entropy as a functional of a distribution density whose time evolution is determined by a transport equation, conceived as the only prescription to use for the calculation. Although the transport equation is built up for the purpose of affording a picture equivalent to that stemming from trajectory dynamics, no direct use of trajectory time evolution is allowed, once the transport equation is defined. With this definition in mind we prove that the detection of a time regime of increase of the density entropy with a rate identical to the KS entropy is possible only in a limited number of cases. The proposals made by some authors to establish a connection between the two entropies in general, violate our definition of density entropy and imply the concept of trajectory, which is foreign to that of density entropy.
Shin, Seung Jun; Wu, Yichao
2014-07-01
This is a discussion of the papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler.
Task specificity of attention training: the case of probability cuing.
Jiang, Yuhong V; Swallow, Khena M; Won, Bo-Yeong; Cistera, Julia D; Rosenbaum, Gail M
2015-01-01
Statistical regularities in our environment enhance perception and modulate the allocation of spatial attention. Surprisingly little is known about how learning-induced changes in spatial attention transfer across tasks. In this study, we investigated whether a spatial attentional bias learned in one task transfers to another. Most of the experiments began with a training phase in which a search target was more likely to be located in one quadrant of the screen than in the other quadrants. An attentional bias toward the high-probability quadrant developed during training (probability cuing). In a subsequent, testing phase, the target's location distribution became random. In addition, the training and testing phases were based on different tasks. Probability cuing did not transfer between visual search and a foraging-like task. However, it did transfer between various types of visual search tasks that differed in stimuli and difficulty. These data suggest that different visual search tasks share a common and transferrable learned attentional bias. However, this bias is not shared by high-level, decision-making tasks such as foraging.
THE BLACK HOLE FORMATION PROBABILITY
Clausen, Drew; Piro, Anthony L.; Ott, Christian D.
2015-02-01
A longstanding question in stellar evolution is which massive stars produce black holes (BHs) rather than neutron stars (NSs) upon death. It has been common practice to assume that a given zero-age main sequence (ZAMS) mass star (and perhaps a given metallicity) simply produces either an NS or a BH, but this fails to account for a myriad of other variables that may effect this outcome, such as spin, binarity, or even stochastic differences in the stellar structure near core collapse. We argue that instead a probabilistic description of NS versus BH formation may be better suited to account for the current uncertainties in understanding how massive stars die. We present an initial exploration of the probability that a star will make a BH as a function of its ZAMS mass, P {sub BH}(M {sub ZAMS}). Although we find that it is difficult to derive a unique P {sub BH}(M {sub ZAMS}) using current measurements of both the BH mass distribution and the degree of chemical enrichment by massive stars, we demonstrate how P {sub BH}(M {sub ZAMS}) changes with these various observational and theoretical uncertainties. We anticipate that future studies of Galactic BHs and theoretical studies of core collapse will refine P {sub BH}(M {sub ZAMS}) and argue that this framework is an important new step toward better understanding BH formation. A probabilistic description of BH formation will be useful as input for future population synthesis studies that are interested in the formation of X-ray binaries, the nature and event rate of gravitational wave sources, and answering questions about chemical enrichment.
The Black Hole Formation Probability
NASA Astrophysics Data System (ADS)
Clausen, Drew; Piro, Anthony L.; Ott, Christian D.
2015-02-01
A longstanding question in stellar evolution is which massive stars produce black holes (BHs) rather than neutron stars (NSs) upon death. It has been common practice to assume that a given zero-age main sequence (ZAMS) mass star (and perhaps a given metallicity) simply produces either an NS or a BH, but this fails to account for a myriad of other variables that may effect this outcome, such as spin, binarity, or even stochastic differences in the stellar structure near core collapse. We argue that instead a probabilistic description of NS versus BH formation may be better suited to account for the current uncertainties in understanding how massive stars die. We present an initial exploration of the probability that a star will make a BH as a function of its ZAMS mass, P BH(M ZAMS). Although we find that it is difficult to derive a unique P BH(M ZAMS) using current measurements of both the BH mass distribution and the degree of chemical enrichment by massive stars, we demonstrate how P BH(M ZAMS) changes with these various observational and theoretical uncertainties. We anticipate that future studies of Galactic BHs and theoretical studies of core collapse will refine P BH(M ZAMS) and argue that this framework is an important new step toward better understanding BH formation. A probabilistic description of BH formation will be useful as input for future population synthesis studies that are interested in the formation of X-ray binaries, the nature and event rate of gravitational wave sources, and answering questions about chemical enrichment.
Canonical Probability Distributions for Model Building, Learning, and Inference
2006-07-14
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Using Playing Cards to Differentiate Probability Interpretations
ERIC Educational Resources Information Center
López Puga, Jorge
2014-01-01
The aprioristic (classical, naïve and symmetric) and frequentist interpretations of probability are commonly known. Bayesian or subjective interpretation of probability is receiving increasing attention. This paper describes an activity to help students differentiate between the three types of probability interpretations.
Teaching Probabilities and Statistics to Preschool Children
ERIC Educational Resources Information Center
Pange, Jenny
2003-01-01
This study considers the teaching of probabilities and statistics to a group of preschool children using traditional classroom activities and Internet games. It was clear from this study that children can show a high level of understanding of probabilities and statistics, and demonstrate high performance in probability games. The use of Internet…
The Cognitive Substrate of Subjective Probability
ERIC Educational Resources Information Center
Nilsson, Hakan; Olsson, Henrik; Juslin, Peter
2005-01-01
The prominent cognitive theories of probability judgment were primarily developed to explain cognitive biases rather than to account for the cognitive processes in probability judgment. In this article the authors compare 3 major theories of the processes and representations in probability judgment: the representativeness heuristic, implemented as…
ERIC Educational Resources Information Center
Kleiman, Glenn; Zweig, Karen
With the Seeing and Thinking Mathematically materials, students learn mathematics by doing mathematics, by using and connecting mathematical ideas, and by actively constructing their own understandings. In this unit students learn to see probability through a mathematical lens by exploring and creating games and simulations and by applying the…
Dynamic Encoding of Speech Sequence Probability in Human Temporal Cortex
Leonard, Matthew K.; Bouchard, Kristofer E.; Tang, Claire
2015-01-01
Sensory processing involves identification of stimulus features, but also integration with the surrounding sensory and cognitive context. Previous work in animals and humans has shown fine-scale sensitivity to context in the form of learned knowledge about the statistics of the sensory environment, including relative probabilities of discrete units in a stream of sequential auditory input. These statistics are a defining characteristic of one of the most important sequential signals humans encounter: speech. For speech, extensive exposure to a language tunes listeners to the statistics of sound sequences. To address how speech sequence statistics are neurally encoded, we used high-resolution direct cortical recordings from human lateral superior temporal cortex as subjects listened to words and nonwords with varying transition probabilities between sound segments. In addition to their sensitivity to acoustic features (including contextual features, such as coarticulation), we found that neural responses dynamically encoded the language-level probability of both preceding and upcoming speech sounds. Transition probability first negatively modulated neural responses, followed by positive modulation of neural responses, consistent with coordinated predictive and retrospective recognition processes, respectively. Furthermore, transition probability encoding was different for real English words compared with nonwords, providing evidence for online interactions with high-order linguistic knowledge. These results demonstrate that sensory processing of deeply learned stimuli involves integrating physical stimulus features with their contextual sequential structure. Despite not being consciously aware of phoneme sequence statistics, listeners use this information to process spoken input and to link low-level acoustic representations with linguistic information about word identity and meaning. PMID:25948269
Dynamic encoding of speech sequence probability in human temporal cortex.
Leonard, Matthew K; Bouchard, Kristofer E; Tang, Claire; Chang, Edward F
2015-05-06
Sensory processing involves identification of stimulus features, but also integration with the surrounding sensory and cognitive context. Previous work in animals and humans has shown fine-scale sensitivity to context in the form of learned knowledge about the statistics of the sensory environment, including relative probabilities of discrete units in a stream of sequential auditory input. These statistics are a defining characteristic of one of the most important sequential signals humans encounter: speech. For speech, extensive exposure to a language tunes listeners to the statistics of sound sequences. To address how speech sequence statistics are neurally encoded, we used high-resolution direct cortical recordings from human lateral superior temporal cortex as subjects listened to words and nonwords with varying transition probabilities between sound segments. In addition to their sensitivity to acoustic features (including contextual features, such as coarticulation), we found that neural responses dynamically encoded the language-level probability of both preceding and upcoming speech sounds. Transition probability first negatively modulated neural responses, followed by positive modulation of neural responses, consistent with coordinated predictive and retrospective recognition processes, respectively. Furthermore, transition probability encoding was different for real English words compared with nonwords, providing evidence for online interactions with high-order linguistic knowledge. These results demonstrate that sensory processing of deeply learned stimuli involves integrating physical stimulus features with their contextual sequential structure. Despite not being consciously aware of phoneme sequence statistics, listeners use this information to process spoken input and to link low-level acoustic representations with linguistic information about word identity and meaning.
A Probability Distribution over Latent Causes, in the Orbitofrontal Cortex
Niv, Yael; Norman, Kenneth A.
2016-01-01
The orbitofrontal cortex (OFC) has been implicated in both the representation of “state,” in studies of reinforcement learning and decision making, and also in the representation of “schemas,” in studies of episodic memory. Both of these cognitive constructs require a similar inference about the underlying situation or “latent cause” that generates our observations at any given time. The statistically optimal solution to this inference problem is to use Bayes' rule to compute a posterior probability distribution over latent causes. To test whether such a posterior probability distribution is represented in the OFC, we tasked human participants with inferring a probability distribution over four possible latent causes, based on their observations. Using fMRI pattern similarity analyses, we found that BOLD activity in the OFC is best explained as representing the (log-transformed) posterior distribution over latent causes. Furthermore, this pattern explained OFC activity better than other task-relevant alternatives, such as the most probable latent cause, the most recent observation, or the uncertainty over latent causes. SIGNIFICANCE STATEMENT Our world is governed by hidden (latent) causes that we cannot observe, but which generate the observations we see. A range of high-level cognitive processes require inference of a probability distribution (or “belief distribution”) over the possible latent causes that might be generating our current observations. This is true for reinforcement learning and decision making (where the latent cause comprises the true “state” of the task), and for episodic memory (where memories are believed to be organized by the inferred situation or “schema”). Using fMRI, we show that this belief distribution over latent causes is encoded in patterns of brain activity in the orbitofrontal cortex, an area that has been separately implicated in the representations of both states and schemas. PMID:27466328
Dawson, Michael R W; Dupuis, Brian; Spetch, Marcia L; Kelly, Debbie M
2009-08-01
The matching law (Herrnstein 1961) states that response rates become proportional to reinforcement rates; this is related to the empirical phenomenon called probability matching (Vulkan 2000). Here, we show that a simple artificial neural network generates responses consistent with probability matching. This behavior was then used to create an operant procedure for network learning. We use the multiarmed bandit (Gittins 1989), a classic problem of choice behavior, to illustrate that operant training balances exploiting the bandit arm expected to pay off most frequently with exploring other arms. Perceptrons provide a medium for relating results from neural networks, genetic algorithms, animal learning, contingency theory, reinforcement learning, and theories of choice.
Prior probabilities modulate cortical surprise responses: A study of event-related potentials.
Seer, Caroline; Lange, Florian; Boos, Moritz; Dengler, Reinhard; Kopp, Bruno
2016-07-01
The human brain predicts events in its environment based on expectations, and unexpected events are surprising. When probabilistic contingencies in the environment are precisely instructed, the individual can form expectations based on quantitative probabilistic information ('inference-based learning'). In contrast, when probabilistic contingencies are imprecisely instructed, expectations are formed based on the individual's cumulative experience ('experience-based learning'). Here, we used the urn-ball paradigm to investigate how variations in prior probabilities and in the precision of information about these priors modulate choice behavior and event-related potential (ERP) correlates of surprise. In the urn-ball paradigm, participants are repeatedly forced to infer hidden states responsible for generating observable events, given small samples of factual observations. We manipulated prior probabilities of the states, and we rendered the priors calculable or incalculable, respectively. The analysis of choice behavior revealed that the tendency to consider prior probabilities when making decisions about hidden states was stronger when prior probabilities were calculable, at least in some of our participants. Surprise-related P3b amplitudes were observed in both the calculable and the incalculable prior probability condition. In contrast, calculability of prior probabilities modulated anteriorly distributed ERP amplitudes: when prior probabilities were calculable, surprising events elicited enhanced P3a amplitudes. However, when prior probabilities were incalculable, surprise was associated with enhanced N2 amplitudes. Furthermore, interindividual variability in reliance on prior probabilities was associated with attenuated P3b surprise responses under calculable in comparison to incalculable prior probabilities. Our results suggest two distinct neural systems for probabilistic learning that are recruited depending on contextual cues such as the precision of
NASA Astrophysics Data System (ADS)
Hoffmann, Achim; Mahidadia, Ashesh
human comprehension as it is essentially a large collection of probability values. In Sect. 9, we present a generic method for improving accuracy of a given learner by generatingmultiple classifiers using variations of the training data. While this works well in most cases, the resulting classifiers have significantly increased complexity and, hence, tend to destroy the human readability of the learning result that a single learner may produce. Section 10 contains a summary, mentions briefly other techniques not discussed in this chapter and presents outlook on the potential of machine learning in the future.
UT Biomedical Informatics Lab (BMIL) probability wheel
NASA Astrophysics Data System (ADS)
Huang, Sheng-Cheng; Lee, Sara; Wang, Allen; Cantor, Scott B.; Sun, Clement; Fan, Kaili; Reece, Gregory P.; Kim, Min Soon; Markey, Mia K.
A probability wheel app is intended to facilitate communication between two people, an "investigator" and a "participant", about uncertainties inherent in decision-making. Traditionally, a probability wheel is a mechanical prop with two colored slices. A user adjusts the sizes of the slices to indicate the relative value of the probabilities assigned to them. A probability wheel can improve the adjustment process and attenuate the effect of anchoring bias when it is used to estimate or communicate probabilities of outcomes. The goal of this work was to develop a mobile application of the probability wheel that is portable, easily available, and more versatile. We provide a motivating example from medical decision-making, but the tool is widely applicable for researchers in the decision sciences.
UT Biomedical Informatics Lab (BMIL) Probability Wheel.
Huang, Sheng-Cheng; Lee, Sara; Wang, Allen; Cantor, Scott B; Sun, Clement; Fan, Kaili; Reece, Gregory P; Kim, Min Soon; Markey, Mia K
2016-01-01
A probability wheel app is intended to facilitate communication between two people, an "investigator" and a "participant," about uncertainties inherent in decision-making. Traditionally, a probability wheel is a mechanical prop with two colored slices. A user adjusts the sizes of the slices to indicate the relative value of the probabilities assigned to them. A probability wheel can improve the adjustment process and attenuate the effect of anchoring bias when it is used to estimate or communicate probabilities of outcomes. The goal of this work was to develop a mobile application of the probability wheel that is portable, easily available, and more versatile. We provide a motivating example from medical decision-making, but the tool is widely applicable for researchers in the decision sciences.
Derivation of quantum probability from measurement
NASA Astrophysics Data System (ADS)
Herbut, Fedor
2016-05-01
To begin with, it is pointed out that the form of the quantum probability formula originates in the very initial state of the object system as seen when the state is expanded with the eigenprojectors of the measured observable. Making use of the probability reproducibility condition, which is a key concept in unitary measurement theory, one obtains the relevant coherent distribution of the complete-measurement results in the final unitary-measurement state in agreement with the mentioned probability formula. Treating the transition from the final unitary, or premeasurement, state, where all possible results are present, to one complete-measurement result sketchily in the usual way, the well-known probability formula is derived. In conclusion it is pointed out that the entire argument is only formal unless one makes it physical assuming that the quantum probability law is valid in the extreme case of probability-one (certain) events (projectors).
Error probability performance of unbalanced QPSK receivers
NASA Technical Reports Server (NTRS)
Simon, M. K.
1978-01-01
A simple technique for calculating the error probability performance and associated noisy reference loss of practical unbalanced QPSK receivers is presented. The approach is based on expanding the error probability conditioned on the loop phase error in a power series in the loop phase error and then, keeping only the first few terms of this series, averaging this conditional error probability over the probability density function of the loop phase error. Doing so results in an expression for the average error probability which is in the form of a leading term representing the ideal (perfect synchronization references) performance plus a term proportional to the mean-squared crosstalk. Thus, the additional error probability due to noisy synchronization references occurs as an additive term proportional to the mean-squared phase jitter directly associated with the receiver's tracking loop. Similar arguments are advanced to give closed-form results for the noisy reference loss itself.
UT Biomedical Informatics Lab (BMIL) Probability Wheel
Lee, Sara; Wang, Allen; Cantor, Scott B.; Sun, Clement; Fan, Kaili; Reece, Gregory P.; Kim, Min Soon; Markey, Mia K.
2016-01-01
A probability wheel app is intended to facilitate communication between two people, an “investigator” and a “participant,” about uncertainties inherent in decision-making. Traditionally, a probability wheel is a mechanical prop with two colored slices. A user adjusts the sizes of the slices to indicate the relative value of the probabilities assigned to them. A probability wheel can improve the adjustment process and attenuate the effect of anchoring bias when it is used to estimate or communicate probabilities of outcomes. The goal of this work was to develop a mobile application of the probability wheel that is portable, easily available, and more versatile. We provide a motivating example from medical decision-making, but the tool is widely applicable for researchers in the decision sciences. PMID:28105462
Probability and Quantum Paradigms: the Interplay
Kracklauer, A. F.
2007-12-03
Since the introduction of Born's interpretation of quantum wave functions as yielding the probability density of presence, Quantum Theory and Probability have lived in a troubled symbiosis. Problems arise with this interpretation because quantum probabilities exhibit features alien to usual probabilities, namely non Boolean structure and non positive-definite phase space probability densities. This has inspired research into both elaborate formulations of Probability Theory and alternate interpretations for wave functions. Herein the latter tactic is taken and a suggested variant interpretation of wave functions based on photo detection physics proposed, and some empirical consequences are considered. Although incomplete in a few details, this variant is appealing in its reliance on well tested concepts and technology.
Total variation denoising of probability measures using iterated function systems with probabilities
NASA Astrophysics Data System (ADS)
La Torre, Davide; Mendivil, Franklin; Vrscay, Edward R.
2017-01-01
In this paper we present a total variation denoising problem for probability measures using the set of fixed point probability measures of iterated function systems with probabilities IFSP. By means of the Collage Theorem for contraction mappings, we provide an upper bound for this problem that can be solved by determining a set of probabilities.
Generating quantum-measurement probabilities from an optimality principle
NASA Astrophysics Data System (ADS)
Suykens, Johan A. K.
2013-05-01
An alternative formulation to the (generalized) Born rule is presented. It involves estimating an unknown model from a finite set of measurement operators on the state. An optimality principle is given that relates to achieving bounded solutions by regularizing the unknown parameters in the model. The objective function maximizes a lower bound on the quadratic Renyi classical entropy. The unknowns of the model in the primal are interpreted as transition witnesses. An interpretation of the Born rule in terms of fidelity is given with respect to transition witnesses for the pure state and the case of positive operator-valued measures (POVMs). The models for generating quantum-measurement probabilities apply to orthogonal projective measurements and POVM measurements, and to isolated and open systems with Kraus maps. A straightforward and constructive method is proposed for deriving the probability rule, which is based on Lagrange duality. An analogy is made with a kernel-based method for probability mass function estimation, for which similarities and differences are discussed. These combined insights from quantum mechanics, statistical modeling, and machine learning provide an alternative way of generating quantum-measurement probabilities.
Estimation of probability densities using scale-free field theories.
Kinney, Justin B
2014-07-01
The question of how best to estimate a continuous probability density from finite data is an intriguing open problem at the interface of statistics and physics. Previous work has argued that this problem can be addressed in a natural way using methods from statistical field theory. Here I describe results that allow this field-theoretic approach to be rapidly and deterministically computed in low dimensions, making it practical for use in day-to-day data analysis. Importantly, this approach does not impose a privileged length scale for smoothness of the inferred probability density, but rather learns a natural length scale from the data due to the tradeoff between goodness of fit and an Occam factor. Open source software implementing this method in one and two dimensions is provided.
Neural Correlates of the Divergence of Instrumental Probability Distributions
Wang, Shuo; Zhang, June; O'Doherty, John P.
2013-01-01
Flexible action selection requires knowledge about how alternative actions impact the environment: a “cognitive map” of instrumental contingencies. Reinforcement learning theories formalize this map as a set of stochastic relationships between actions and states, such that for any given action considered in a current state, a probability distribution is specified over possible outcome states. Here, we show that activity in the human inferior parietal lobule correlates with the divergence of such outcome distributions–a measure that reflects whether discrimination between alternative actions increases the controllability of the future–and, further, that this effect is dissociable from those of other information theoretic and motivational variables, such as outcome entropy, action values, and outcome utilities. Our results suggest that, although ultimately combined with reward estimates to generate action values, outcome probability distributions associated with alternative actions may be contrasted independently of valence computations, to narrow the scope of the action selection problem. PMID:23884955
Cost functions to estimate a posteriori probabilities in multiclass problems.
Cid-Sueiro, J; Arribas, J I; Urbán-Muñoz, S; Figueiras-Vidal, A R
1999-01-01
The problem of designing cost functions to estimate a posteriori probabilities in multiclass problems is addressed in this paper. We establish necessary and sufficient conditions that these costs must satisfy in one-class one-output networks whose outputs are consistent with probability laws. We focus our attention on a particular subset of the corresponding cost functions; those which verify two usually interesting properties: symmetry and separability (well-known cost functions, such as the quadratic cost or the cross entropy are particular cases in this subset). Finally, we present a universal stochastic gradient learning rule for single-layer networks, in the sense of minimizing a general version of these cost functions for a wide family of nonlinear activation functions.
Estimation of probability densities using scale-free field theories
NASA Astrophysics Data System (ADS)
Kinney, Justin B.
2014-07-01
The question of how best to estimate a continuous probability density from finite data is an intriguing open problem at the interface of statistics and physics. Previous work has argued that this problem can be addressed in a natural way using methods from statistical field theory. Here I describe results that allow this field-theoretic approach to be rapidly and deterministically computed in low dimensions, making it practical for use in day-to-day data analysis. Importantly, this approach does not impose a privileged length scale for smoothness of the inferred probability density, but rather learns a natural length scale from the data due to the tradeoff between goodness of fit and an Occam factor. Open source software implementing this method in one and two dimensions is provided.
Lattice Duality: The Origin of Probability and Entropy
NASA Technical Reports Server (NTRS)
Knuth, Kevin H.
2004-01-01
Bayesian probability theory is an inference calculus, which originates from a generalization of inclusion on the Boolean lattice of logical assertions to a degree of inclusion represented by a real number. Dual to this lattice is the distributive lattice of questions constructed from the ordered set of down-sets of assertions, which forms the foundation of the calculus of inquiry-a generalization of information theory. In this paper we introduce this novel perspective on these spaces in which machine learning is performed and discuss the relationship between these results and several proposed generalizations of information theory in the literature.
Simulations of Probabilities for Quantum Computing
NASA Technical Reports Server (NTRS)
Zak, M.
1996-01-01
It has been demonstrated that classical probabilities, and in particular, probabilistic Turing machine, can be simulated by combining chaos and non-LIpschitz dynamics, without utilization of any man-made devices (such as random number generators). Self-organizing properties of systems coupling simulated and calculated probabilities and their link to quantum computations are discussed.
47 CFR 1.1623 - Probability calculation.
Code of Federal Regulations, 2012 CFR
2012-10-01
... 47 Telecommunication 1 2012-10-01 2012-10-01 false Probability calculation. 1.1623 Section 1.1623 Telecommunication FEDERAL COMMUNICATIONS COMMISSION GENERAL PRACTICE AND PROCEDURE Grants by Random Selection Random Selection Procedures for Mass Media Services General Procedures § 1.1623 Probability calculation. (a)...
Correlation as Probability of Common Descent.
ERIC Educational Resources Information Center
Falk, Ruma; Well, Arnold D.
1996-01-01
One interpretation of the Pearson product-moment correlation ("r"), correlation as the probability of originating from common descent, important to the genetic measurement of inbreeding, is examined. The conditions under which "r" can be interpreted as the probability of "identity by descent" are specified, and the…
Probability: A Matter of Life and Death
ERIC Educational Resources Information Center
Hassani, Mehdi; Kippen, Rebecca; Mills, Terence
2016-01-01
Life tables are mathematical tables that document probabilities of dying and life expectancies at different ages in a society. Thus, the life table contains some essential features of the health of a population. Probability is often regarded as a difficult branch of mathematics. Life tables provide an interesting approach to introducing concepts…
Phonotactic Probabilities in Young Children's Speech Production
ERIC Educational Resources Information Center
Zamuner, Tania S.; Gerken, Louann; Hammond, Michael
2004-01-01
This research explores the role of phonotactic probability in two-year-olds' production of coda consonants. Twenty-nine children were asked to repeat CVC non-words that were used as labels for pictures of imaginary animals. The CVC non-words were controlled for their phonotactic probabilities, neighbourhood densities, word-likelihood ratings, and…
47 CFR 1.1623 - Probability calculation.
Code of Federal Regulations, 2010 CFR
2010-10-01
... 47 Telecommunication 1 2010-10-01 2010-10-01 false Probability calculation. 1.1623 Section 1.1623 Telecommunication FEDERAL COMMUNICATIONS COMMISSION GENERAL PRACTICE AND PROCEDURE Random Selection Procedures for Mass Media Services General Procedures § 1.1623 Probability calculation. (a) All calculations shall be computed to no less than...
Teaching Statistics and Probability: 1981 Yearbook.
ERIC Educational Resources Information Center
Shulte, Albert P., Ed.; Smart, James R., Ed.
This 1981 yearbook of the National Council of Teachers of Mathematics (NCTM) offers classroom ideas for teaching statistics and probability, viewed as important topics in the school mathematics curriculum. Statistics and probability are seen as appropriate because they: (1) provide meaningful applications of mathematics at all levels; (2) provide…
Teaching Probability: A Socio-Constructivist Perspective
ERIC Educational Resources Information Center
Sharma, Sashi
2015-01-01
There is a considerable and rich literature on students' misconceptions in probability. However, less attention has been paid to the development of students' probabilistic thinking in the classroom. This paper offers a sequence, grounded in socio-constructivist perspective for teaching probability.
Stimulus Probability Effects in Absolute Identification
ERIC Educational Resources Information Center
Kent, Christopher; Lamberts, Koen
2016-01-01
This study investigated the effect of stimulus presentation probability on accuracy and response times in an absolute identification task. Three schedules of presentation were used to investigate the interaction between presentation probability and stimulus position within the set. Data from individual participants indicated strong effects of…
Malawian Students' Meanings for Probability Vocabulary
ERIC Educational Resources Information Center
Kazima, Mercy
2007-01-01
The paper discusses findings of a study that investigated Malawian students' meanings for some probability vocabulary. The study explores the meanings that, prior to instruction, students assign to some words that are commonly used in teaching probability. The aim is to have some insight into the meanings that students bring to the classroom. The…
Probability Simulations by Non-Lipschitz Chaos
NASA Technical Reports Server (NTRS)
Zak, Michail
1996-01-01
It has been demonstrated that classical probabilities, and in particular, probabilistic Turing machine, can be simulated by combining chaos and non-Lipschitz dynamics, without utilization of any man-made devices. Self-organizing properties of systems coupling simulated and calculated probabilities and their link to quantum computations are discussed.
Laboratory-Tutorial Activities for Teaching Probability
ERIC Educational Resources Information Center
Wittmann, Michael C.; Morgan, Jeffrey T.; Feeley, Roger E.
2006-01-01
We report on the development of students' ideas of probability and probability density in a University of Maine laboratory-based general education physics course called "Intuitive Quantum Physics". Students in the course are generally math phobic with unfavorable expectations about the nature of physics and their ability to do it. We…
Probability Issues in without Replacement Sampling
ERIC Educational Resources Information Center
Joarder, A. H.; Al-Sabah, W. S.
2007-01-01
Sampling without replacement is an important aspect in teaching conditional probabilities in elementary statistics courses. Different methods proposed in different texts for calculating probabilities of events in this context are reviewed and their relative merits and limitations in applications are pinpointed. An alternative representation of…
Average Transmission Probability of a Random Stack
ERIC Educational Resources Information Center
Lu, Yin; Miniatura, Christian; Englert, Berthold-Georg
2010-01-01
The transmission through a stack of identical slabs that are separated by gaps with random widths is usually treated by calculating the average of the logarithm of the transmission probability. We show how to calculate the average of the transmission probability itself with the aid of a recurrence relation and derive analytical upper and lower…
Assessment of the probability of contaminating Mars
NASA Technical Reports Server (NTRS)
Judd, B. R.; North, D. W.; Pezier, J. P.
1974-01-01
New methodology is proposed to assess the probability that the planet Mars will by biologically contaminated by terrestrial microorganisms aboard a spacecraft. Present NASA methods are based on the Sagan-Coleman formula, which states that the probability of contamination is the product of the expected microbial release and a probability of growth. The proposed new methodology extends the Sagan-Coleman approach to permit utilization of detailed information on microbial characteristics, the lethality of release and transport mechanisms, and of other information about the Martian environment. Three different types of microbial release are distinguished in the model for assessing the probability of contamination. The number of viable microbes released by each mechanism depends on the bio-burden in various locations on the spacecraft and on whether the spacecraft landing is accomplished according to plan. For each of the three release mechanisms a probability of growth is computed, using a model for transport into an environment suited to microbial growth.
Alternative probability theories for cognitive psychology.
Narens, Louis
2014-01-01
Various proposals for generalizing event spaces for probability functions have been put forth in the mathematical, scientific, and philosophic literatures. In cognitive psychology such generalizations are used for explaining puzzling results in decision theory and for modeling the influence of context effects. This commentary discusses proposals for generalizing probability theory to event spaces that are not necessarily boolean algebras. Two prominent examples are quantum probability theory, which is based on the set of closed subspaces of a Hilbert space, and topological probability theory, which is based on the set of open sets of a topology. Both have been applied to a variety of cognitive situations. This commentary focuses on how event space properties can influence probability concepts and impact cognitive modeling.
Optimizing Probability of Detection Point Estimate Demonstration
NASA Technical Reports Server (NTRS)
Koshti, Ajay M.
2017-01-01
Probability of detection (POD) analysis is used in assessing reliably detectable flaw size in nondestructive evaluation (NDE). MIL-HDBK-18231and associated mh18232POD software gives most common methods of POD analysis. Real flaws such as cracks and crack-like flaws are desired to be detected using these NDE methods. A reliably detectable crack size is required for safe life analysis of fracture critical parts. The paper provides discussion on optimizing probability of detection (POD) demonstration experiments using Point Estimate Method. POD Point estimate method is used by NASA for qualifying special NDE procedures. The point estimate method uses binomial distribution for probability density. Normally, a set of 29 flaws of same size within some tolerance are used in the demonstration. The optimization is performed to provide acceptable value for probability of passing demonstration (PPD) and achieving acceptable value for probability of false (POF) calls while keeping the flaw sizes in the set as small as possible.
Time-dependent landslide probability mapping
Campbell, Russell H.; Bernknopf, Richard L.; ,
1993-01-01
Case studies where time of failure is known for rainfall-triggered debris flows can be used to estimate the parameters of a hazard model in which the probability of failure is a function of time. As an example, a time-dependent function for the conditional probability of a soil slip is estimated from independent variables representing hillside morphology, approximations of material properties, and the duration and rate of rainfall. If probabilities are calculated in a GIS (geomorphic information system ) environment, the spatial distribution of the result for any given hour can be displayed on a map. Although the probability levels in this example are uncalibrated, the method offers a potential for evaluating different physical models and different earth-science variables by comparing the map distribution of predicted probabilities with inventory maps for different areas and different storms. If linked with spatial and temporal socio-economic variables, this method could be used for short-term risk assessment.
Multinomial mixture model with heterogeneous classification probabilities
Holland, M.D.; Gray, B.R.
2011-01-01
Royle and Link (Ecology 86(9):2505-2512, 2005) proposed an analytical method that allowed estimation of multinomial distribution parameters and classification probabilities from categorical data measured with error. While useful, we demonstrate algebraically and by simulations that this method yields biased multinomial parameter estimates when the probabilities of correct category classifications vary among sampling units. We address this shortcoming by treating these probabilities as logit-normal random variables within a Bayesian framework. We use Markov chain Monte Carlo to compute Bayes estimates from a simulated sample from the posterior distribution. Based on simulations, this elaborated Royle-Link model yields nearly unbiased estimates of multinomial and correct classification probability estimates when classification probabilities are allowed to vary according to the normal distribution on the logit scale or according to the Beta distribution. The method is illustrated using categorical submersed aquatic vegetation data. ?? 2010 Springer Science+Business Media, LLC.
Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability.
Dawson, Michael R W; Gupta, Maya
2017-01-01
Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent's environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR) of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned.
Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability
2017-01-01
Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent’s environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR) of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned. PMID:28212422
An introductory analysis of satellite collision probabilities
NASA Astrophysics Data System (ADS)
Carlton-Wippern, Kitt C.
This paper addresses a probailistic approach in assessing the probabilities of a satellite collision occurring due to relative trajectory analyses and probability density functions representing the satellites' position/momentum vectors. The paper is divided into 2 parts: Static and Dynamic Collision Probabilities. In the Static Collision Probability section, the basic phenomenon under study is: given the mean positions and associated position probability density functions for the two objects, calculate the probability that the two objects collide (defined as being within some distance of each other). The paper presents the classic Laplace problem of the probability of arrival, using standard uniform distribution functions. This problem is then extrapolated to show how 'arrival' can be classified as 'collision', how the arrival space geometries map to collision space geometries and how arbitrary position density functions can then be included and integrated into the analysis. In the Dynamic Collision Probability section, the nature of collisions based upon both trajectory and energy considerations is discussed, and that energy states alone cannot be used to completely describe whether or not a collision occurs. This fact invalidates some earlier work on the subject and demonstrates why Liouville's theorem cannot be used in general to describe the constant density of the position/momentum space in which a collision may occur. Future position probability density functions are then shown to be the convolution of the current position and momentum density functions (linear analysis), and the paper further demonstrates the dependency of the future position density functions on time. Strategies for assessing the collision probabilities for two point masses with uncertainties in position and momentum at some given time, and thes integrated with some arbitrary impact volume schema, are then discussed. This presentation concludes with the formulation of a high level design
Prior probability modulates anticipatory activity in category-specific areas.
Trapp, Sabrina; Lepsien, Jöran; Kotz, Sonja A; Bar, Moshe
2016-02-01
Bayesian models are currently a dominant framework for describing human information processing. However, it is not clear yet how major tenets of this framework can be translated to brain processes. In this study, we addressed the neural underpinning of prior probability and its effect on anticipatory activity in category-specific areas. Before fMRI scanning, participants were trained in two behavioral sessions to learn the prior probability and correct order of visual events within a sequence. The events of each sequence included two different presentations of a geometric shape and one picture of either a house or a face, which appeared with either a high or a low likelihood. Each sequence was preceded by a cue that gave participants probabilistic information about which items to expect next. This allowed examining cue-related anticipatory modulation of activity as a function of prior probability in category-specific areas (fusiform face area and parahippocampal place area). Our findings show that activity in the fusiform face area was higher when faces had a higher prior probability. The finding of a difference between levels of expectations is consistent with graded, probabilistically modulated activity, but the data do not rule out the alternative explanation of a categorical neural response. Importantly, these differences were only visible during anticipation, and vanished at the time of stimulus presentation, calling for a functional distinction when considering the effects of prior probability. Finally, there were no anticipatory effects for houses in the parahippocampal place area, suggesting sensitivity to stimulus material when looking at effects of prediction.
Liquefaction probability curves for surficial geologic deposits
Holzer, Thomas L.; Noce, Thomas E.; Bennett, Michael J.
2011-01-01
Liquefaction probability curves that predict the probability of surface manifestations of earthquake-induced liquefaction are developed for 14 different types of surficial geologic units. The units consist of alluvial fan, beach ridge, river delta topset and foreset beds, eolian dune, point bar, flood basin, natural river and alluvial fan levees, abandoned river channel, deep-water lake, lagoonal, sandy artificial fill, and valley train deposits. Probability is conditioned on earthquake magnitude and peak ground acceleration. Curves are developed for water table depths of 1.5 and 5.0 m. Probabilities are derived from complementary cumulative frequency distributions of the liquefaction potential index (LPI) that were computed from 927 cone penetration tests. For natural deposits with a water table at 1.5 m and subjected to a M7.5 earthquake with peak ground acceleration (PGA) = 0.25g, probabilities range from 0.5 for beach ridge, point bar, and deltaic deposits. The curves also were used to assign ranges of liquefaction probabilities to the susceptibility categories proposed previously for different geologic deposits. For the earthquake described here, probabilities for susceptibility categories have ranges of 0–0.08 for low, 0.09–0.30 for moderate, 0.31–0.62 for high, and 0.63–1.00 for very high. Retrospective predictions of liquefaction during historical earthquakes based on the curves compare favorably to observations.
The probability distribution of intense daily precipitation
NASA Astrophysics Data System (ADS)
Cavanaugh, Nicholas R.; Gershunov, Alexander; Panorska, Anna K.; Kozubowski, Tomasz J.
2015-03-01
The probability tail structure of over 22,000 weather stations globally is examined in order to identify the physically and mathematically consistent distribution type for modeling the probability of intense daily precipitation and extremes. Results indicate that when aggregating data annually, most locations are to be considered heavy tailed with statistical significance. When aggregating data by season, it becomes evident that the thickness of the probability tail is related to the variability in precipitation causing events and thus that the fundamental cause of precipitation volatility is weather diversity. These results have both theoretical and practical implications for the modeling of high-frequency climate variability worldwide.
Objective and subjective probability in gene expression.
Velasco, Joel D
2012-09-01
In this paper I address the question of whether the probabilities that appear in models of stochastic gene expression are objective or subjective. I argue that while our best models of the phenomena in question are stochastic models, this fact should not lead us to automatically assume that the processes are inherently stochastic. After distinguishing between models and reality, I give a brief introduction to the philosophical problem of the interpretation of probability statements. I argue that the objective vs. subjective distinction is a false dichotomy and is an unhelpful distinction in this case. Instead, the probabilities in our models of gene expression exhibit standard features of both objectivity and subjectivity.
Characteristic length of the knotting probability revisited
NASA Astrophysics Data System (ADS)
Uehara, Erica; Deguchi, Tetsuo
2015-09-01
We present a self-avoiding polygon (SAP) model for circular DNA in which the radius of impermeable cylindrical segments corresponds to the screening length of double-stranded DNA surrounded by counter ions. For the model we evaluate the probability for a generated SAP with N segments having a given knot K through simulation. We call it the knotting probability of a knot K with N segments for the SAP model. We show that when N is large the most significant factor in the knotting probability is given by the exponentially decaying part exp(-N/NK), where the estimates of parameter NK are consistent with the same value for all the different knots we investigated. We thus call it the characteristic length of the knotting probability. We give formulae expressing the characteristic length as a function of the cylindrical radius rex, i.e. the screening length of double-stranded DNA.
Transition Probability and the ESR Experiment
ERIC Educational Resources Information Center
McBrierty, Vincent J.
1974-01-01
Discusses the use of a modified electron spin resonance apparatus to demonstrate some features of the expression for the transition probability per second between two energy levels. Applications to the third year laboratory program are suggested. (CC)
Inclusion probability with dropout: an operational formula.
Milot, E; Courteau, J; Crispino, F; Mailly, F
2015-05-01
In forensic genetics, a mixture of two or more contributors to a DNA profile is often interpreted using the inclusion probabilities theory. In this paper, we present a general formula for estimating the probability of inclusion (PI, also known as the RMNE probability) from a subset of visible alleles when dropouts are possible. This one-locus formula can easily be extended to multiple loci using the cumulative probability of inclusion. We show that an exact formulation requires fixing the number of contributors, hence to slightly modify the classic interpretation of the PI. We discuss the implications of our results for the enduring debate over the use of PI vs likelihood ratio approaches within the context of low template amplifications.
The low synaptic release probability in vivo.
Borst, J Gerard G
2010-06-01
The release probability, the average probability that an active zone of a presynaptic terminal releases one or more vesicles following an action potential, is tightly regulated. Measurements in cultured neurons or in slices indicate that this probability can vary greatly between synapses, but on average it is estimated to be as high as 0.5. In vivo, however, the size of synaptic potentials is relatively independent of recent history, suggesting that release probability is much lower. Possible causes for this discrepancy include maturational differences, a higher spontaneous activity, a lower extracellular calcium concentration and more prominent tonic inhibition by ambient neurotransmitters during in vivo recordings. Existing evidence thus suggests that under physiological conditions in vivo, presynaptic action potentials trigger the release of neurotransmitter much less frequently than what is observed in in vitro preparations.
Classical and Quantum Spreading of Position Probability
ERIC Educational Resources Information Center
Farina, J. E. G.
1977-01-01
Demonstrates that the standard deviation of the position probability of a particle moving freely in one dimension is a function of the standard deviation of its velocity distribution and time in classical or quantum mechanics. (SL)
On Convergent Probability of a Random Walk
ERIC Educational Resources Information Center
Lee, Y.-F.; Ching, W.-K.
2006-01-01
This note introduces an interesting random walk on a straight path with cards of random numbers. The method of recurrent relations is used to obtain the convergent probability of the random walk with different initial positions.
Robust satisficing and the probability of survival
NASA Astrophysics Data System (ADS)
Ben-Haim, Yakov
2014-01-01
Concepts of robustness are sometimes employed when decisions under uncertainty are made without probabilistic information. We present a theorem that establishes necessary and sufficient conditions for non-probabilistic robustness to be equivalent to the probability of satisfying the specified outcome requirements. When this holds, probability is enhanced (or maximised) by enhancing (or maximising) robustness. Two further theorems establish important special cases. These theorems have implications for success or survival under uncertainty. Applications to foraging and finance are discussed.
Probability, clinical decision making and hypothesis testing
Banerjee, A.; Jadhav, S. L.; Bhawalkar, J. S.
2009-01-01
Few clinicians grasp the true concept of probability expressed in the ‘P value.’ For most, a statistically significant P value is the end of the search for truth. In fact, the opposite is the case. The present paper attempts to put the P value in proper perspective by explaining different types of probabilities, their role in clinical decision making, medical research and hypothesis testing. PMID:21234167
Grounding quantum probability in psychological mechanism.
Love, Bradley C
2013-06-01
Pothos & Busemeyer (P&B) provide a compelling case that quantum probability (QP) theory is a better match to human judgment than is classical probability (CP) theory. However, any theory (QP, CP, or other) phrased solely at the computational level runs the risk of being underconstrained. One suggestion is to ground QP accounts in mechanism, to leverage a wide range of process-level data.
A Manual for Encoding Probability Distributions.
1978-09-01
summary of the most significant information contained in the report. If the report contains a significant bibliography or literature survey, mention it...probability distri- bution. Some terms in the literature that are used synonymously to Encoding: Assessment, Assignment (used for single events in this...sessions conducted as parts of practical decision analyses as well as on experimental evidence in the literature . Probability encoding can be applied
Imprecise Probability Methods for Weapons UQ
Picard, Richard Roy; Vander Wiel, Scott Alan
2016-05-13
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.
Probability distribution of the vacuum energy density
Duplancic, Goran; Stefancic, Hrvoje; Glavan, Drazen
2010-12-15
As the vacuum state of a quantum field is not an eigenstate of the Hamiltonian density, the vacuum energy density can be represented as a random variable. We present an analytical calculation of the probability distribution of the vacuum energy density for real and complex massless scalar fields in Minkowski space. The obtained probability distributions are broad and the vacuum expectation value of the Hamiltonian density is not fully representative of the vacuum energy density.
When probability trees don't work
NASA Astrophysics Data System (ADS)
Chan, K. C.; Lenard, C. T.; Mills, T. M.
2016-08-01
Tree diagrams arise naturally in courses on probability at high school or university, even at an elementary level. Often they are used to depict outcomes and associated probabilities from a sequence of games. A subtle issue is whether or not the Markov condition holds in the sequence of games. We present two examples that illustrate the importance of this issue. Suggestions as to how these examples may be used in a classroom are offered.
Site occupancy models with heterogeneous detection probabilities
Royle, J. Andrew
2006-01-01
Models for estimating the probability of occurrence of a species in the presence of imperfect detection are important in many ecological disciplines. In these ?site occupancy? models, the possibility of heterogeneity in detection probabilities among sites must be considered because variation in abundance (and other factors) among sampled sites induces variation in detection probability (p). In this article, I develop occurrence probability models that allow for heterogeneous detection probabilities by considering several common classes of mixture distributions for p. For any mixing distribution, the likelihood has the general form of a zero-inflated binomial mixture for which inference based upon integrated likelihood is straightforward. A recent paper by Link (2003, Biometrics 59, 1123?1130) demonstrates that in closed population models used for estimating population size, different classes of mixture distributions are indistinguishable from data, yet can produce very different inferences about population size. I demonstrate that this problem can also arise in models for estimating site occupancy in the presence of heterogeneous detection probabilities. The implications of this are discussed in the context of an application to avian survey data and the development of animal monitoring programs.
Infants Segment Continuous Events Using Transitional Probabilities
ERIC Educational Resources Information Center
Stahl, Aimee E.; Romberg, Alexa R.; Roseberry, Sarah; Golinkoff, Roberta Michnick; Hirsh-Pasek, Kathryn
2014-01-01
Throughout their 1st year, infants adeptly detect statistical structure in their environment. However, little is known about whether statistical learning is a primary mechanism for event segmentation. This study directly tests whether statistical learning alone is sufficient to segment continuous events. Twenty-eight 7- to 9-month-old infants…
The Estimation of Tree Posterior Probabilities Using Conditional Clade Probability Distributions
Larget, Bret
2013-01-01
In this article I introduce the idea of conditional independence of separated subtrees as a principle by which to estimate the posterior probability of trees using conditional clade probability distributions rather than simple sample relative frequencies. I describe an algorithm for these calculations and software which implements these ideas. I show that these alternative calculations are very similar to simple sample relative frequencies for high probability trees but are substantially more accurate for relatively low probability trees. The method allows the posterior probability of unsampled trees to be calculated when these trees contain only clades that are in other sampled trees. Furthermore, the method can be used to estimate the total probability of the set of sampled trees which provides a measure of the thoroughness of a posterior sample. [Bayesian phylogenetics; conditional clade distributions; improved accuracy; posterior probabilities of trees.] PMID:23479066
Predicting Robust Learning with the Visual Form of the Moment-by-Moment Learning Curve
ERIC Educational Resources Information Center
Baker, Ryan S.; Hershkovitz, Arnon; Rossi, Lisa M.; Goldstein, Adam B.; Gowda, Sujith M.
2013-01-01
We present a new method for analyzing a student's learning over time for a specific skill: analysis of the graph of the student's moment-by-moment learning over time. Moment-by-moment learning is calculated using a data-mined model that assesses the probability that a student learned a skill or concept at a specific time during learning (Baker,…
Tsunami probability in the Caribbean Region
Parsons, T.; Geist, E.L.
2008-01-01
We calculated tsunami runup probability (in excess of 0.5 m) at coastal sites throughout the Caribbean region. We applied a Poissonian probability model because of the variety of uncorrelated tsunami sources in the region. Coastlines were discretized into 20 km by 20 km cells, and the mean tsunami runup rate was determined for each cell. The remarkable ???500-year empirical record compiled by O'Loughlin and Lander (2003) was used to calculate an empirical tsunami probability map, the first of three constructed for this study. However, it is unclear whether the 500-year record is complete, so we conducted a seismic moment-balance exercise using a finite-element model of the Caribbean-North American plate boundaries and the earthquake catalog, and found that moment could be balanced if the seismic coupling coefficient is c = 0.32. Modeled moment release was therefore used to generate synthetic earthquake sequences to calculate 50 tsunami runup scenarios for 500-year periods. We made a second probability map from numerically-calculated runup rates in each cell. Differences between the first two probability maps based on empirical and numerical-modeled rates suggest that each captured different aspects of tsunami generation; the empirical model may be deficient in primary plate-boundary events, whereas numerical model rates lack backarc fault and landslide sources. We thus prepared a third probability map using Bayesian likelihood functions derived from the empirical and numerical rate models and their attendant uncertainty to weight a range of rates at each 20 km by 20 km coastal cell. Our best-estimate map gives a range of 30-year runup probability from 0 - 30% regionally. ?? irkhaueser 2008.
Learning in a Changing Environment
ERIC Educational Resources Information Center
Speekenbrink, Maarten; Shanks, David R.
2010-01-01
Multiple cue probability learning studies have typically focused on stationary environments. We present 3 experiments investigating learning in changing environments. A fine-grained analysis of the learning dynamics shows that participants were responsive to both abrupt and gradual changes in cue-outcome relations. We found no evidence that…
The Problem with Probability: Why rare hazards feel even rarer
NASA Astrophysics Data System (ADS)
Thompson, K. J.
2013-12-01
Even as scientists improve the accuracy of their forecasts for large-scale events like natural hazards and climate change, a gap remains between the confidence the scientific community has in those estimates, and the skepticism with which the lay public tends to view statements of uncertainty. Beyond the challenges of helping the public to understand probabilistic forecasts lies yet another barrier to effective communication: the fact that even when humans can estimate or state the correct probability of a rare event, we tend to distort that probability in our minds, acting as if the likelihood is higher or lower than we know it to be. A half century of empirical research in psychology and economics leaves us with a clear view of the ways that people interpret stated, or described probabilities--e.g., "There is a 6% chance of a Northridge-sized earthquake occurring in your area in the next 10 years." In the past decade, the focus of cognitive scientists has turned to the other method humans use to learn probabilities: intuitively estimating the chances of a rare event by assessing our personal experience with various outcomes. While it is well understood that described probabilities are over-weighted when they are small (e.g., a 5% chance might be treated more like a 10% or 12% chance), it appears that in many cases, experienced rare probabilities are in fact under-weighted. This distortion is not an under-estimation, and therefore cannot be prevented by reminding people of the described probability. This paper discusses the mechanisms and effects of this difference in the way probability is used when a number is provided, as opposed to when the frequency of a rare event is intuited. In addition to recommendations based on the current state of research on the way people appear to make decisions from experience, suggestions are made for how to present probabilistic information to best take advantage of people's tendencies to either amplify risk or ignore it, as well
Minimal entropy probability paths between genome families.
Ahlbrandt, Calvin; Benson, Gary; Casey, William
2004-05-01
We develop a metric for probability distributions with applications to biological sequence analysis. Our distance metric is obtained by minimizing a functional defined on the class of paths over probability measures on N categories. The underlying mathematical theory is connected to a constrained problem in the calculus of variations. The solution presented is a numerical solution, which approximates the true solution in a set of cases called rich paths where none of the components of the path is zero. The functional to be minimized is motivated by entropy considerations, reflecting the idea that nature might efficiently carry out mutations of genome sequences in such a way that the increase in entropy involved in transformation is as small as possible. We characterize sequences by frequency profiles or probability vectors, in the case of DNA where N is 4 and the components of the probability vector are the frequency of occurrence of each of the bases A, C, G and T. Given two probability vectors a and b, we define a distance function based as the infimum of path integrals of the entropy function H( p) over all admissible paths p(t), 0 < or = t< or =1, with p(t) a probability vector such that p(0)=a and p(1)=b. If the probability paths p(t) are parameterized as y(s) in terms of arc length s and the optimal path is smooth with arc length L, then smooth and "rich" optimal probability paths may be numerically estimated by a hybrid method of iterating Newton's method on solutions of a two point boundary value problem, with unknown distance L between the abscissas, for the Euler-Lagrange equations resulting from a multiplier rule for the constrained optimization problem together with linear regression to improve the arc length estimate L. Matlab code for these numerical methods is provided which works only for "rich" optimal probability vectors. These methods motivate a definition of an elementary distance function which is easier and faster to calculate, works on non
Approximation of Failure Probability Using Conditional Sampling
NASA Technical Reports Server (NTRS)
Giesy. Daniel P.; Crespo, Luis G.; Kenney, Sean P.
2008-01-01
In analyzing systems which depend on uncertain parameters, one technique is to partition the uncertain parameter domain into a failure set and its complement, and judge the quality of the system by estimating the probability of failure. If this is done by a sampling technique such as Monte Carlo and the probability of failure is small, accurate approximation can require so many sample points that the computational expense is prohibitive. Previous work of the authors has shown how to bound the failure event by sets of such simple geometry that their probabilities can be calculated analytically. In this paper, it is shown how to make use of these failure bounding sets and conditional sampling within them to substantially reduce the computational burden of approximating failure probability. It is also shown how the use of these sampling techniques improves the confidence intervals for the failure probability estimate for a given number of sample points and how they reduce the number of sample point analyses needed to achieve a given level of confidence.
Causal inference, probability theory, and graphical insights.
Baker, Stuart G
2013-11-10
Causal inference from observational studies is a fundamental topic in biostatistics. The causal graph literature typically views probability theory as insufficient to express causal concepts in observational studies. In contrast, the view here is that probability theory is a desirable and sufficient basis for many topics in causal inference for the following two reasons. First, probability theory is generally more flexible than causal graphs: Besides explaining such causal graph topics as M-bias (adjusting for a collider) and bias amplification and attenuation (when adjusting for instrumental variable), probability theory is also the foundation of the paired availability design for historical controls, which does not fit into a causal graph framework. Second, probability theory is the basis for insightful graphical displays including the BK-Plot for understanding Simpson's paradox with a binary confounder, the BK2-Plot for understanding bias amplification and attenuation in the presence of an unobserved binary confounder, and the PAD-Plot for understanding the principal stratification component of the paired availability design.
Computing Earthquake Probabilities on Global Scales
NASA Astrophysics Data System (ADS)
Holliday, James R.; Graves, William R.; Rundle, John B.; Turcotte, Donald L.
2016-03-01
Large devastating events in systems such as earthquakes, typhoons, market crashes, electricity grid blackouts, floods, droughts, wars and conflicts, and landslides can be unexpected and devastating. Events in many of these systems display frequency-size statistics that are power laws. Previously, we presented a new method for calculating probabilities for large events in systems such as these. This method counts the number of small events since the last large event and then converts this count into a probability by using a Weibull probability law. We applied this method to the calculation of large earthquake probabilities in California-Nevada, USA. In that study, we considered a fixed geographic region and assumed that all earthquakes within that region, large magnitudes as well as small, were perfectly correlated. In the present article, we extend this model to systems in which the events have a finite correlation length. We modify our previous results by employing the correlation function for near mean field systems having long-range interactions, an example of which is earthquakes and elastic interactions. We then construct an application of the method and show examples of computed earthquake probabilities.
The role of probabilities in physics.
Le Bellac, Michel
2012-09-01
Although modern physics was born in the XVIIth century as a fully deterministic theory in the form of Newtonian mechanics, the use of probabilistic arguments turned out later on to be unavoidable. Three main situations can be distinguished. (1) When the number of degrees of freedom is very large, on the order of Avogadro's number, a detailed dynamical description is not possible, and in fact not useful: we do not care about the velocity of a particular molecule in a gas, all we need is the probability distribution of the velocities. This statistical description introduced by Maxwell and Boltzmann allows us to recover equilibrium thermodynamics, gives a microscopic interpretation of entropy and underlies our understanding of irreversibility. (2) Even when the number of degrees of freedom is small (but larger than three) sensitivity to initial conditions of chaotic dynamics makes determinism irrelevant in practice, because we cannot control the initial conditions with infinite accuracy. Although die tossing is in principle predictable, the approach to chaotic dynamics in some limit implies that our ignorance of initial conditions is translated into a probabilistic description: each face comes up with probability 1/6. (3) As is well-known, quantum mechanics is incompatible with determinism. However, quantum probabilities differ in an essential way from the probabilities introduced previously: it has been shown from the work of John Bell that quantum probabilities are intrinsic and cannot be given an ignorance interpretation based on a hypothetical deeper level of description.
Probability, arrow of time and decoherence
NASA Astrophysics Data System (ADS)
Bacciagaluppi, Guido
This paper relates both to the metaphysics of probability and to the physics of time asymmetry. Using the formalism of decoherent histories, it investigates whether intuitions about intrinsic time directedness that are often associated with probability can be justified in the context of no-collapse approaches to quantum mechanics. The standard (two-vector) approach to time symmetry in the decoherent histories literature is criticised, and an alternative approach is proposed, based on two decoherence conditions ('forwards' and 'backwards') within the one-vector formalism. In turn, considerations of forwards and backwards decoherence and of decoherence and recoherence suggest that a time-directed interpretation of probabilities, if adopted, should be both contingent and perspectival.
Match probabilities in racially admixed populations.
Lange, K
1993-01-01
The calculation of match probabilities is the most contentious issue dividing prosecution and defense experts in the forensic applications of DNA fingerprinting. In particular, defense experts question the applicability of the population genetic laws of Hardy-Weinberg and linkage equilibrium to racially admixed American populations. Linkage equilibrium justifies the product rule for computing match probabilities across loci. The present paper suggests a method of bounding match probabilities that depends on modeling gene descent from ancestral populations to contemporary populations under the assumptions of Hardy-Weinberg and linkage equilibrium only in the ancestral populations. Although these bounds are conservative from the defendant's perspective, they should be small enough in practice to satisfy prosecutors. PMID:8430693
Local Directed Percolation Probability in Two Dimensions
NASA Astrophysics Data System (ADS)
Inui, Norio; Konno, Norio; Komatsu, Genichi; Kameoka, Koichi
1998-01-01
Using the series expansion method and Monte Carlo simulation,we study the directed percolation probability on the square lattice Vn0=\\{ (x,y) \\in {Z}2:x+y=even, 0 ≤ y ≤ n, - y ≤ x ≤ y \\}.We calculate the local percolationprobability Pnl defined as the connection probability between theorigin and a site (0,n). The critical behavior of P∞lis clearly different from the global percolation probability P∞g characterized by a critical exponent βg.An analysis based on the Padé approximants shows βl=2βg.In addition, we find that the series expansion of P2nl can be expressed as a function of Png.
Explosion probability of unexploded ordnance: expert beliefs.
MacDonald, Jacqueline Anne; Small, Mitchell J; Morgan, M G
2008-08-01
This article reports on a study to quantify expert beliefs about the explosion probability of unexploded ordnance (UXO). Some 1,976 sites at closed military bases in the United States are contaminated with UXO and are slated for cleanup, at an estimated cost of $15-140 billion. Because no available technology can guarantee 100% removal of UXO, information about explosion probability is needed to assess the residual risks of civilian reuse of closed military bases and to make decisions about how much to invest in cleanup. This study elicited probability distributions for the chance of UXO explosion from 25 experts in explosive ordnance disposal, all of whom have had field experience in UXO identification and deactivation. The study considered six different scenarios: three different types of UXO handled in two different ways (one involving children and the other involving construction workers). We also asked the experts to rank by sensitivity to explosion 20 different kinds of UXO found at a case study site at Fort Ord, California. We found that the experts do not agree about the probability of UXO explosion, with significant differences among experts in their mean estimates of explosion probabilities and in the amount of uncertainty that they express in their estimates. In three of the six scenarios, the divergence was so great that the average of all the expert probability distributions was statistically indistinguishable from a uniform (0, 1) distribution-suggesting that the sum of expert opinion provides no information at all about the explosion risk. The experts' opinions on the relative sensitivity to explosion of the 20 UXO items also diverged. The average correlation between rankings of any pair of experts was 0.41, which, statistically, is barely significant (p= 0.049) at the 95% confidence level. Thus, one expert's rankings provide little predictive information about another's rankings. The lack of consensus among experts suggests that empirical studies
Exact probability distribution functions for Parrondo's games
NASA Astrophysics Data System (ADS)
Zadourian, Rubina; Saakian, David B.; Klümper, Andreas
2016-12-01
We study the discrete time dynamics of Brownian ratchet models and Parrondo's games. Using the Fourier transform, we calculate the exact probability distribution functions for both the capital dependent and history dependent Parrondo's games. In certain cases we find strong oscillations near the maximum of the probability distribution with two limiting distributions for odd and even number of rounds of the game. Indications of such oscillations first appeared in the analysis of real financial data, but now we have found this phenomenon in model systems and a theoretical understanding of the phenomenon. The method of our work can be applied to Brownian ratchets, molecular motors, and portfolio optimization.
Intrinsic Probability of a Multifractal Set
NASA Astrophysics Data System (ADS)
Hosokawa, Iwao
1991-12-01
It is shown that a self-similar measure isotropically distributed in a d-dimensional set should have its own intermittency exponents equivalent to its own generalized dimensions (in the sense of Hentschel and Procaccia), and that the intermittency exponents are completely designated by an intrinsic probability which governs the spatial distribution of the measure. Based on this, it is proven that the intrinsic probability uniquely determines the spatial distribution of the scaling index α of the measure as well as the so-called f-α spectrum of the multifractal set.
Atomic transition probabilities of Nd I
NASA Astrophysics Data System (ADS)
Stockett, M. H.; Wood, M. P.; Den Hartog, E. A.; Lawler, J. E.
2011-12-01
Fourier transform spectra are used to determine emission branching fractions for 236 lines of the first spectrum of neodymium (Nd i). These branching fractions are converted to absolute atomic transition probabilities using radiative lifetimes from time-resolved laser-induced fluorescence measurements (Den Hartog et al 2011 J. Phys. B: At. Mol. Opt. Phys. 44 225001). The wavelength range of the data set is from 390 to 950 nm. These transition probabilities from emission and laser measurements are compared to relative absorption measurements in order to assess the importance of unobserved infrared branches from selected upper levels.
Probabilities for separating sets of order statistics.
Glueck, D H; Karimpour-Fard, A; Mandel, J; Muller, K E
2010-04-01
Consider a set of order statistics that arise from sorting samples from two different populations, each with their own, possibly different distribution functions. The probability that these order statistics fall in disjoint, ordered intervals and that of the smallest statistics, a certain number come from the first populations is given in terms of the two distribution functions. The result is applied to computing the joint probability of the number of rejections and the number of false rejections for the Benjamini-Hochberg false discovery rate procedure.
Quantum probability and quantum decision-making.
Yukalov, V I; Sornette, D
2016-01-13
A rigorous general definition of quantum probability is given, which is valid not only for elementary events but also for composite events, for operationally testable measurements as well as for inconclusive measurements, and also for non-commuting observables in addition to commutative observables. Our proposed definition of quantum probability makes it possible to describe quantum measurements and quantum decision-making on the same common mathematical footing. Conditions are formulated for the case when quantum decision theory reduces to its classical counterpart and for the situation where the use of quantum decision theory is necessary.
Steering in spin tomographic probability representation
NASA Astrophysics Data System (ADS)
Man'ko, V. I.; Markovich, L. A.
2016-09-01
The steering property known for two-qubit state in terms of specific inequalities for the correlation function is translated for the state of qudit with the spin j = 3 / 2. Since most steering detection inequalities are based on the correlation functions we introduce analogs of such functions for the single qudit systems. The tomographic probability representation for the qudit states is applied. The connection between the correlation function in the two-qubit system and the single qudit is presented in an integral form with an intertwining kernel calculated explicitly in tomographic probability terms.
Determining system maintainability as a probability
Wright, R.E.; Atwood, C.L.
1988-01-01
Maintainability has often been defined in principle as the probability that a system or component can be repaired in a specific time given that it is in a failed state, but presented in practice in terms of mean-time-to-repair. In this paper, formulas are developed for maintainability as a probability, analogous to those for reliability and availability. This formulation is expressed in terms of cut sets, and leads to a natural definition of unmaintainability importance for cut sets and basic events. 6 refs.
Probability in biology: overview of a comprehensive theory of probability in living systems.
Nakajima, Toshiyuki
2013-09-01
Probability is closely related to biological organization and adaptation to the environment. Living systems need to maintain their organizational order by producing specific internal events non-randomly, and must cope with the uncertain environments. These processes involve increases in the probability of favorable events for these systems by reducing the degree of uncertainty of events. Systems with this ability will survive and reproduce more than those that have less of this ability. Probabilistic phenomena have been deeply explored using the mathematical theory of probability since Kolmogorov's axiomatization provided mathematical consistency for the theory. However, the interpretation of the concept of probability remains both unresolved and controversial, which creates problems when the mathematical theory is applied to problems in real systems. In this article, recent advances in the study of the foundations of probability from a biological viewpoint are reviewed, and a new perspective is discussed toward a comprehensive theory of probability for understanding the organization and adaptation of living systems.
Investigating Probability with the NBA Draft Lottery.
ERIC Educational Resources Information Center
Quinn, Robert J.
1997-01-01
Investigates an interesting application of probability in the world of sports. Considers the role of permutations in the lottery system used by the National Basketball Association (NBA) in the United States to determine the order in which nonplayoff teams select players from the college ranks. Presents a lesson on this topic in which students work…
Confusion between Odds and Probability, a Pandemic?
ERIC Educational Resources Information Center
Fulton, Lawrence V.; Mendez, Francis A.; Bastian, Nathaniel D.; Musal, R. Muzaffer
2012-01-01
This manuscript discusses the common confusion between the terms probability and odds. To emphasize the importance and responsibility of being meticulous in the dissemination of information and knowledge, this manuscript reveals five cases of sources of inaccurate statistical language imbedded in the dissemination of information to the general…
Probability distribution functions of the Grincevicjus series
NASA Astrophysics Data System (ADS)
Kapica, Rafal; Morawiec, Janusz
2008-06-01
Given a sequence ([xi]n,[eta]n) of independent identically distributed vectors of random variables we consider the Grincevicjus series and a functional-integral equation connected with it. We prove that the equation characterizes all probability distribution functions of the Grincevicjus series. Moreover, some application of this characterization to a continuous refinement equation is presented.
Time Required to Compute A Posteriori Probabilities,
The paper discusses the time required to compute a posteriori probabilities using Bayes ’ Theorem . In a two-hypothesis example it is shown that, to... Bayes ’ Theorem as the group operation. Winograd’s results concerning the lower bound on the time required to perform a group operation on a finite group using logical circuitry are therefore applicable. (Author)
Interstitial lung disease probably caused by imipramine.
Deshpande, Prasanna R; Ravi, Ranjani; Gouda, Sinddalingana; Stanley, Weena; Hande, Manjunath H
2014-01-01
Drugs are rarely associated with causing interstitial lung disease (ILD). We report a case of a 75-year-old woman who developed ILD after exposure to imipramine. To our knowledge, this is one of the rare cases of ILD probably caused due to imipramine. There is need to report such rare adverse effects related to ILD and drugs for better management of ILD.
The Smart Potential behind Probability Matching
ERIC Educational Resources Information Center
Gaissmaier, Wolfgang; Schooler, Lael J.
2008-01-01
Probability matching is a classic choice anomaly that has been studied extensively. While many approaches assume that it is a cognitive shortcut driven by cognitive limitations, recent literature suggests that it is not a strategy per se, but rather another outcome of people's well-documented misperception of randomness. People search for patterns…
Probability of boundary conditions in quantum cosmology
NASA Astrophysics Data System (ADS)
Suenobu, Hiroshi; Nambu, Yasusada
2017-02-01
One of the main interest in quantum cosmology is to determine boundary conditions for the wave function of the universe which can predict observational data of our universe. For this purpose, we solve the Wheeler-DeWitt equation for a closed universe with a scalar field numerically and evaluate probabilities for boundary conditions of the wave function of the universe. To impose boundary conditions of the wave function, we use exact solutions of the Wheeler-DeWitt equation with a constant scalar field potential. These exact solutions include wave functions with well known boundary condition proposals, the no-boundary proposal and the tunneling proposal. We specify the exact solutions by introducing two real parameters to discriminate boundary conditions, and obtain the probability for these parameters under the requirement of sufficient e-foldings of the inflation. The probability distribution of boundary conditions prefers the tunneling boundary condition to the no-boundary boundary condition. Furthermore, for large values of a model parameter related to the inflaton mass and the cosmological constant, the probability of boundary conditions selects an unique boundary condition different from the tunneling type.
Idempotent probability measures on ultrametric spaces
NASA Astrophysics Data System (ADS)
Hubal, Oleksandra; Zarichnyi, Mykhailo
2008-07-01
Following the construction due to Hartog and Vink we introduce a metric on the set of idempotent probability measures (Maslov measures) defined on an ultrametric space. This construction determines a functor on the category of ultrametric spaces and nonexpanding maps. We prove that this functor is the functorial part of a monad on this category. This monad turns out to contain the hyperspace monad.
Five-Parameter Bivariate Probability Distribution
NASA Technical Reports Server (NTRS)
Tubbs, J.; Brewer, D.; Smith, O. W.
1986-01-01
NASA technical memorandum presents four papers about five-parameter bivariate gamma class of probability distributions. With some overlap of subject matter, papers address different aspects of theories of these distributions and use in forming statistical models of such phenomena as wind gusts. Provides acceptable results for defining constraints in problems designing aircraft and spacecraft to withstand large wind-gust loads.
Independent Events in Elementary Probability Theory
ERIC Educational Resources Information Center
Csenki, Attila
2011-01-01
In Probability and Statistics taught to mathematicians as a first introduction or to a non-mathematical audience, joint independence of events is introduced by requiring that the multiplication rule is satisfied. The following statement is usually tacitly assumed to hold (and, at best, intuitively motivated): If the n events E[subscript 1],…
Assessing Schematic Knowledge of Introductory Probability Theory
ERIC Educational Resources Information Center
Birney, Damian P.; Fogarty, Gerard J.; Plank, Ashley
2005-01-01
The ability to identify schematic knowledge is an important goal for both assessment and instruction. In the current paper, schematic knowledge of statistical probability theory is explored from the declarative-procedural framework using multiple methods of assessment. A sample of 90 undergraduate introductory statistics students was required to…
Automatic Item Generation of Probability Word Problems
ERIC Educational Resources Information Center
Holling, Heinz; Bertling, Jonas P.; Zeuch, Nina
2009-01-01
Mathematical word problems represent a common item format for assessing student competencies. Automatic item generation (AIG) is an effective way of constructing many items with predictable difficulties, based on a set of predefined task parameters. The current study presents a framework for the automatic generation of probability word problems…
Probability from a Socio-Cultural Perspective
ERIC Educational Resources Information Center
Sharma, Sashi
2016-01-01
There exists considerable and rich literature on students' misconceptions about probability; less attention has been paid to the development of students' probabilistic thinking in the classroom. Grounded in an analysis of the literature, this article offers a lesson sequence for developing students' probabilistic understanding. In particular, a…
Posterior Probabilities for a Consensus Ordering.
ERIC Educational Resources Information Center
Fligner, Michael A.; Verducci, Joseph S.
1990-01-01
The concept of consensus ordering is defined, and formulas for exact and approximate posterior probabilities for consensus ordering are developed under the assumption of a generalized Mallows' model with a diffuse conjugate prior. These methods are applied to a data set concerning 98 college students. (SLD)
Phonotactic Probability Effects in Children Who Stutter
ERIC Educational Resources Information Center
Anderson, Julie D.; Byrd, Courtney T.
2008-01-01
Purpose: The purpose of this study was to examine the influence of "phonotactic probability", which is the frequency of different sound segments and segment sequences, on the overall fluency with which words are produced by preschool children who stutter (CWS) as well as to determine whether it has an effect on the type of stuttered disfluency…
Probability distribution functions in turbulent convection
NASA Technical Reports Server (NTRS)
Balachandar, S.; Sirovich, L.
1991-01-01
Results of an extensive investigation of probability distribution functions (pdfs) for Rayleigh-Benard convection, in hard turbulence regime, are presented. It is shown that the pdfs exhibit a high degree of internal universality. In certain cases this universality is established within two Kolmogorov scales of a boundary. A discussion of the factors leading to the universality is presented.
Activities in Elementary Probability, Monograph No. 9.
ERIC Educational Resources Information Center
Fouch, Daniel J.
This monograph on elementary probability for middle school, junior high, or high school consumer mathematics students is divided into two parts. Part one emphasizes lessons which cover the fundamental counting principle, permutations, and combinations. The 5 lessons of part I indicate the objectives, examples, methods, application, and problems…
Probability in Action: The Red Traffic Light
ERIC Educational Resources Information Center
Shanks, John A.
2007-01-01
Emphasis on problem solving in mathematics has gained considerable attention in recent years. While statistics teaching has always been problem driven, the same cannot be said for the teaching of probability where discrete examples involving coins and playing cards are often the norm. This article describes an application of simple probability…
Technique for Evaluating Multiple Probability Occurrences /TEMPO/
NASA Technical Reports Server (NTRS)
Mezzacappa, M. A.
1970-01-01
Technique is described for adjustment of engineering response information by broadening the application of statistical subjective stimuli theory. The study is specifically concerned with a mathematical evaluation of the expected probability of relative occurrence which can be identified by comparison rating techniques.
Monte Carlo methods to calculate impact probabilities
NASA Astrophysics Data System (ADS)
Rickman, H.; Wiśniowski, T.; Wajer, P.; Gabryszewski, R.; Valsecchi, G. B.
2014-09-01
Context. Unraveling the events that took place in the solar system during the period known as the late heavy bombardment requires the interpretation of the cratered surfaces of the Moon and terrestrial planets. This, in turn, requires good estimates of the statistical impact probabilities for different source populations of projectiles, a subject that has received relatively little attention, since the works of Öpik (1951, Proc. R. Irish Acad. Sect. A, 54, 165) and Wetherill (1967, J. Geophys. Res., 72, 2429). Aims: We aim to work around the limitations of the Öpik and Wetherill formulae, which are caused by singularities due to zero denominators under special circumstances. Using modern computers, it is possible to make good estimates of impact probabilities by means of Monte Carlo simulations, and in this work, we explore the available options. Methods: We describe three basic methods to derive the average impact probability for a projectile with a given semi-major axis, eccentricity, and inclination with respect to a target planet on an elliptic orbit. One is a numerical averaging of the Wetherill formula; the next is a Monte Carlo super-sizing method using the target's Hill sphere. The third uses extensive minimum orbit intersection distance (MOID) calculations for a Monte Carlo sampling of potentially impacting orbits, along with calculations of the relevant interval for the timing of the encounter allowing collision. Numerical experiments are carried out for an intercomparison of the methods and to scrutinize their behavior near the singularities (zero relative inclination and equal perihelion distances). Results: We find an excellent agreement between all methods in the general case, while there appear large differences in the immediate vicinity of the singularities. With respect to the MOID method, which is the only one that does not involve simplifying assumptions and approximations, the Wetherill averaging impact probability departs by diverging toward
ERIC Educational Resources Information Center
Storkel, Holly L.; Lee, Su-Yeon
2011-01-01
The goal of this research was to disentangle effects of phonotactic probability, the likelihood of occurrence of a sound sequence, and neighbourhood density, the number of phonologically similar words, in lexical acquisition. Two-word learning experiments were conducted with 4-year-old children. Experiment 1 manipulated phonotactic probability…
Chance and Probability: What Do They Mean to University Engineering Students?
ERIC Educational Resources Information Center
Barragues, J. I.; Guisasola, J.; Morais, A.
2006-01-01
The great interest aroused by the incorporation of Statistics and Probability into curricular projects has been accompanied by considerable evidence of significant difficulties in the meaningful learning and application of the concepts. These difficulties have been the subject of many studies, mostly concerning secondary school students. This…
Uncovering the Best Skill Multimap by Constraining the Error Probabilities of the Gain-Loss Model
ERIC Educational Resources Information Center
Anselmi, Pasquale; Robusto, Egidio; Stefanutti, Luca
2012-01-01
The Gain-Loss model is a probabilistic skill multimap model for assessing learning processes. In practical applications, more than one skill multimap could be plausible, while none corresponds to the true one. The article investigates whether constraining the error probabilities is a way of uncovering the best skill assignment among a number of…
ERIC Educational Resources Information Center
Rasanen, Okko
2011-01-01
Word segmentation from continuous speech is a difficult task that is faced by human infants when they start to learn their native language. Several studies indicate that infants might use several different cues to solve this problem, including intonation, linguistic stress, and transitional probabilities between subsequent speech sounds. In this…
The Probability of Reading Failure in I.T.A. and T.O.
ERIC Educational Resources Information Center
Downing, John
1977-01-01
Discusses the hazards that the English language contains for children learning to read, reports the Bullock Report's recommendation to judge I.T.A. on its merits, and describes research findings suggesting that the probability of reading failure is considerably greater when T.O. is used than when I.T.A. is used. (GT)
ERIC Educational Resources Information Center
Calvert, Carol Elaine
2014-01-01
This case study relates to distance learning students on open access courses. It demonstrates the use of predictive analytics to generate a model of the probabilities of success and retention at different points, or milestones, in a student journey. A core set of explanatory variables has been established and their varying relative importance at…
Probability Sampling and Inferential Statistics: An Interactive Exercise using M&M's.
ERIC Educational Resources Information Center
Auster, Carol J.
2000-01-01
Describes a class exercise that attempts to alleviate student anxiety and fear of learning statistics. Explains that the exercise focuses on concepts associated with probability sampling theory and sampling distribution through the use of M and M's candy. Describes the four steps involved and additional considerations. (CMK)
Using High-Probability Foods to Increase the Acceptance of Low-Probability Foods
ERIC Educational Resources Information Center
Meier, Aimee E.; Fryling, Mitch J.; Wallace, Michele D.
2012-01-01
Studies have evaluated a range of interventions to treat food selectivity in children with autism and related developmental disabilities. The high-probability instructional sequence is one intervention with variable results in this area. We evaluated the effectiveness of a high-probability sequence using 3 presentations of a preferred food on…
ERIC Educational Resources Information Center
Karelitz, Tzur M.; Budescu, David V.
2004-01-01
When forecasters and decision makers describe uncertain events using verbal probability terms, there is a risk of miscommunication because people use different probability phrases and interpret them in different ways. In an effort to facilitate the communication process, the authors investigated various ways of converting the forecasters' verbal…
ERIC Educational Resources Information Center
Lecoutre, Bruno; Lecoutre, Marie-Paule; Poitevineau, Jacques
2010-01-01
P. R. Killeen's (2005a) probability of replication ("p[subscript rep]") of an experimental result is the fiducial Bayesian predictive probability of finding a same-sign effect in a replication of an experiment. "p[subscript rep]" is now routinely reported in "Psychological Science" and has also begun to appear in…
ERIC Educational Resources Information Center
Satake, Eiki; Amato, Philip P.
2008-01-01
This paper presents an alternative version of formulas of conditional probabilities and Bayes' rule that demonstrate how the truth table of elementary mathematical logic applies to the derivations of the conditional probabilities of various complex, compound statements. This new approach is used to calculate the prior and posterior probabilities…
VOLCANIC RISK ASSESSMENT - PROBABILITY AND CONSEQUENCES
G.A. Valentine; F.V. Perry; S. Dartevelle
2005-08-26
Risk is the product of the probability and consequences of an event. Both of these must be based upon sound science that integrates field data, experiments, and modeling, but must also be useful to decision makers who likely do not understand all aspects of the underlying science. We review a decision framework used in many fields such as performance assessment for hazardous and/or radioactive waste disposal sites that can serve to guide the volcanological community towards integrated risk assessment. In this framework the underlying scientific understanding of processes that affect probability and consequences drive the decision-level results, but in turn these results can drive focused research in areas that cause the greatest level of uncertainty at the decision level. We review two examples of the determination of volcanic event probability: (1) probability of a new volcano forming at the proposed Yucca Mountain radioactive waste repository, and (2) probability that a subsurface repository in Japan would be affected by the nearby formation of a new stratovolcano. We also provide examples of work on consequences of explosive eruptions, within the framework mentioned above. These include field-based studies aimed at providing data for ''closure'' of wall rock erosion terms in a conduit flow model, predictions of dynamic pressure and other variables related to damage by pyroclastic flow into underground structures, and vulnerability criteria for structures subjected to conditions of explosive eruption. Process models (e.g., multiphase flow) are important for testing the validity or relative importance of possible scenarios in a volcanic risk assessment. We show how time-dependent multiphase modeling of explosive ''eruption'' of basaltic magma into an open tunnel (drift) at the Yucca Mountain repository provides insight into proposed scenarios that include the development of secondary pathways to the Earth's surface. Addressing volcanic risk within a decision
Grammaticality, Acceptability, and Probability: A Probabilistic View of Linguistic Knowledge.
Lau, Jey Han; Clark, Alexander; Lappin, Shalom
2016-10-12
The question of whether humans represent grammatical knowledge as a binary condition on membership in a set of well-formed sentences, or as a probabilistic property has been the subject of debate among linguists, psychologists, and cognitive scientists for many decades. Acceptability judgments present a serious problem for both classical binary and probabilistic theories of grammaticality. These judgements are gradient in nature, and so cannot be directly accommodated in a binary formal grammar. However, it is also not possible to simply reduce acceptability to probability. The acceptability of a sentence is not the same as the likelihood of its occurrence, which is, in part, determined by factors like sentence length and lexical frequency. In this paper, we present the results of a set of large-scale experiments using crowd-sourced acceptability judgments that demonstrate gradience to be a pervasive feature in acceptability judgments. We then show how one can predict acceptability judgments on the basis of probability by augmenting probabilistic language models with an acceptability measure. This is a function that normalizes probability values to eliminate the confounding factors of length and lexical frequency. We describe a sequence of modeling experiments with unsupervised language models drawn from state-of-the-art machine learning methods in natural language processing. Several of these models achieve very encouraging levels of accuracy in the acceptability prediction task, as measured by the correlation between the acceptability measure scores and mean human acceptability values. We consider the relevance of these results to the debate on the nature of grammatical competence, and we argue that they support the view that linguistic knowledge can be intrinsically probabilistic.
Cheating Probabilities on Multiple Choice Tests
NASA Astrophysics Data System (ADS)
Rizzuto, Gaspard T.; Walters, Fred
1997-10-01
This paper is strictly based on mathematical statistics and as such does not depend on prior performance and assumes the probability of each choice to be identical. In a real life situation, the probability of two students having identical responses becomes larger the better the students are. However the mathematical model is developed for all responses, both correct and incorrect, and provides a baseline for evaluation. David Harpp and coworkers (2, 3) at McGill University have evaluated ratios of exact errors in common (EEIC) to errors in common (EIC) and differences (D). In pairings where the ratio EEIC/EIC was greater than 0.75, the pair had unusually high odds against their answer pattern being random. Detection of copying of the EEIC/D ratios at values >1.0 indicate that pairs of these students were seated adjacent to one another and copied from one another. The original papers should be examined for details.
Approaches to Evaluating Probability of Collision Uncertainty
NASA Technical Reports Server (NTRS)
Hejduk, Matthew D.; Johnson, Lauren C.
2016-01-01
While the two-dimensional probability of collision (Pc) calculation has served as the main input to conjunction analysis risk assessment for over a decade, it has done this mostly as a point estimate, with relatively little effort made to produce confidence intervals on the Pc value based on the uncertainties in the inputs. The present effort seeks to try to carry these uncertainties through the calculation in order to generate a probability density of Pc results rather than a single average value. Methods for assessing uncertainty in the primary and secondary objects' physical sizes and state estimate covariances, as well as a resampling approach to reveal the natural variability in the calculation, are presented; and an initial proposal for operationally-useful display and interpretation of these data for a particular conjunction is given.
A probability distribution model for rain rate
NASA Technical Reports Server (NTRS)
Kedem, Benjamin; Pavlopoulos, Harry; Guan, Xiaodong; Short, David A.
1994-01-01
A systematic approach is suggested for modeling the probability distribution of rain rate. Rain rate, conditional on rain and averaged over a region, is modeled as a temporally homogeneous diffusion process with appropiate boundary conditions. The approach requires a drift coefficient-conditional average instantaneous rate of change of rain intensity-as well as a diffusion coefficient-the conditional average magnitude of the rate of growth and decay of rain rate about its drift. Under certain assumptions on the drift and diffusion coefficients compatible with rain rate, a new parametric family-containing the lognormal distribution-is obtained for the continuous part of the stationary limit probability distribution. The family is fitted to tropical rainfall from Darwin and Florida, and it is found that the lognormal distribution provides adequate fits as compared with other members of the family and also with the gamma distribution.
Earthquake probabilities: theoretical assessments and reality
NASA Astrophysics Data System (ADS)
Kossobokov, V. G.
2013-12-01
It is of common knowledge that earthquakes are complex phenomena which classification and sizing remain serious problems of the contemporary seismology. In general, their frequency-magnitude distribution exhibit power law scaling. This scaling differs significantly when different time and/or space domains are considered. At the scale of a particular earthquake rupture zone the frequency of similar size events is usually estimated to be about once in several hundred years. Evidently, contemporary seismology does not possess enough reported instrumental data for any reliable quantification of an earthquake probability at a given place of expected event. Regretfully, most of the state-of-the-art theoretical approaches to assess probability of seismic events are based on trivial (e.g. Poisson, periodic, etc) or, conversely, delicately-designed (e.g. STEP, ETAS, etc) models of earthquake sequences. Some of these models are evidently erroneous, some can be rejected by the existing statistics, and some are hardly testable in our life-time. Nevertheless such probabilistic counts including seismic hazard assessment and earthquake forecasting when used on practice eventually mislead to scientifically groundless advices communicated to decision makers and inappropriate decisions. As a result, the population of seismic regions continues facing unexpected risk and losses. The international project Global Earthquake Model (GEM) is on the wrong track, if it continues to base seismic risk estimates on the standard, mainly probabilistic, methodology to assess seismic hazard. It is generally accepted that earthquakes are infrequent, low-probability events. However, they keep occurring at earthquake-prone areas with 100% certainty. Given the expectation of seismic event once per hundred years, the daily probability of occurrence on a certain date may range from 0 to 100% depending on a choice of probability space (which is yet unknown and, therefore, made by a subjective lucky chance
Complex analysis methods in noncommutative probability
NASA Astrophysics Data System (ADS)
Teodor Belinschi, Serban
2006-02-01
In this thesis we study convolutions that arise from noncommutative probability theory. We prove several regularity results for free convolutions, and for measures in partially defined one-parameter free convolution semigroups. We discuss connections between Boolean and free convolutions and, in the last chapter, we prove that any infinitely divisible probability measure with respect to monotonic additive or multiplicative convolution belongs to a one-parameter semigroup with respect to the corresponding convolution. Earlier versions of some of the results in this thesis have already been published, while some others have been submitted for publication. We have preserved almost entirely the specific format for PhD theses required by Indiana University. This adds several unnecessary pages to the document, but we wanted to preserve the specificity of the document as a PhD thesis at Indiana University.
A quantum probability perspective on borderline vagueness.
Blutner, Reinhard; Pothos, Emmanuel M; Bruza, Peter
2013-10-01
The term "vagueness" describes a property of natural concepts, which normally have fuzzy boundaries, admit borderline cases, and are susceptible to Zeno's sorites paradox. We will discuss the psychology of vagueness, especially experiments investigating the judgment of borderline cases and contradictions. In the theoretical part, we will propose a probabilistic model that describes the quantitative characteristics of the experimental finding and extends Alxatib's and Pelletier's () theoretical analysis. The model is based on a Hopfield network for predicting truth values. Powerful as this classical perspective is, we show that it falls short of providing an adequate coverage of the relevant empirical results. In the final part, we will argue that a substantial modification of the analysis put forward by Alxatib and Pelletier and its probabilistic pendant is needed. The proposed modification replaces the standard notion of probabilities by quantum probabilities. The crucial phenomenon of borderline contradictions can be explained then as a quantum interference phenomenon.
Approximate probability distributions of the master equation.
Thomas, Philipp; Grima, Ramon
2015-07-01
Master equations are common descriptions of mesoscopic systems. Analytical solutions to these equations can rarely be obtained. We here derive an analytical approximation of the time-dependent probability distribution of the master equation using orthogonal polynomials. The solution is given in two alternative formulations: a series with continuous and a series with discrete support, both of which can be systematically truncated. While both approximations satisfy the system size expansion of the master equation, the continuous distribution approximations become increasingly negative and tend to oscillations with increasing truncation order. In contrast, the discrete approximations rapidly converge to the underlying non-Gaussian distributions. The theory is shown to lead to particularly simple analytical expressions for the probability distributions of molecule numbers in metabolic reactions and gene expression systems.
Transit probabilities for debris around white dwarfs
NASA Astrophysics Data System (ADS)
Lewis, John Arban; Johnson, John A.
2017-01-01
The discovery of WD 1145+017 (Vanderburg et al. 2015), a metal-polluted white dwarf with an infrared-excess and transits confirmed the long held theory that at least some metal-polluted white dwarfs are actively accreting material from crushed up planetesimals. A statistical understanding of WD 1145-like systems would inform us on the various pathways for metal-pollution and the end states of planetary systems around medium- to high-mass stars. However, we only have one example and there are presently no published studies of transit detection/discovery probabilities for white dwarfs within this interesting regime. We present a preliminary look at the transit probabilities for metal-polluted white dwarfs and their projected space density in the Solar Neighborhood, which will inform future searches for analogs to WD 1145+017.
Volcano shapes, entropies, and eruption probabilities
NASA Astrophysics Data System (ADS)
Gudmundsson, Agust; Mohajeri, Nahid
2014-05-01
We propose that the shapes of polygenetic volcanic edifices reflect the shapes of the associated probability distributions of eruptions. In this view, the peak of a given volcanic edifice coincides roughly with the peak of the probability (or frequency) distribution of its eruptions. The broadness and slopes of the edifices vary widely, however. The shapes of volcanic edifices can be approximated by various distributions, either discrete (binning or histogram approximation) or continuous. For a volcano shape (profile) approximated by a normal curve, for example, the broadness would be reflected in its standard deviation (spread). Entropy (S) of a discrete probability distribution is a measure of the absolute uncertainty as to the next outcome/message: in this case, the uncertainty as to time and place of the next eruption. A uniform discrete distribution (all bins of equal height), representing a flat volcanic field or zone, has the largest entropy or uncertainty. For continuous distributions, we use differential entropy, which is a measure of relative uncertainty, or uncertainty change, rather than absolute uncertainty. Volcano shapes can be approximated by various distributions, from which the entropies and thus the uncertainties as regards future eruptions can be calculated. We use the Gibbs-Shannon formula for the discrete entropies and the analogues general formula for the differential entropies and compare their usefulness for assessing the probabilities of eruptions in volcanoes. We relate the entropies to the work done by the volcano during an eruption using the Helmholtz free energy. Many factors other than the frequency of eruptions determine the shape of a volcano. These include erosion, landslides, and the properties of the erupted materials (including their angle of repose). The exact functional relation between the volcano shape and the eruption probability distribution must be explored for individual volcanoes but, once established, can be used to
Probability of identity by descent in metapopulations.
Kaj, I; Lascoux, M
1999-01-01
Equilibrium probabilities of identity by descent (IBD), for pairs of genes within individuals, for genes between individuals within subpopulations, and for genes between subpopulations are calculated in metapopulation models with fixed or varying colony sizes. A continuous-time analog to the Moran model was used in either case. For fixed-colony size both propagule and migrant pool models were considered. The varying population size model is based on a birth-death-immigration (BDI) process, to which migration between colonies is added. Wright's F statistics are calculated and compared to previous results. Adding between-island migration to the BDI model can have an important effect on the equilibrium probabilities of IBD and on Wright's index. PMID:10388835
Conflict Probability Estimation for Free Flight
NASA Technical Reports Server (NTRS)
Paielli, Russell A.; Erzberger, Heinz
1996-01-01
The safety and efficiency of free flight will benefit from automated conflict prediction and resolution advisories. Conflict prediction is based on trajectory prediction and is less certain the farther in advance the prediction, however. An estimate is therefore needed of the probability that a conflict will occur, given a pair of predicted trajectories and their levels of uncertainty. A method is developed in this paper to estimate that conflict probability. The trajectory prediction errors are modeled as normally distributed, and the two error covariances for an aircraft pair are combined into a single equivalent covariance of the relative position. A coordinate transformation is then used to derive an analytical solution. Numerical examples and Monte Carlo validation are presented.
Approximate probability distributions of the master equation
NASA Astrophysics Data System (ADS)
Thomas, Philipp; Grima, Ramon
2015-07-01
Master equations are common descriptions of mesoscopic systems. Analytical solutions to these equations can rarely be obtained. We here derive an analytical approximation of the time-dependent probability distribution of the master equation using orthogonal polynomials. The solution is given in two alternative formulations: a series with continuous and a series with discrete support, both of which can be systematically truncated. While both approximations satisfy the system size expansion of the master equation, the continuous distribution approximations become increasingly negative and tend to oscillations with increasing truncation order. In contrast, the discrete approximations rapidly converge to the underlying non-Gaussian distributions. The theory is shown to lead to particularly simple analytical expressions for the probability distributions of molecule numbers in metabolic reactions and gene expression systems.
Computing association probabilities using parallel Boltzmann machines.
Iltis, R A; Ting, P Y
1993-01-01
A new computational method is presented for solving the data association problem using parallel Boltzmann machines. It is shown that the association probabilities can be computed with arbitrarily small errors if a sufficient number of parallel Boltzmann machines are available. The probability beta(i)(j) that the i th measurement emanated from the jth target can be obtained simply by observing the relative frequency with which neuron v(i,j) in a two-dimensional network is on throughout the layers. Some simple tracking examples comparing the performance of the Boltzmann algorithm to the exact data association solution and with the performance of an alternative parallel method using the Hopfield neural network are also presented.
Nuclear data uncertainties: I, Basic concepts of probability
Smith, D.L.
1988-12-01
Some basic concepts of probability theory are presented from a nuclear-data perspective, in order to provide a foundation for thorough understanding of the role of uncertainties in nuclear data research. Topics included in this report are: events, event spaces, calculus of events, randomness, random variables, random-variable distributions, intuitive and axiomatic probability, calculus of probability, conditional probability and independence, probability distributions, binomial and multinomial probability, Poisson and interval probability, normal probability, the relationships existing between these probability laws, and Bayes' theorem. This treatment emphasizes the practical application of basic mathematical concepts to nuclear data research, and it includes numerous simple examples. 34 refs.
The Origin of Probability and Entropy
NASA Astrophysics Data System (ADS)
Knuth, Kevin H.
2008-11-01
Measuring is the quantification of ordering. Thus the process of ordering elements of a set is a more fundamental activity than measuring. Order theory, also known as lattice theory, provides a firm foundation on which to build measure theory. The result is a set of new insights that cast probability theory and information theory in a new light, while simultaneously opening the door to a better understanding of measures as a whole.
Calculating Cumulative Binomial-Distribution Probabilities
NASA Technical Reports Server (NTRS)
Scheuer, Ernest M.; Bowerman, Paul N.
1989-01-01
Cumulative-binomial computer program, CUMBIN, one of set of three programs, calculates cumulative binomial probability distributions for arbitrary inputs. CUMBIN, NEWTONP (NPO-17556), and CROSSER (NPO-17557), used independently of one another. Reliabilities and availabilities of k-out-of-n systems analyzed. Used by statisticians and users of statistical procedures, test planners, designers, and numerical analysts. Used for calculations of reliability and availability. Program written in C.
Sampling probability distributions of lesions in mammograms
NASA Astrophysics Data System (ADS)
Looney, P.; Warren, L. M.; Dance, D. R.; Young, K. C.
2015-03-01
One approach to image perception studies in mammography using virtual clinical trials involves the insertion of simulated lesions into normal mammograms. To facilitate this, a method has been developed that allows for sampling of lesion positions across the cranio-caudal and medio-lateral radiographic projections in accordance with measured distributions of real lesion locations. 6825 mammograms from our mammography image database were segmented to find the breast outline. The outlines were averaged and smoothed to produce an average outline for each laterality and radiographic projection. Lesions in 3304 mammograms with malignant findings were mapped on to a standardised breast image corresponding to the average breast outline using piecewise affine transforms. A four dimensional probability distribution function was found from the lesion locations in the cranio-caudal and medio-lateral radiographic projections for calcification and noncalcification lesions. Lesion locations sampled from this probability distribution function were mapped on to individual mammograms using a piecewise affine transform which transforms the average outline to the outline of the breast in the mammogram. The four dimensional probability distribution function was validated by comparing it to the two dimensional distributions found by considering each radiographic projection and laterality independently. The correlation of the location of the lesions sampled from the four dimensional probability distribution function across radiographic projections was shown to match the correlation of the locations of the original mapped lesion locations. The current system has been implemented as a web-service on a server using the Python Django framework. The server performs the sampling, performs the mapping and returns the results in a javascript object notation format.
SureTrak Probability of Impact Display
NASA Technical Reports Server (NTRS)
Elliott, John
2012-01-01
The SureTrak Probability of Impact Display software was developed for use during rocket launch operations. The software displays probability of impact information for each ship near the hazardous area during the time immediately preceding the launch of an unguided vehicle. Wallops range safety officers need to be sure that the risk to humans is below a certain threshold during each use of the Wallops Flight Facility Launch Range. Under the variable conditions that can exist at launch time, the decision to launch must be made in a timely manner to ensure a successful mission while not exceeding those risk criteria. Range safety officers need a tool that can give them the needed probability of impact information quickly, and in a format that is clearly understandable. This application is meant to fill that need. The software is a reuse of part of software developed for an earlier project: Ship Surveillance Software System (S4). The S4 project was written in C++ using Microsoft Visual Studio 6. The data structures and dialog templates from it were copied into a new application that calls the implementation of the algorithms from S4 and displays the results as needed. In the S4 software, the list of ships in the area was received from one local radar interface and from operators who entered the ship information manually. The SureTrak Probability of Impact Display application receives ship data from two local radars as well as the SureTrak system, eliminating the need for manual data entry.
Non-signalling Theories and Generalized Probability
NASA Astrophysics Data System (ADS)
Tylec, Tomasz I.; Kuś, Marek; Krajczok, Jacek
2016-09-01
We provide mathematically rigorous justification of using term probability in connection to the so called non-signalling theories, known also as Popescu's and Rohrlich's box worlds. No only do we prove correctness of these models (in the sense that they describe composite system of two independent subsystems) but we obtain new properties of non-signalling boxes and expose new tools for further investigation. Moreover, it allows strightforward generalization to more complicated systems.
Probability and Statistics in Aerospace Engineering
NASA Technical Reports Server (NTRS)
Rheinfurth, M. H.; Howell, L. W.
1998-01-01
This monograph was prepared to give the practicing engineer a clear understanding of probability and statistics with special consideration to problems frequently encountered in aerospace engineering. It is conceived to be both a desktop reference and a refresher for aerospace engineers in government and industry. It could also be used as a supplement to standard texts for in-house training courses on the subject.
Theoretical Analysis of Rain Attenuation Probability
NASA Astrophysics Data System (ADS)
Roy, Surendra Kr.; Jha, Santosh Kr.; Jha, Lallan
2007-07-01
Satellite communication technologies are now highly developed and high quality, distance-independent services have expanded over a very wide area. As for the system design of the Hokkaido integrated telecommunications(HIT) network, it must first overcome outages of satellite links due to rain attenuation in ka frequency bands. In this paper theoretical analysis of rain attenuation probability on a slant path has been made. The formula proposed is based Weibull distribution and incorporates recent ITU-R recommendations concerning the necessary rain rates and rain heights inputs. The error behaviour of the model was tested with the loading rain attenuation prediction model recommended by ITU-R for large number of experiments at different probability levels. The novel slant path rain attenuastion prediction model compared to the ITU-R one exhibits a similar behaviour at low time percentages and a better root-mean-square error performance for probability levels above 0.02%. The set of presented models exhibits the advantage of implementation with little complexity and is considered useful for educational and back of the envelope computations.
The Probability Distribution of Daily Streamflow
NASA Astrophysics Data System (ADS)
Blum, A.; Vogel, R. M.
2015-12-01
Flow duration curves (FDCs) are a graphical illustration of the cumulative distribution of streamflow. Daily streamflows often range over many orders of magnitude, making it extremely challenging to find a probability distribution function (pdf) which can mimic the steady state or period of record FDC (POR-FDC). Median annual FDCs (MA-FDCs) describe the pdf of daily streamflow in a typical year. For POR- and MA-FDCs, Lmoment diagrams, visual assessments of FDCs and Quantile-Quantile probability plot correlation coefficients are used to evaluate goodness of fit (GOF) of candidate probability distributions. FDCs reveal that both four-parameter kappa (KAP) and three-parameter generalized Pareto (GP3) models result in very high GOF for the MA-FDC and a relatively lower GOF for POR-FDCs at over 500 rivers across the coterminous U.S. Physical basin characteristics, such as baseflow index as well as hydroclimatic indices such as the aridity index and the runoff ratio are found to be correlated with one of the shape parameters (kappa) of the KAP and GP3 pdfs. Our work also reveals several important areas for future research including improved parameter estimators for the KAP pdf, as well as increasing our understanding of the conditions which give rise to improved GOF of analytical pdfs to large samples of daily streamflows.
Probability of metastable states in Yukawa clusters
NASA Astrophysics Data System (ADS)
Ludwig, Patrick; Kaehlert, Hanno; Baumgartner, Henning; Bonitz, Michael
2008-11-01
Finite strongly coupled systems of charged particles in external traps are of high interest in many fields. Here we analyze the occurrence probabilities of ground- and metastable states of spherical, three-dimensional Yukawa clusters by means of molecular dynamics and Monte Carlo simulations and an analytical method. We find that metastable states can occur with a higher probability than the ground state, thus confirming recent dusty plasma experiments with so-called Yukawa balls [1]. The analytical method [2], based on the harmonic approximation of the potential energy, allows for a very intuitive explanation of the probabilities when combined with the simulation results [3].[1] D. Block, S. Käding, A. Melzer, A. Piel, H. Baumgartner, and M. Bonitz, Physics of Plasmas 15, 040701 (2008)[2] F. Baletto and R. Ferrando, Reviews of Modern Physics 77, 371 (2005)[3] H. Kählert, P. Ludwig, H. Baumgartner, M. Bonitz, D. Block, S. Käding, A. Melzer, and A. Piel, submitted for publication (2008)
Atomic Transition Probabilities for Rare Earths
NASA Astrophysics Data System (ADS)
Curry, J. J.; Anderson, Heidi M.; den Hartog, E. A.; Wickliffe, M. E.; Lawler, J. E.
1996-10-01
Accurate absolute atomic transition probabilities for selected neutral and singly ionized rare earth elements including Tm, Dy, and Ho are being measured. The increasing use of rare earths in high intensity discharge lamps provides motivation; the data are needed for diagnosing and modeling the lamps. Radiative lifetimes, measured using time resolved laser induced fluorescence (LIF), are combined with branching fractions, measured using a large Fourier transform spectrometer (FTS), to determine accurate absolute atomic transition probabilities. More than 15,000 LIF decay curves from Tm and Dy atoms and ions in slow beams have been recorded and analyzed. Radiative lifetimes for 298 levels of TmI and TmII and for 450 levels of DyI and DyII are determined. Branching fractions are extracted from spectra recorded using the 1.0 m FTS at the National Solar Observatory. Branching fractions and absolute transition probabilities for 500 of the strongest TmI and TmII lines are complete. Representative lifetime and branching fraction data will be presented and discussed. Supported by Osram Sylvania Inc. and the NSF.
Bacteria survival probability in bactericidal filter paper.
Mansur-Azzam, Nura; Hosseinidoust, Zeinab; Woo, Su Gyeong; Vyhnalkova, Renata; Eisenberg, Adi; van de Ven, Theo G M
2014-05-01
Bactericidal filter papers offer the simplicity of gravity filtration to simultaneously eradicate microbial contaminants and particulates. We previously detailed the development of biocidal block copolymer micelles that could be immobilized on a filter paper to actively eradicate bacteria. Despite the many advantages offered by this system, its widespread use is hindered by its unknown mechanism of action which can result in non-reproducible outcomes. In this work, we sought to investigate the mechanism by which a certain percentage of Escherichia coli cells survived when passing through the bactericidal filter paper. Through the process of elimination, the possibility that the bacterial survival probability was controlled by the initial bacterial load or the existence of resistant sub-populations of E. coli was dismissed. It was observed that increasing the thickness or the number of layers of the filter significantly decreased bacterial survival probability for the biocidal filter paper but did not affect the efficiency of the blank filter paper (no biocide). The survival probability of bacteria passing through the antibacterial filter paper appeared to depend strongly on the number of collision between each bacterium and the biocide-loaded micelles. It was thus hypothesized that during each collision a certain number of biocide molecules were directly transferred from the hydrophobic core of the micelle to the bacterial lipid bilayer membrane. Therefore, each bacterium must encounter a certain number of collisions to take up enough biocide to kill the cell and cells that do not undergo the threshold number of collisions are expected to survive.
Probability Explorations in a Multicultural Context
ERIC Educational Resources Information Center
Naresh, Nirmala; Harper, Suzanne R.; Keiser, Jane M.; Krumpe, Norm
2014-01-01
Mathematical ideas exist and develop in many different cultures. From this multicultural perspective, teachers can use a variety of approaches to acknowledge the role of culture in the teaching and learning of mathematics. Curricular materials that "emphasize both the mathematical and sociocultural aspects" not only help teachers achieve…
Finding Possibility and Probability Lessons in Sports
ERIC Educational Resources Information Center
Busadee, Nutjira; Laosinchai, Parames; Panijpan, Bhinyo
2011-01-01
Today's students demand that their lessons be real, interesting, relevant, and manageable. Mathematics is one subject that eludes many students partly because its traditional presentation lacks those elements that encourage students to learn. Easy accessibility through electronic media has exposed people all over the world to a variety of sports…
Probability with Collaborative Data Visualization Software
ERIC Educational Resources Information Center
Willis, Melinda B. N.; Hay, Sue; Martin, Fred G.; Scribner-MacLean, Michelle; Rudnicki, Ivan
2015-01-01
Mathematics teachers continually look for ways to make the learning of mathematics more active and engaging. Hands-on activities, in particular, have been demonstrated to improve student engagement and understanding in mathematics classes. Likewise, many scholars have emphasized the growing importance of giving students experience with the…
The probability and severity of decompression sickness
Hada, Ethan A.; Vann, Richard D.; Denoble, Petar J.
2017-01-01
Decompression sickness (DCS), which is caused by inert gas bubbles in tissues, is an injury of concern for scuba divers, compressed air workers, astronauts, and aviators. Case reports for 3322 air and N2-O2 dives, resulting in 190 DCS events, were retrospectively analyzed and the outcomes were scored as (1) serious neurological, (2) cardiopulmonary, (3) mild neurological, (4) pain, (5) lymphatic or skin, and (6) constitutional or nonspecific manifestations. Following standard U.S. Navy medical definitions, the data were grouped into mild—Type I (manifestations 4–6)–and serious–Type II (manifestations 1–3). Additionally, we considered an alternative grouping of mild–Type A (manifestations 3–6)–and serious–Type B (manifestations 1 and 2). The current U.S. Navy guidance allows for a 2% probability of mild DCS and a 0.1% probability of serious DCS. We developed a hierarchical trinomial (3-state) probabilistic DCS model that simultaneously predicts the probability of mild and serious DCS given a dive exposure. Both the Type I/II and Type A/B discriminations of mild and serious DCS resulted in a highly significant (p << 0.01) improvement in trinomial model fit over the binomial (2-state) model. With the Type I/II definition, we found that the predicted probability of ‘mild’ DCS resulted in a longer allowable bottom time for the same 2% limit. However, for the 0.1% serious DCS limit, we found a vastly decreased allowable bottom dive time for all dive depths. If the Type A/B scoring was assigned to outcome severity, the no decompression limits (NDL) for air dives were still controlled by the acceptable serious DCS risk limit rather than the acceptable mild DCS risk limit. However, in this case, longer NDL limits were allowed than with the Type I/II scoring. The trinomial model mild and serious probabilities agree reasonably well with the current air NDL only with the Type A/B scoring and when 0.2% risk of serious DCS is allowed. PMID:28296928
The probability and severity of decompression sickness.
Howle, Laurens E; Weber, Paul W; Hada, Ethan A; Vann, Richard D; Denoble, Petar J
2017-01-01
Decompression sickness (DCS), which is caused by inert gas bubbles in tissues, is an injury of concern for scuba divers, compressed air workers, astronauts, and aviators. Case reports for 3322 air and N2-O2 dives, resulting in 190 DCS events, were retrospectively analyzed and the outcomes were scored as (1) serious neurological, (2) cardiopulmonary, (3) mild neurological, (4) pain, (5) lymphatic or skin, and (6) constitutional or nonspecific manifestations. Following standard U.S. Navy medical definitions, the data were grouped into mild-Type I (manifestations 4-6)-and serious-Type II (manifestations 1-3). Additionally, we considered an alternative grouping of mild-Type A (manifestations 3-6)-and serious-Type B (manifestations 1 and 2). The current U.S. Navy guidance allows for a 2% probability of mild DCS and a 0.1% probability of serious DCS. We developed a hierarchical trinomial (3-state) probabilistic DCS model that simultaneously predicts the probability of mild and serious DCS given a dive exposure. Both the Type I/II and Type A/B discriminations of mild and serious DCS resulted in a highly significant (p < 0.01) improvement in trinomial model fit over the binomial (2-state) model. With the Type I/II definition, we found that the predicted probability of 'mild' DCS resulted in a longer allowable bottom time for the same 2% limit. However, for the 0.1% serious DCS limit, we found a vastly decreased allowable bottom dive time for all dive depths. If the Type A/B scoring was assigned to outcome severity, the no decompression limits (NDL) for air dives were still controlled by the acceptable serious DCS risk limit rather than the acceptable mild DCS risk limit. However, in this case, longer NDL limits were allowed than with the Type I/II scoring. The trinomial model mild and serious probabilities agree reasonably well with the current air NDL only with the Type A/B scoring and when 0.2% risk of serious DCS is allowed.
CPROB: A COMPUTATIONAL TOOL FOR CONDUCTING CONDITIONAL PROBABILITY ANALYSIS
Conditional probability analysis measures the probability of observing one event given that another event has occurred. In an environmental context, conditional probability analysis helps assess the association between an environmental contaminant (i.e. the stressor) and the ec...
Probability sampling in legal cases: Kansas cellphone users
NASA Astrophysics Data System (ADS)
Kadane, Joseph B.
2012-10-01
Probability sampling is a standard statistical technique. This article introduces the basic ideas of probability sampling, and shows in detail how probability sampling was used in a particular legal case.
Locurto, Charles; Dillon, Laura; Collins, Meaghan; Conway, Maura; Cunningham, Kate
2013-07-01
Three experiments examined the implicit learning of sequences under conditions in which the elements comprising a sequence were equated in terms of reinforcement probability. In Experiment 1 cotton-top tamarins (Saguinus oedipus) experienced a five-element sequence displayed serially on a touch screen in which reinforcement probability was equated across elements at .16 per element. Tamarins demonstrated learning of this sequence with higher latencies during a random test as compared to baseline sequence training. In Experiments 2 and 3, manipulations of the procedure used in the first experiment were undertaken to rule out a confound owing to the fact that the elements in Experiment 1 bore different temporal relations to the intertrial interval (ITI), an inhibitory period. The results of Experiments 2 and 3 indicated that the implicit learning observed in Experiment 1 was not due to temporal proximity between some elements and the inhibitory ITI. The results taken together support two conclusion: First that tamarins engaged in sequence learning whether or not there was contingent reinforcement for learning the sequence, and second that this learning was not due to subtle differences in associative strength between the elements of the sequence.
Schulze, Christin; Newell, Ben R
2016-07-01
Cognitive load has previously been found to have a positive effect on strategy selection in repeated risky choice. Specifically, whereas inferior probability matching often prevails under single-task conditions, optimal probability maximizing sometimes dominates when a concurrent task competes for cognitive resources. We examined the extent to which this seemingly beneficial effect of increased task demands hinges on the effort required to implement each of the choice strategies. Probability maximizing typically involves a simple repeated response to a single option, whereas probability matching requires choice proportions to be tracked carefully throughout a sequential choice task. Here, we flipped this pattern by introducing a manipulation that made the implementation of maximizing more taxing and, at the same time, allowed decision makers to probability match via a simple repeated response to a single option. The results from two experiments showed that increasing the implementation effort of probability maximizing resulted in decreased adoption rates of this strategy. This was the case both when decision makers simultaneously learned about the outcome probabilities and responded to a dual task (Exp. 1) and when these two aspects were procedurally separated in two distinct stages (Exp. 2). We conclude that the effort involved in implementing a choice strategy is a key factor in shaping repeated choice under uncertainty. Moreover, highlighting the importance of implementation effort casts new light on the sometimes surprising and inconsistent effects of cognitive load that have previously been reported in the literature.
Prediction error as a linear function of reward probability is coded in human nucleus accumbens.
Abler, Birgit; Walter, Henrik; Erk, Susanne; Kammerer, Hannes; Spitzer, Manfred
2006-06-01
Reward probability has been shown to be coded by dopamine neurons in monkeys. Phasic neuronal activation not only increased linearly with reward probability upon expectation of reward, but also varied monotonically across the range of probabilities upon omission or receipt of rewards, therefore modeling discrepancies between expected and received rewards. Such a discrete coding of prediction error has been suggested to be one of the basic principles of learning. We used functional magnetic resonance imaging (fMRI) to show that the human dopamine system codes reward probability and prediction error in a similar way. We used a simple delayed incentive task with a discrete range of reward probabilities from 0%-100%. Activity in the nucleus accumbens of human subjects strongly resembled the phasic responses found in monkey neurons. First, during the expectation period of the task, the fMRI signal in the human nucleus accumbens (NAc) increased linearly with the probability of the reward. Second, during the outcome phase, activity in the NAc coded the prediction error as a linear function of reward probabilities. Third, we found that the Nac signal was correlated with individual differences in sensation seeking and novelty seeking, indicating a link between individual fMRI activation of the dopamine system in a probabilistic paradigm and personality traits previously suggested to be linked with reward processing. We therefore identify two different covariates that model activity in the Nac: specific properties of a psychological task and individual character traits.
A match made by modafinil: probability matching in choice decisions and spatial attention.
Geng, Joy J; Soosman, Steffan; Sun, Yile; Diquattro, Nicholas E; Stankevitch, Beth; Minzenberg, Michael J
2013-05-01
When predicting where a target or reward will be, participants tend to choose each location commensurate with the true underlying probability (i.e., probability match). The strategy of probability matching involves independent sampling of high and low probability locations on separate trials. In contrast, models of probabilistic spatial attention hypothesize that on any given trial attention will either be weighted toward the high probability location or be distributed equally across all locations. Thus, the strategies of probabilistic sampling by choice decisions and spatial attention appear to differ with regard to low-probability events. This distinction is somewhat surprising because similar brain mechanisms (e.g., pFC-mediated cognitive control) are thought to be important in both functions. Thus, the goal of the current study was to examine the relationship between choice decisions and attentional selection within single trials to test for any strategic differences, then to determine whether that relationship is malleable to manipulations of catecholamine-modulated cognitive control with the drug modafinil. Our results demonstrate that spatial attention and choice decisions followed different strategies of probabilistic information selection on placebo, but that modafinil brought the pattern of spatial attention into alignment with that of predictive choices. Modafinil also produced earlier learning of the probability distribution. Together, these results suggest that enhancing cognitive control mechanisms (e.g., through prefrontal cortical function) leads spatial attention to follow choice decisions in selecting information according to rule-based expectations.
Estimating the exceedance probability of extreme rainfalls up to the probable maximum precipitation
NASA Astrophysics Data System (ADS)
Nathan, Rory; Jordan, Phillip; Scorah, Matthew; Lang, Simon; Kuczera, George; Schaefer, Melvin; Weinmann, Erwin
2016-12-01
If risk-based criteria are used in the design of high hazard structures (such as dam spillways and nuclear power stations), then it is necessary to estimate the annual exceedance probability (AEP) of extreme rainfalls up to and including the Probable Maximum Precipitation (PMP). This paper describes the development and application of two largely independent methods to estimate the frequencies of such extreme rainfalls. One method is based on stochastic storm transposition (SST), which combines the "arrival" and "transposition" probabilities of an extreme storm using the total probability theorem. The second method, based on "stochastic storm regression" (SSR), combines frequency curves of point rainfalls with regression estimates of local and transposed areal rainfalls; rainfall maxima are generated by stochastically sampling the independent variates, where the required exceedance probabilities are obtained using the total probability theorem. The methods are applied to two large catchments (with areas of 3550 km2 and 15,280 km2) located in inland southern Australia. Both methods were found to provide similar estimates of the frequency of extreme areal rainfalls for the two study catchments. The best estimates of the AEP of the PMP for the smaller and larger of the catchments were found to be 10-7 and 10-6, respectively, but the uncertainty of these estimates spans one to two orders of magnitude. Additionally, the SST method was applied to a range of locations within a meteorologically homogenous region to investigate the nature of the relationship between the AEP of PMP and catchment area.
Elemental mercury poisoning probably causes cortical myoclonus.
Ragothaman, Mona; Kulkarni, Girish; Ashraf, Valappil V; Pal, Pramod K; Chickabasavaiah, Yasha; Shankar, Susarla K; Govindappa, Srikanth S; Satishchandra, Parthasarthy; Muthane, Uday B
2007-10-15
Mercury toxicity causes postural tremors, commonly referred to as "mercurial tremors," and cerebellar dysfunction. A 23-year woman, 2 years after injecting herself with elemental mercury developed disabling generalized myoclonus and ataxia. Electrophysiological studies confirmed the myoclonus was probably of cortical origin. Her deficits progressed over 2 years and improved after subcutaneous mercury deposits at the injection site were surgically cleared. Myoclonus of cortical origin has never been described in mercury poisoning. It is important to ask patients presenting with jerks about exposure to elemental mercury even if they have a progressive illness, as it is a potentially reversible condition as in our patient.
The Prediction of Spatial Aftershock Probabilities (PRESAP)
NASA Astrophysics Data System (ADS)
McCloskey, J.
2003-12-01
It is now widely accepted that the goal of deterministic earthquake prediction is unattainable in the short term and may even be forbidden by nonlinearity in the generating dynamics. This nonlinearity does not, however, preclude the estimation of earthquake probability and, in particular, how this probability might change in space and time; earthquake hazard estimation might be possible in the absence of earthquake prediction. Recently, there has been a major development in the understanding of stress triggering of earthquakes which allows accurate calculation of the spatial variation of aftershock probability following any large earthquake. Over the past few years this Coulomb stress technique (CST) has been the subject of intensive study in the geophysics literature and has been extremely successful in explaining the spatial distribution of aftershocks following several major earthquakes. The power of current micro-computers, the great number of local, telemeter seismic networks, the rapid acquisition of data from satellites coupled with the speed of modern telecommunications and data transfer all mean that it may be possible that these new techniques could be applied in a forward sense. In other words, it is theoretically possible today to make predictions of the likely spatial distribution of aftershocks in near-real-time following a large earthquake. Approximate versions of such predictions could be available within, say, 0.1 days after the mainshock and might be continually refined and updated over the next 100 days. The European Commission has recently provided funding for a project to assess the extent to which it is currently possible to move CST predictions into a practically useful time frame so that low-confidence estimates of aftershock probability might be made within a few hours of an event and improved in near-real-time, as data of better quality become available over the following day to tens of days. Specifically, the project aim is to assess the
Probable interaction between trazodone and carbamazepine.
Sánchez-Romero, A; Mayordomo-Aranda, A; García-Delgado, R; Durán-Quintana, J A
2011-06-01
The need to maintain long-term treatment of chronic pathologies makes the appearance of interactions possible when such therapies incorporate other drugs to deal with the aggravation of the same or other intercurrent pathologies. A case is presented in which the addition of trazodone to a chronic treatment with carbamazepine (CBZ) is associated with symptoms typical for intoxication by this antiepileptic, accompanied by a raised serum concentration. When the trazodone was suspended, these symptoms lessened and the concentration of CBZ decreased progressively, suggesting a probable interaction between the 2 drugs.
Atomic transition probabilities of Gd i
NASA Astrophysics Data System (ADS)
Lawler, J. E.; Bilty, K. A.; Den Hartog, E. A.
2011-05-01
Fourier transform spectra are used to determine emission branching fractions for 1290 lines of the first spectrum of gadolinium (Gd i). These branching fractions are converted to absolute atomic transition probabilities using previously reported radiative lifetimes from time-resolved laser-induced-fluorescence measurements (Den Hartog et al 2011 J. Phys. B: At. Mol. Opt. Phys. 44 055001). The wavelength range of the data set is from 300 to 1850 nm. A least squares technique for separating blends of the first and second spectra lines is also described and demonstrated in this work.
Atomic transition probabilities of Er i
NASA Astrophysics Data System (ADS)
Lawler, J. E.; Wyart, J.-F.; Den Hartog, E. A.
2010-12-01
Atomic transition probabilities for 562 lines of the first spectrum of erbium (Er i) are reported. These data are from new branching fraction measurements on Fourier transform spectra normalized with previously reported radiative lifetimes from time-resolved laser-induced fluorescence measurements (Den Hartog et al 2010 J. Phys. B: At. Mol. Opt. Phys. 43 155004). The wavelength range of the data set is from 298 to 1981 nm. In this work we explore the utility of parametric fits based on the Cowan code in assessing branching fraction errors due to lines connecting to unobserved lower levels.
Modulation Based on Probability Density Functions
NASA Technical Reports Server (NTRS)
Williams, Glenn L.
2009-01-01
A proposed method of modulating a sinusoidal carrier signal to convey digital information involves the use of histograms representing probability density functions (PDFs) that characterize samples of the signal waveform. The method is based partly on the observation that when a waveform is sampled (whether by analog or digital means) over a time interval at least as long as one half cycle of the waveform, the samples can be sorted by frequency of occurrence, thereby constructing a histogram representing a PDF of the waveform during that time interval.
Probability density functions in turbulent channel flow
NASA Technical Reports Server (NTRS)
Dinavahi, Surya P. G.
1992-01-01
The probability density functions (pdf's) of the fluctuating velocity components, as well as their first and second derivatives, are calculated using data from the direct numerical simulations (DNS) of fully developed turbulent channel flow. It is observed that, beyond the buffer region, the pdf of each of these quantities is independent of the distance from the channel wall. It is further observed that, beyond the buffer region, the pdf's for all the first derivatives collapse onto a single universal curve and those of the second derivatives also collapse onto another universal curve, irrespective of the distance from the wall. The kinetic-energy dissipation rate exhibits log normal behavior.
Neural dynamics of reward probability coding: a Magnetoencephalographic study in humans.
Thomas, Julie; Vanni-Mercier, Giovanna; Dreher, Jean-Claude
2013-01-01
Prediction of future rewards and discrepancy between actual and expected outcomes (prediction error) are crucial signals for adaptive behavior. In humans, a number of fMRI studies demonstrated that reward probability modulates these two signals in a large brain network. Yet, the spatio-temporal dynamics underlying the neural coding of reward probability remains unknown. Here, using magnetoencephalography, we investigated the neural dynamics of prediction and reward prediction error computations while subjects learned to associate cues of slot machines with monetary rewards with different probabilities. We showed that event-related magnetic fields (ERFs) arising from the visual cortex coded the expected reward value 155 ms after the cue, demonstrating that reward value signals emerge early in the visual stream. Moreover, a prediction error was reflected in ERF peaking 300 ms after the rewarded outcome and showing decreasing amplitude with higher reward probability. This prediction error signal was generated in a network including the anterior and posterior cingulate cortex. These findings pinpoint the spatio-temporal characteristics underlying reward probability coding. Together, our results provide insights into the neural dynamics underlying the ability to learn probabilistic stimuli-reward contingencies.
Estimating flood exceedance probabilities in estuarine regions
NASA Astrophysics Data System (ADS)
Westra, Seth; Leonard, Michael
2016-04-01
Flood events in estuarine regions can arise from the interaction of extreme rainfall and storm surge. Determining flood level exceedance probabilities in these regions is complicated by the dependence of these processes for extreme events. A comprehensive study of tide and rainfall gauges along the Australian coastline was conducted to determine the dependence of these extremes using a bivariate logistic threshold-excess model. The dependence strength is shown to vary as a function of distance over many hundreds of kilometres indicating that the dependence arises due to synoptic scale meteorological forcings. It is also shown to vary as a function of storm burst duration, time lag between the extreme rainfall and the storm surge event. The dependence estimates are then used with a bivariate design variable method to determine flood risk in estuarine regions for a number of case studies. Aspects of the method demonstrated in the case studies include, the resolution and range of the hydraulic response table, fitting of probability distributions, computational efficiency, uncertainty, potential variation in marginal distributions due to climate change, and application to two dimensional output from hydraulic models. Case studies are located on the Swan River (Western Australia), Nambucca River and Hawkesbury Nepean River (New South Wales).
An all-timescales rainfall probability distribution
NASA Astrophysics Data System (ADS)
Papalexiou, S. M.; Koutsoyiannis, D.
2009-04-01
The selection of a probability distribution for rainfall intensity at many different timescales simultaneously is of primary interest and importance as typically the hydraulic design strongly depends on the rainfall model choice. It is well known that the rainfall distribution may have a long tail, is highly skewed at fine timescales and tends to normality as the timescale increases. This behaviour, explained by the maximum entropy principle (and for large timescales also by the central limit theorem), indicates that the construction of a "universal" probability distribution, capable to adequately describe the rainfall in all timescales, is a difficult task. A search in hydrological literature confirms this argument, as many different distributions have been proposed as appropriate models for different timescales or even for the same timescale, such as Normal, Skew-Normal, two- and three-parameter Log-Normal, Log-Normal mixtures, Generalized Logistic, Pearson Type III, Log-Pearson Type III, Wakeby, Generalized Pareto, Weibull, three- and four-parameter Kappa distribution, and many more. Here we study a single flexible four-parameter distribution for rainfall intensity (the JH distribution) and derive its basic statistics. This distribution incorporates as special cases many other well known distributions, and is capable of describing rainfall in a great range of timescales. Furthermore, we demonstrate the excellent fitting performance of the distribution in various rainfall samples from different areas and for timescales varying from sub-hourly to annual.
Computation-distributed probability hypothesis density filter
NASA Astrophysics Data System (ADS)
Wang, Junjie; Zhao, Lingling; Su, Xiaohong; Shi, Chunmei; Ma, JiQuan
2016-12-01
Particle probability hypothesis density filtering has become a promising approach for multi-target tracking due to its capability of handling an unknown and time-varying number of targets in a nonlinear, non-Gaussian system. However, its computational complexity linearly increases with the number of obtained observations and the number of particles, which can be very time consuming, particularly when numerous targets and clutter exist in the surveillance region. To address this issue, we present a distributed computation particle probability hypothesis density(PHD) filter for target tracking. It runs several local decomposed particle PHD filters in parallel while processing elements. Each processing element takes responsibility for a portion of particles but all measurements and provides local estimates. A central unit controls particle exchange among the processing elements and specifies a fusion rule to match and fuse the estimates from different local filters. The proposed framework is suitable for parallel implementation. Simulations verify that the proposed method can significantly accelerate and maintain a comparative accuracy compared to the standard particle PHD filter.
Measures, Probability and Holography in Cosmology
NASA Astrophysics Data System (ADS)
Phillips, Daniel
This dissertation compiles four research projects on predicting values for cosmological parameters and models of the universe on the broadest scale. The first examines the Causal Entropic Principle (CEP) in inhomogeneous cosmologies. The CEP aims to predict the unexpectedly small value of the cosmological constant Lambda using a weighting by entropy increase on causal diamonds. The original work assumed a purely isotropic and homogeneous cosmology. But even the level of inhomogeneity observed in our universe forces reconsideration of certain arguments about entropy production. In particular, we must consider an ensemble of causal diamonds associated with each background cosmology and we can no longer immediately discard entropy production in the far future of the universe. Depending on our choices for a probability measure and our treatment of black hole evaporation, the prediction for Lambda may be left intact or dramatically altered. The second related project extends the CEP to universes with curvature. We have found that curvature values larger than rho k = 40rhom are disfavored by more than $99.99% and a peak value at rhoLambda = 7.9 x 10-123 and rhok =4.3rho m for open universes. For universes that allow only positive curvature or both positive and negative curvature, we find a correlation between curvature and dark energy that leads to an extended region of preferred values. Our universe is found to be disfavored to an extent depending the priors on curvature. We also provide a comparison to previous anthropic constraints on open universes and discuss future directions for this work. The third project examines how cosmologists should formulate basic questions of probability. We argue using simple models that all successful practical uses of probabilities originate in quantum fluctuations in the microscopic physical world around us, often propagated to macroscopic scales. Thus we claim there is no physically verified fully classical theory of probability. We
The Human Bathtub: Safety and Risk Predictions Including the Dynamic Probability of Operator Errors
Duffey, Romney B.; Saull, John W.
2006-07-01
Reactor safety and risk are dominated by the potential and major contribution for human error in the design, operation, control, management, regulation and maintenance of the plant, and hence to all accidents. Given the possibility of accidents and errors, now we need to determine the outcome (error) probability, or the chance of failure. Conventionally, reliability engineering is associated with the failure rate of components, or systems, or mechanisms, not of human beings in and interacting with a technological system. The probability of failure requires a prior knowledge of the total number of outcomes, which for any predictive purposes we do not know or have. Analysis of failure rates due to human error and the rate of learning allow a new determination of the dynamic human error rate in technological systems, consistent with and derived from the available world data. The basis for the analysis is the 'learning hypothesis' that humans learn from experience, and consequently the accumulated experience defines the failure rate. A new 'best' equation has been derived for the human error, outcome or failure rate, which allows for calculation and prediction of the probability of human error. We also provide comparisons to the empirical Weibull parameter fitting used in and by conventional reliability engineering and probabilistic safety analysis methods. These new analyses show that arbitrary Weibull fitting parameters and typical empirical hazard function techniques cannot be used to predict the dynamics of human errors and outcomes in the presence of learning. Comparisons of these new insights show agreement with human error data from the world's commercial airlines, the two shuttle failures, and from nuclear plant operator actions and transient control behavior observed in transients in both plants and simulators. The results demonstrate that the human error probability (HEP) is dynamic, and that it may be predicted using the learning hypothesis and the minimum
Significance of "high probability/low damage" versus "low probability/high damage" flood events
NASA Astrophysics Data System (ADS)
Merz, B.; Elmer, F.; Thieken, A. H.
2009-06-01
The need for an efficient use of limited resources fosters the application of risk-oriented design in flood mitigation. Flood defence measures reduce future damage. Traditionally, this benefit is quantified via the expected annual damage. We analyse the contribution of "high probability/low damage" floods versus the contribution of "low probability/high damage" events to the expected annual damage. For three case studies, i.e. actual flood situations in flood-prone communities in Germany, it is shown that the expected annual damage is dominated by "high probability/low damage" events. Extreme events play a minor role, even though they cause high damage. Using typical values for flood frequency behaviour, flood plain morphology, distribution of assets and vulnerability, it is shown that this also holds for the general case of river floods in Germany. This result is compared to the significance of extreme events in the public perception. "Low probability/high damage" events are more important in the societal view than it is expressed by the expected annual damage. We conclude that the expected annual damage should be used with care since it is not in agreement with societal priorities. Further, risk aversion functions that penalise events with disastrous consequences are introduced in the appraisal of risk mitigation options. It is shown that risk aversion may have substantial implications for decision-making. Different flood mitigation decisions are probable, when risk aversion is taken into account.
Naive Probability: A Mental Model Theory of Extensional Reasoning.
ERIC Educational Resources Information Center
Johnson-Laird, P. N.; Legrenzi, Paolo; Girotto, Vittorio; Legrenzi, Maria Sonino; Caverni, Jean-Paul
1999-01-01
Outlines a theory of naive probability in which individuals who are unfamiliar with the probability calculus can infer the probabilities of events in an "extensional" way. The theory accommodates reasoning based on numerical premises, and explains how naive reasoners can infer posterior probabilities without relying on Bayes's theorem.…
Economic choices reveal probability distortion in macaque monkeys.
Stauffer, William R; Lak, Armin; Bossaerts, Peter; Schultz, Wolfram
2015-02-18
Economic choices are largely determined by two principal elements, reward value (utility) and probability. Although nonlinear utility functions have been acknowledged for centuries, nonlinear probability weighting (probability distortion) was only recently recognized as a ubiquitous aspect of real-world choice behavior. Even when outcome probabilities are known and acknowledged, human decision makers often overweight low probability outcomes and underweight high probability outcomes. Whereas recent studies measured utility functions and their corresponding neural correlates in monkeys, it is not known whether monkeys distort probability in a manner similar to humans. Therefore, we investigated economic choices in macaque monkeys for evidence of probability distortion. We trained two monkeys to predict reward from probabilistic gambles with constant outcome values (0.5 ml or nothing). The probability of winning was conveyed using explicit visual cues (sector stimuli). Choices between the gambles revealed that the monkeys used the explicit probability information to make meaningful decisions. Using these cues, we measured probability distortion from choices between the gambles and safe rewards. Parametric modeling of the choices revealed classic probability weighting functions with inverted-S shape. Therefore, the animals overweighted low probability rewards and underweighted high probability rewards. Empirical investigation of the behavior verified that the choices were best explained by a combination of nonlinear value and nonlinear probability distortion. Together, these results suggest that probability distortion may reflect evolutionarily preserved neuronal processing.
On the probability of dinosaur fleas.
Dittmar, Katharina; Zhu, Qiyun; Hastriter, Michael W; Whiting, Michael F
2016-01-11
Recently, a set of publications described flea fossils from Jurassic and Early Cretaceous geological strata in northeastern China, which were suggested to have parasitized feathered dinosaurs, pterosaurs, and early birds or mammals. In support of these fossils being fleas, a recent publication in BMC Evolutionary Biology described the extended abdomen of a female fossil specimen as due to blood feeding.We here comment on these findings, and conclude that the current interpretation of the evolutionary trajectory and ecology of these putative dinosaur fleas is based on appeal to probability, rather than evidence. Hence, their taxonomic positioning as fleas, or stem fleas, as well as their ecological classification as ectoparasites and blood feeders is not supported by currently available data.
Quantum probabilities for inflation from holography
Hartle, James B.; Hawking, S.W.; Hertog, Thomas E-mail: S.W.Hawking@damtp.cam.ac.uk
2014-01-01
The evolution of the universe is determined by its quantum state. The wave function of the universe obeys the constraints of general relativity and in particular the Wheeler-DeWitt equation (WDWE). For non-zero Λ, we show that solutions of the WDWE at large volume have two domains in which geometries and fields are asymptotically real. In one the histories are Euclidean asymptotically anti-de Sitter, in the other they are Lorentzian asymptotically classical de Sitter. Further, the universal complex semiclassical asymptotic structure of solutions of the WDWE implies that the leading order in h-bar quantum probabilities for classical, asymptotically de Sitter histories can be obtained from the action of asymptotically anti-de Sitter configurations. This leads to a promising, universal connection between quantum cosmology and holography.
Carrier Modulation Via Waveform Probability Density Function
NASA Technical Reports Server (NTRS)
Williams, Glenn L.
2006-01-01
Beyond the classic modes of carrier modulation by varying amplitude (AM), phase (PM), or frequency (FM), we extend the modulation domain of an analog carrier signal to include a class of general modulations which are distinguished by their probability density function histogram. Separate waveform states are easily created by varying the pdf of the transmitted waveform. Individual waveform states are assignable as proxies for digital one's or zero's. At the receiver, these states are easily detected by accumulating sampled waveform statistics and performing periodic pattern matching, correlation, or statistical filtering. No fundamental physical laws are broken in the detection process. We show how a typical modulation scheme would work in the digital domain and suggest how to build an analog version. We propose that clever variations of the modulating waveform (and thus the histogram) can provide simple steganographic encoding.
Probability-one homotopies in computational science
NASA Astrophysics Data System (ADS)
Watson, Layne T.
2002-03-01
Probability-one homotopy algorithms are a class of methods for solving nonlinear systems of equations that, under mild assumptions, are globally convergent for a wide range of problems in science and engineering. Convergence theory, robust numerical algorithms, and production quality mathematical software exist for general nonlinear systems of equations, and special cases such as Brouwer fixed point problems, polynomial systems, and nonlinear constrained optimization. Using a sample of challenging scientific problems as motivation, some pertinent homotopy theory and algorithms are presented. The problems considered are analog circuit simulation (for nonlinear systems), reconfigurable space trusses (for polynomial systems), and fuel-optimal orbital rendezvous (for nonlinear constrained optimization). The mathematical software packages HOMPACK90 and POLSYS_PLP are also briefly described.
Audio feature extraction using probability distribution function
NASA Astrophysics Data System (ADS)
Suhaib, A.; Wan, Khairunizam; Aziz, Azri A.; Hazry, D.; Razlan, Zuradzman M.; Shahriman A., B.
2015-05-01
Voice recognition has been one of the popular applications in robotic field. It is also known to be recently used for biometric and multimedia information retrieval system. This technology is attained from successive research on audio feature extraction analysis. Probability Distribution Function (PDF) is a statistical method which is usually used as one of the processes in complex feature extraction methods such as GMM and PCA. In this paper, a new method for audio feature extraction is proposed which is by using only PDF as a feature extraction method itself for speech analysis purpose. Certain pre-processing techniques are performed in prior to the proposed feature extraction method. Subsequently, the PDF result values for each frame of sampled voice signals obtained from certain numbers of individuals are plotted. From the experimental results obtained, it can be seen visually from the plotted data that each individuals' voice has comparable PDF values and shapes.
Microtechnique for most-probable-number analysis.
Rowe, R; Todd, R; Waide, J
1977-03-01
A microtechnique based on the most-probable-number (MPN) method has been developed for the enumeration of the ammonium-oxidizing population in soil samples. An MPN table for a research design ([8 by 12] i.e., 12 dilutions, 8 replicates per dilution) is presented. A correlation of 0.68 was found between MPNs determined by the microtechnique and the standard tube technique. Higher MPNs were obtained with the microtechnique with increased accuracy in endpoint determinations being a possible cause. Considerable savings of time, space, equipment, and reagents are observed using this method. The microtechnique described may be adapted to other microbial populations using various types of media and endpoint determinations.
Continuity of percolation probability on hyperbolic graphs
NASA Astrophysics Data System (ADS)
Wu, C. Chris
1997-05-01
Let T k be a forwarding tree of degree k where each vertex other than the origin has k children and one parent and the origin has k children but no parent ( k≥2). Define G to be the graph obtained by adding to T k nearest neighbor bonds connecting the vertices which are in the same generation. G is regarded as a discretization of the hyperbolic plane H 2 in the same sense that Z d is a discretization of R d . Independent percolation on G has been proved to have multiple phase transitions. We prove that the percolation probability O(p) is continuous on [0,1] as a function of p.
Probability and delay discounting of erotic stimuli.
Lawyer, Steven R
2008-09-01
Adult undergraduate men (n=38) and women (n=33) were categorized as erotica "users" (n=34) and "non-users" (n=37) based on their responses to screening questions and completed computerized delay and probability discounting tasks concerning hypothetical money and erotica. Erotica users discounted the value of erotica similarly to money on three of the four erotica tasks; erotica non-users discounted the value of money consistent with erotica users, but not the value of erotica. Erotica users were disproportionately male, scored higher on several psychometric measures of sexuality-related constructs, and exhibited more impulsive choice patterns on the delay discounting for money task than erotica non-users did. These findings suggest that discounting processes generalize to erotic outcomes for some individuals.
Trending in Probability of Collision Measurements
NASA Technical Reports Server (NTRS)
Vallejo, J. J.; Hejduk, M. D.; Stamey, J. D.
2015-01-01
A simple model is proposed to predict the behavior of Probabilities of Collision (P(sub c)) for conjunction events. The model attempts to predict the location and magnitude of the peak P(sub c) value for an event by assuming the progression of P(sub c) values can be modeled to first order by a downward-opening parabola. To incorporate prior information from a large database of past conjunctions, the Bayes paradigm is utilized; and the operating characteristics of the model are established through a large simulation study. Though the model is simple, it performs well in predicting the temporal location of the peak (P(sub c)) and thus shows promise as a decision aid in operational conjunction assessment risk analysis.
Carrier Modulation Via Waveform Probability Density Function
NASA Technical Reports Server (NTRS)
Williams, Glenn L.
2004-01-01
Beyond the classic modes of carrier modulation by varying amplitude (AM), phase (PM), or frequency (FM), we extend the modulation domain of an analog carrier signal to include a class of general modulations which are distinguished by their probability density function histogram. Separate waveform states are easily created by varying the pdf of the transmitted waveform. Individual waveform states are assignable as proxies for digital ONEs or ZEROs. At the receiver, these states are easily detected by accumulating sampled waveform statistics and performing periodic pattern matching, correlation, or statistical filtering. No fundamental natural laws are broken in the detection process. We show how a typical modulation scheme would work in the digital domain and suggest how to build an analog version. We propose that clever variations of the modulating waveform (and thus the histogram) can provide simple steganographic encoding.
Microtechnique for Most-Probable-Number Analysis
Rowe, R.; Todd, R.; Waide, J.
1977-01-01
A microtechnique based on the most-probable-number (MPN) method has been developed for the enumeration of the ammonium-oxidizing population in soil samples. An MPN table for a research design ([8 by 12] i.e., 12 dilutions, 8 replicates per dilution) is presented. A correlation of 0.68 was found between MPNs determined by the microtechnique and the standard tube technique. Higher MPNs were obtained with the microtechnique with increased accuracy in endpoint determinations being a possible cause. Considerable savings of time, space, equipment, and reagents are observed using this method. The microtechnique described may be adapted to other microbial populations using various types of media and endpoint determinations. Images PMID:16345226
Parabolic Ejecta Features on Titan? Probably Not
NASA Astrophysics Data System (ADS)
Lorenz, R. D.; Melosh, H. J.
1996-03-01
Radar mapping of Venus by Magellan indicated a number of dark parabolic features, associated with impact craters. A suggested mechanism for generating such features is that ejecta from the impact event is 'winnowed' by the zonal wind field, with smaller ejecta particles falling out of the atmosphere more slowly, and hence drifting further. What discriminates such features from simple wind streaks is the 'stingray' or parabolic shape. This is due to the ejecta's spatial distribution prior to being winnowed during fallout, and this distribution is generated by the explosion plume of the impact piercing the atmosphere, allowing the ejecta to disperse pseudoballistically before re-entering the atmosphere, decelerating to terminal velocity and then being winnowed. Here we apply this model to Titan, which has a zonal wind field similar to that of Venus. We find that Cassini will probably not find parabolic features, as the winds stretch the deposition so far that ejecta will form streaks or bands instead.
Dinov, Ivo D; Kamino, Scott; Bhakhrani, Bilal; Christou, Nicolas
2013-01-01
Data analysis requires subtle probability reasoning to answer questions like What is the chance of event A occurring, given that event B was observed? This generic question arises in discussions of many intriguing scientific questions such as What is the probability that an adolescent weighs between 120 and 140 pounds given that they are of average height? and What is the probability of (monetary) inflation exceeding 4% and housing price index below 110? To address such problems, learning some applied, theoretical or cross-disciplinary probability concepts is necessary. Teaching such courses can be improved by utilizing modern information technology resources. Students' understanding of multivariate distributions, conditional probabilities, correlation and causation can be significantly strengthened by employing interactive web-based science educational resources. Independent of the type of a probability course (e.g. majors, minors or service probability course, rigorous measure-theoretic, applied or statistics course) student motivation, learning experiences and knowledge retention may be enhanced by blending modern technological tools within the classical conceptual pedagogical models. We have designed, implemented and disseminated a portable open-source web-application for teaching multivariate distributions, marginal, joint and conditional probabilities using the special case of bivariate Normal distribution. A real adolescent height and weight dataset is used to demonstrate the classroom utilization of the new web-application to address problems of parameter estimation, univariate and multivariate inference.
Bayesian probability approach to ADHD appraisal.
Robeva, Raina; Penberthy, Jennifer Kim
2009-01-01
Accurate diagnosis of attentional disorders such as attention-deficit hyperactivity disorder (ADHD) is imperative because there are multiple negative psychosocial sequelae related to undiagnosed and untreated ADHD. Early and accurate detection can lead to effective intervention and prevention of negative sequelae. Unfortunately, diagnosing ADHD presents a challenge to traditional assessment paradigms because there is no single test that definitively establishes its presence. Even though ADHD is a physiologically based disorder with a multifactorial etiology, the diagnosis has been traditionally based on a subjective history of symptoms. In this chapter we outline a stochastic method that utilizes a Bayesian interface for quantifying and assessing ADHD. It can be used to combine of a variety of psychometric tests and physiological markers into a single standardized instrument that, on each step, refines a probability for ADHD for each individual based on information provided by the individual assessments. The method is illustrated with data from a small study of six college female students with ADHD and six matched controls in which the method achieves correct classification for all participants, where none of the individual assessments was capable of achieving perfect classification. Further, we provide a framework for applying this Bayesian method for performing meta-analysis of data obtained from disparate studies and using disparate tests for ADHD based on calibration of the data into a unified probability scale. We use this method to combine data from five studies that examine the diagnostic abilities of different behavioral rating scales and EEG assessments of ADHD, enrolling a total of 56 ADHD and 55 control subjects of different age groups and gender.
Helton, J.C.
1996-03-01
A formal description of the structure of several recent performance assessments (PAs) for the Waste Isolation Pilot Plant (WIPP) is given in terms of the following three components: a probability space (S{sub st}, S{sub st}, p{sub st}) for stochastic uncertainty, a probability space (S{sub su}, S{sub su}, p{sub su}) for subjective uncertainty and a function (i.e., a random variable) defined on the product space associated with (S{sub st}, S{sub st}, p{sub st}) and (S{sub su}, S{sub su}, p{sub su}). The explicit recognition of the existence of these three components allows a careful description of the use of probability, conditional probability and complementary cumulative distribution functions within the WIPP PA. This usage is illustrated in the context of the U.S. Environmental Protection Agency`s standard for the geologic disposal of radioactive waste (40 CFR 191, Subpart B). The paradigm described in this presentation can also be used to impose a logically consistent structure on PAs for other complex systems.
Mixture Modeling of Individual Learning Curves
ERIC Educational Resources Information Center
Streeter, Matthew
2015-01-01
We show that student learning can be accurately modeled using a mixture of learning curves, each of which specifies error probability as a function of time. This approach generalizes Knowledge Tracing [7], which can be viewed as a mixture model in which the learning curves are step functions. We show that this generality yields order-of-magnitude…
ERIC Educational Resources Information Center
Noddings, Nel
2004-01-01
Most teachers have been good students. Some students are fast learners and attain the required knowledge and skills easily; others are obedient, hard workers. In either case, teachers are likely to believe that if students really try, they will do well. Listening to students over many years, the author has learned that this is probably not true.…
Most probable longest common subsequence for recognition of gesture character input.
Frolova, Darya; Stern, Helman; Berman, Sigal
2013-06-01
This paper presents a technique for trajectory classification with applications to dynamic free-air hand gesture recognition. Such gestures are unencumbered and drawn in free air. Our approach is an extension to the longest common subsequence (LCS) classification algorithm. A learning preprocessing stage is performed to create a probabilistic 2-D template for each gesture, which allows taking into account different trajectory distortions with different probabilities. The modified LCS, termed the most probable LCS (MPLCS), is developed to measure the similarity between the probabilistic template and the hand gesture sample. The final decision is based on the length and probability of the extracted subsequence. Validation tests using a cohort of gesture digits from video-based capture show that the approach is promising with a recognition rate of more than 98 % for video stream preisolated digits. The MPLCS algorithm can be integrated into a gesture recognition interface to facilitate gesture character input. This can greatly enhance the usability of such interfaces.
A model selection algorithm for a posteriori probability estimation with neural networks.
Arribas, Juan Ignacio; Cid-Sueiro, Jesús
2005-07-01
This paper proposes a novel algorithm to jointly determine the structure and the parameters of a posteriori probability model based on neural networks (NNs). It makes use of well-known ideas of pruning, splitting, and merging neural components and takes advantage of the probabilistic interpretation of these components. The algorithm, so called a posteriori probability model selection (PPMS), is applied to an NN architecture called the generalized softmax perceptron (GSP) whose outputs can be understood as probabilities although results shown can be extended to more general network architectures. Learning rules are derived from the application of the expectation-maximization algorithm to the GSP-PPMS structure. Simulation results show the advantages of the proposed algorithm with respect to other schemes.
Transit probabilities around hypervelocity and runaway stars
NASA Astrophysics Data System (ADS)
Fragione, G.; Ginsburg, I.
2017-04-01
In the blooming field of exoplanetary science, NASA's Kepler Space Telescope has revolutionized our understanding of exoplanets. Kepler's very precise and long-duration photometry is ideal for detecting planetary transits around Sun-like stars. The forthcoming Transiting Exoplanet Survey Satellite (TESS) is expected to continue Kepler's legacy. Along with transits, the Doppler technique remains an invaluable tool for discovering planets. The next generation of spectrographs, such as G-CLEF, promise precision radial velocity measurements. In this paper, we explore the possibility of detecting planets around hypervelocity and runaway stars, which should host a very compact system as consequence of their turbulent origin. We find that the probability of a multiplanetary transit is 10-3 ≲ P ≲ 10-1. We therefore need to observe ∼10-1000 high-velocity stars to spot a transit. However, even if transits are rare around runaway and hypervelocity stars, the chances of detecting such planets using radial velocity surveys is high. We predict that the European Gaia satellite, along with TESS and the new-generation spectrographs G-CLEF and ESPRESSO, will spot planetary systems orbiting high-velocity stars.
Essays on probability elicitation scoring rules
NASA Astrophysics Data System (ADS)
Firmino, Paulo Renato A.; dos Santos Neto, Ademir B.
2012-10-01
In probability elicitation exercises it has been usual to considerer scoring rules (SRs) to measure the performance of experts when inferring about a given unknown, Θ, for which the true value, θ*, is (or will shortly be) known to the experimenter. Mathematically, SRs quantify the discrepancy between f(θ) (the distribution reflecting the expert's uncertainty about Θ) and d(θ), a zero-one indicator function of the observation θ*. Thus, a remarkable characteristic of SRs is to contrast expert's beliefs with the observation θ*. The present work aims at extending SRs concepts and formulas for the cases where Θ is aleatory, highlighting advantages of goodness-of-fit and entropy-like measures. Conceptually, it is argued that besides of evaluating the personal performance of the expert, SRs may also play a role when comparing the elicitation processes adopted to obtain f(θ). Mathematically, it is proposed to replace d(θ) by g(θ), the distribution that model the randomness of Θ, and do also considerer goodness-of-fit and entropylike metrics, leading to SRs that measure the adherence of f(θ) to g(θ). The implications of this alternative perspective are discussed and illustrated by means of case studies based on the simulation of controlled experiments. The usefulness of the proposed approach for evaluating the performance of experts and elicitation processes is investigated.
Probability judgments under ambiguity and conflict.
Smithson, Michael
2015-01-01
Whether conflict and ambiguity are distinct kinds of uncertainty remains an open question, as does their joint impact on judgments of overall uncertainty. This paper reviews recent advances in our understanding of human judgment and decision making when both ambiguity and conflict are present, and presents two types of testable models of judgments under conflict and ambiguity. The first type concerns estimate-pooling to arrive at "best" probability estimates. The second type is models of subjective assessments of conflict and ambiguity. These models are developed for dealing with both described and experienced information. A framework for testing these models in the described-information setting is presented, including a reanalysis of a multi-nation data-set to test best-estimate models, and a study of participants' assessments of conflict, ambiguity, and overall uncertainty reported by Smithson (2013). A framework for research in the experienced-information setting is then developed, that differs substantially from extant paradigms in the literature. This framework yields new models of "best" estimates and perceived conflict. The paper concludes with specific suggestions for future research on judgment and decision making under conflict and ambiguity.
Atomic Transition Probabilities for Neutral Cerium
NASA Astrophysics Data System (ADS)
Lawler, J. E.; den Hartog, E. A.; Wood, M. P.; Nitz, D. E.; Chisholm, J.; Sobeck, J.
2009-10-01
The spectra of neutral cerium (Ce I) and singly ionized cerium (Ce II) are more complex than spectra of other rare earth species. The resulting high density of lines in the visible makes Ce ideal for use in metal halide (MH) High Intensity Discharge (HID) lamps. Inclusion of cerium-iodide in a lamp dose can improve both the Color Rendering Index and luminous efficacy of a MH-HID lamp. Basic spectroscopic data including absolute atomic transition probabilities for Ce I and Ce II are needed for diagnosing and modeling these MH-HID lamps. Recent work on Ce II [1] is now being augmented with similar work on Ce I. Radiative lifetimes from laser induced fluorescence measurements [2] on neutral Ce are being combined with emission branching fractions from spectra recorded using a Fourier transform spectrometer. A total of 14 high resolution spectra are being analyzed to determine branching fractions for 2000 to 3000 lines from 153 upper levels in neutral Ce. Representative data samples and progress to date will be presented. [4pt] [1] J. E. Lawler, C. Sneden, J. J. Cowan, I. I. Ivans, and E. A. Den Hartog, Astrophys. J. Suppl. Ser. 182, 51-79 (2009). [0pt] [2] E. A. Den Hartog, K. P. Buettner, and J. E. Lawler, J. Phys. B: Atomic, Molecular & Optical Physics 42, 085006 (7pp) (2009).
Atomic Transition Probabilities for Neutral Cerium
NASA Astrophysics Data System (ADS)
Chisholm, John; Nitz, D.; Sobeck, J.; Den Hartog, E. A.; Wood, M. P.; Lawler, J. E.
2010-01-01
Among the rare earth species, the spectra of neutral cerium (Ce I) and singly ionized cerium (Ce II) are some of the most complex. Like other rare earth species, Ce has many lines in the visible which are suitable for elemental abundance studies. Recent work on Ce II transition probabilities [1] is now being augmented with similar work on Ce I for future studies using such lines from astrophysical sources. Radiative lifetimes from laser induced fluorescence measurements [2] on neutral Ce are being combined with emission branching fractions from spectra recorded using a Fourier transform spectrometer. A total of 14 high resolution spectra are being analyzed to determine branching fractions for 2500 to 3000 lines from 153 upper levels in neutral Ce. Representative data samples and progress to date will be presented. This work was supported by the National Science Foundation's REU program and the Department of Defense's ASSURE program through NSF Award AST-0453442 and NSF Grant CTS0613277. [1] J. E. Lawler, C. Sneden, J. J. Cowan, I. I. Ivans, and E. A. Den Hartog, Astrophys. J. Suppl. Ser. 182, 51-79 (2009). [2] E. A. Den Hartog, K. P. Buettner, and J. E. Lawler, J. Phys. B: Atomic, Molecular & Optical Physics 42, 085006 (7pp) (2009).
Lectures on probability and statistics. Revision
Yost, G.P.
1985-06-01
These notes are based on a set of statistics lectures delivered at Imperial College to the first-year postgraduate students in High Energy Physics. They are designed for the professional experimental scientist. They begin with the fundamentals of probability theory, in which one makes statements about the set of possible outcomes of an experiment, based upon a complete a priori understanding of the experiment. For example, in a roll of a set of (fair) dice, one understands a priori that any given side of each die is equally likely to turn up. From that, we can calculate the probabilty of any specified outcome. They finish with the inverse problem, statistics. Here, one begins with a set of actual data (e.g., the outcomes of a number of rolls of the dice), and attempts to make inferences about the state of nature which gave those data (e.g., the likelihood of seeing any given side of any given die turn up). This is a much more difficult problem, of course, and one's solutions often turn out to be unsatisfactory in one respect or another. Hopefully, the reader will come away from these notes with a feel for some of the problems and uncertainties involved. Although there are standard approaches, most of the time there is no cut and dried ''best'' solution - ''best'' according to every criterion.
Insights into decision making using choice probability.
Crapse, Trinity B; Basso, Michele A
2015-12-01
A long-standing question in systems neuroscience is how the activity of single neurons gives rise to our perceptions and actions. Critical insights into this question occurred in the last part of the 20th century when scientists began linking modulations of neuronal activity directly to perceptual behavior. A significant conceptual advance was the application of signal detection theory to both neuronal activity and behavior, providing a quantitative assessment of the relationship between brain and behavior. One metric that emerged from these efforts was choice probability (CP), which provides information about how well an ideal observer can predict the choice an animal makes from a neuron's discharge rate distribution. In this review, we describe where CP has been studied, locational trends in the values found, and why CP values are typically so low. We discuss its dependence on correlated activity among neurons of a population, assess whether it arises from feedforward or feedback mechanisms, and investigate what CP tells us about how many neurons are required for a decision and how they are pooled to do so.
Probability judgments under ambiguity and conflict
Smithson, Michael
2015-01-01
Whether conflict and ambiguity are distinct kinds of uncertainty remains an open question, as does their joint impact on judgments of overall uncertainty. This paper reviews recent advances in our understanding of human judgment and decision making when both ambiguity and conflict are present, and presents two types of testable models of judgments under conflict and ambiguity. The first type concerns estimate-pooling to arrive at “best” probability estimates. The second type is models of subjective assessments of conflict and ambiguity. These models are developed for dealing with both described and experienced information. A framework for testing these models in the described-information setting is presented, including a reanalysis of a multi-nation data-set to test best-estimate models, and a study of participants' assessments of conflict, ambiguity, and overall uncertainty reported by Smithson (2013). A framework for research in the experienced-information setting is then developed, that differs substantially from extant paradigms in the literature. This framework yields new models of “best” estimates and perceived conflict. The paper concludes with specific suggestions for future research on judgment and decision making under conflict and ambiguity. PMID:26042081
Parametric probability distributions for anomalous change detection
Theiler, James P; Foy, Bernard R; Wohlberg, Brendt E; Scovel, James C
2010-01-01
The problem of anomalous change detection arises when two (or possibly more) images are taken of the same scene, but at different times. The aim is to discount the 'pervasive differences' that occur thoughout the imagery, due to the inevitably different conditions under which the images were taken (caused, for instance, by differences in illumination, atmospheric conditions, sensor calibration, or misregistration), and to focus instead on the 'anomalous changes' that actually take place in the scene. In general, anomalous change detection algorithms attempt to model these normal or pervasive differences, based on data taken directly from the imagery, and then identify as anomalous those pixels for which the model does not hold. For many algorithms, these models are expressed in terms of probability distributions, and there is a class of such algorithms that assume the distributions are Gaussian. By considering a broader class of distributions, however, a new class of anomalous change detection algorithms can be developed. We consider several parametric families of such distributions, derive the associated change detection algorithms, and compare the performance with standard algorithms that are based on Gaussian distributions. We find that it is often possible to significantly outperform these standard algorithms, even using relatively simple non-Gaussian models.
Statistical Physics of Pairwise Probability Models
Roudi, Yasser; Aurell, Erik; Hertz, John A.
2009-01-01
Statistical models for describing the probability distribution over the states of biological systems are commonly used for dimensional reduction. Among these models, pairwise models are very attractive in part because they can be fit using a reasonable amount of data: knowledge of the mean values and correlations between pairs of elements in the system is sufficient. Not surprisingly, then, using pairwise models for studying neural data has been the focus of many studies in recent years. In this paper, we describe how tools from statistical physics can be employed for studying and using pairwise models. We build on our previous work on the subject and study the relation between different methods for fitting these models and evaluating their quality. In particular, using data from simulated cortical networks we study how the quality of various approximate methods for inferring the parameters in a pairwise model depends on the time bin chosen for binning the data. We also study the effect of the size of the time bin on the model quality itself, again using simulated data. We show that using finer time bins increases the quality of the pairwise model. We offer new ways of deriving the expressions reported in our previous work for assessing the quality of pairwise models. PMID:19949460
Grain Exchange Probabilities Within a Gravel Bed
NASA Astrophysics Data System (ADS)
Haschenburger, J.
2008-12-01
Sediment transfers in gravel-bed rivers involve the vertical exchange of sediments during floods. These exchanges regulate the virtual velocity of sediment and bed material texture. This study describes general tendencies in the vertical exchange of gravels within the substrate that result from multiple floods. Empirical observations come from Carnation Creek, a small gravel-bed river with large woody debris located on the west coast of Vancouver Island, British Columbia. Frequent floods and the relatively limited armor layer facilitate streambed activity and relatively high bedload transport rates, typically under partial sediment transport conditions. Over 2000 magnetically tagged stones, ranging in size from 16 to 180 mm, were deployed on the bed surface between 1991 and 1992. These tracers have been recovered 10 times over 12 flood seasons to quantify their vertical position in the streambed. For analysis, the bed is divided into layers based on armor layer thickness. Once tracers are well mixed within the streambed, grains in the surface layer are most likely to be mixed into the subsurface, while subsurface grains are most likely to persist within the subsurface. Fractional exchange probabilities approach size independence when the most active depth of the substrate is considered. Overall these results highlight vertical mixing as an important process in the dispersion of gravels.
Topology of spaces of probability measures
NASA Astrophysics Data System (ADS)
Banakh, T. O.; Radul, T. N.
1997-08-01
We study the space \\widehat P(X) of Radon probability measures on a metric space X and its subspaces P_c(X), P_d(X) and P_\\omega (X) of continuous measures, discrete measures, and finitely supported measures, respectively. It is proved that for any completely metrizable space X, the space \\widehat P(X) is homeomorphic to a Hilbert space. A topological classification is obtained for the pairs (\\widehat P(K),\\widehat P(X)), (\\widehat P(K),P_d(Y)) and (\\widehat P(K),P_c(Z)), where K is a metric compactum, X an everywhere dense Borel subset of K, Y an everywhere dense F_{\\sigma \\delta }-set of K, and Z an everywhere uncountable everywhere dense Borel subset of K of sufficiently high Borel class. Conditions on the pair (X,Y) are found that are necessary and sufficient for the pair (\\widehat P(X),P_\\omega (Y)) to be homeomorphic to (l^2(A),l^2_f(A)).
Do aftershock probabilities decay with time?
Michael, Andrew J.
2012-01-01
So, do aftershock probabilities decay with time? Consider a thought experiment in which we are at the time of the mainshock and ask how many aftershocks will occur a day, week, month, year, or even a century from now. First we must decide how large a window to use around each point in time. Let's assume that, as we go further into the future, we are asking a less precise question. Perhaps a day from now means 1 day 10% of a day, a week from now means 1 week 10% of a week, and so on. If we ignore c because it is a small fraction of a day (e.g., Reasenberg and Jones, 1989, hereafter RJ89), and set p = 1 because it is usually close to 1 (its value in the original Omori law), then the rate of earthquakes (K=t) decays at 1=t. If the length of the windows being considered increases proportionally to t, then the number of earthquakes at any time from now is the same because the rate decrease is canceled by the increase in the window duration. Under these conditions we should never think "It's a bit late for this to be an aftershock."
Semi-supervised dimensionality reduction using estimated class membership probabilities
NASA Astrophysics Data System (ADS)
Li, Wei; Ruan, Qiuqi; Wan, Jun
2012-10-01
In solving pattern-recognition tasks with partially labeled training data, the semi-supervised dimensionality reduction method, which considers both labeled and unlabeled data, is preferable for improving the classification and generalization capability of the testing data. Among such techniques, graph-based semi-supervised learning methods have attracted a lot of attention due to their appealing properties in discovering discriminative structure and geometric structure of data points. Although they have achieved remarkable success, they cannot promise good performance when the size of the labeled data set is small, as a result of inaccurate class matrix variance approximated by insufficient labeled training data. In this paper, we tackle this problem by combining class membership probabilities estimated from unlabeled data and ground-truth class information associated with labeled data to more precisely characterize the class distribution. Therefore, it is expected to enhance performance in classification tasks. We refer to this approach as probabilistic semi-supervised discriminant analysis (PSDA). The proposed PSDA is applied to face and facial expression recognition tasks and is evaluated using the ORL, Extended Yale B, and CMU PIE face databases and the Cohn-Kanade facial expression database. The promising experimental results demonstrate the effectiveness of our proposed method.
Dissociating dynamic probability and predictability in observed actions—an fMRI study
Ahlheim, Christiane; Stadler, Waltraud; Schubotz, Ricarda I.
2014-01-01
The present fMRI study investigated whether human observers spontaneously exploit the statistical structure underlying continuous action sequences. In particular, we tested whether two different statistical properties can be distinguished with regard to their neural correlates: an action step's predictability and its probability. To assess these properties we used measures from information theory. Predictability of action steps was operationalized by its inverse, conditional entropy, which combines the number of possible action steps with their respective probabilities. Probability of action steps was assessed using conditional surprisal, which increases with decreasing probability. Participants were trained in an action observation paradigm with video clips showing sequences of 9–33 s length with varying numbers of action steps that were statistically structured according to a Markov chain. Behavioral tests revealed that participants implicitly learned this statistical structure, showing that humans are sensitive toward these probabilistic regularities. Surprisal (lower probability) enhanced the BOLD signal in the anterior intraparietal sulcus. In contrast, high conditional entropy, i.e., low predictability, was correlated with higher activity in dorsomedial prefrontal cortex, orbitofrontal gyrus, and posterior intraparietal sulcus. Furthermore, we found a correlation between the anterior hippocampus' response to conditional entropy with the extent of learning, such that the more participants had learnt the structure, the greater the magnitude of hippocampus activation in response to conditional entropy. Findings show that two aspects of predictions can be dissociated: an action's predictability is reflected in a top-down modulation of attentional focus, evident in increased fronto-parietal activation. In contrast, an action's probability depends on the identity of the stimulus itself, resulting in bottom-up driven processing costs in the parietal cortex. PMID
ERIC Educational Resources Information Center
Missouri Univ., Columbia. Coll. of Education.
Information is provided regarding major learning styles and other factors important to student learning. Several typically asked questions are presented regarding different learning styles (visual, auditory, tactile and kinesthetic, and multisensory learning), associated considerations, determining individuals' learning styles, and appropriate…
Quantum reinforcement learning.
Dong, Daoyi; Chen, Chunlin; Li, Hanxiong; Tarn, Tzyh-Jong
2008-10-01
The key approaches for machine learning, particularly learning in unknown probabilistic environments, are new representations and computation mechanisms. In this paper, a novel quantum reinforcement learning (QRL) method is proposed by combining quantum theory and reinforcement learning (RL). Inspired by the state superposition principle and quantum parallelism, a framework of a value-updating algorithm is introduced. The state (action) in traditional RL is identified as the eigen state (eigen action) in QRL. The state (action) set can be represented with a quantum superposition state, and the eigen state (eigen action) can be obtained by randomly observing the simulated quantum state according to the collapse postulate of quantum measurement. The probability of the eigen action is determined by the probability amplitude, which is updated in parallel according to rewards. Some related characteristics of QRL such as convergence, optimality, and balancing between exploration and exploitation are also analyzed, which shows that this approach makes a good tradeoff between exploration and exploitation using the probability amplitude and can speedup learning through the quantum parallelism. To evaluate the performance and practicability of QRL, several simulated experiments are given, and the results demonstrate the effectiveness and superiority of the QRL algorithm for some complex problems. This paper is also an effective exploration on the application of quantum computation to artificial intelligence.
Projecting Climate Change Impacts on Wildfire Probabilities
NASA Astrophysics Data System (ADS)
Westerling, A. L.; Bryant, B. P.; Preisler, H.
2008-12-01
We present preliminary results of the 2008 Climate Change Impact Assessment for wildfire in California, part of the second biennial science report to the California Climate Action Team organized via the California Climate Change Center by the California Energy Commission's Public Interest Energy Research Program pursuant to Executive Order S-03-05 of Governor Schwarzenegger. In order to support decision making by the State pertaining to mitigation of and adaptation to climate change and its impacts, we model wildfire occurrence monthly from 1950 to 2100 under a range of climate scenarios from the Intergovernmental Panel on Climate Change. We use six climate change models (GFDL CM2.1, NCAR PCM1, CNRM CM3, MPI ECHAM5, MIROC3.2 med, NCAR CCSM3) under two emissions scenarios--A2 (C02 850ppm max atmospheric concentration) and B1(CO2 550ppm max concentration). Climate model output has been downscaled to a 1/8 degree (~12 km) grid using two alternative methods: a Bias Correction and Spatial Donwscaling (BCSD) and a Constructed Analogues (CA) downscaling. Hydrologic variables have been simulated from temperature, precipitation, wind and radiation forcing data using the Variable Infiltration Capacity (VIC) Macroscale Hydrologic Model. We model wildfire as a function of temperature, moisture deficit, and land surface characteristics using nonlinear logistic regression techniques. Previous work on wildfire climatology and seasonal forecasting has demonstrated that these variables account for much of the inter-annual and seasonal variation in wildfire. The results of this study are monthly gridded probabilities of wildfire occurrence by fire size class, and estimates of the number of structures potentially affected by fires. In this presentation we will explore the range of modeled outcomes for wildfire in California, considering the effects of emissions scenarios, climate model sensitivities, downscaling methods, hydrologic simulations, statistical model specifications for
28 CFR 2.101 - Probable cause hearing and determination.
Code of Federal Regulations, 2010 CFR
2010-07-01
... who have given information upon which revocation may be based) at a postponed probable cause hearing... attendance, unless good cause is found for not allowing confrontation. Whenever a probable cause hearing...
Prospect evaluation as a function of numeracy and probability denominator.
Millroth, Philip; Juslin, Peter
2015-05-01
This study examines how numeracy and probability denominator (a direct-ratio probability, a relative frequency with denominator 100, a relative frequency with denominator 10,000) affect the evaluation of prospects in an expected-value based pricing task. We expected that numeracy would affect the results due to differences in the linearity of number perception and the susceptibility to denominator neglect with different probability formats. An analysis with functional measurement verified that participants integrated value and probability into an expected value. However, a significant interaction between numeracy and probability format and subsequent analyses of the parameters of cumulative prospect theory showed that the manipulation of probability denominator changed participants' psychophysical response to probability and value. Standard methods in decision research may thus confound people's genuine risk attitude with their numerical capacities and the probability format used.
On differentiating the probability of error in multipopular feature selection
NASA Technical Reports Server (NTRS)
Peters, B. C.
1974-01-01
A method of linear feature selection for n dimensional observation vectors which belong to one of m populations is presented. Each population has a known apriori probability and is described by a known multivariate normal density function. Specifically we consider the problem of finding a k x n matrix B of rank k (k n) for which the transformed probability of misclassification is minimized. Providing that the transformed a posterior probabilities are distinct theoretical results are obtained which, for the case k = l, give rise to a numerically tractable formula for the derivative of the probability of misclassification. It is shown that for the two population problem this condition is also necessary. The dependence of the minimum probability of error on the a priori probabilities is investigated. The minimum probability of error satisfies a uniform Lipschitz condition with respect to the a priori probabilities.
Deduction and Inference Using Conditional Logic and Probability
1991-05-01
Carnap and R. C. Jeffrey [30] & [31], H. Gaifman [32], D. Scott and P. Kraus [33], E. W. Adams [19], and T. Hailperin [8] have all defined the probability...Theories of Logic and Probabilities, Open Court. 30. Carnap , R. (1960; 1st ed., 1950) Logical Foundations of Probability 2nd. ed., Univ. of Chicago...Press. 31. Carnap , R. & Jeffrey, R. C. (1971) Studies in Inductive Logic and Probability, Univ. of California Press. 32. Gaifman, H. (1964) Concerning
A short note on probability in clinical medicine.
Upshur, Ross E G
2013-06-01
Probability claims are ubiquitous in clinical medicine, yet exactly how clinical events relate to interpretations of probability has been not been well explored. This brief essay examines the major interpretations of probability and how these interpretations may account for the probabilistic nature of clinical events. It is argued that there are significant problems with the unquestioned application of interpretation of probability to clinical events. The essay concludes by suggesting other avenues to understand uncertainty in clinical medicine.
Pretest probability assessment derived from attribute matching
Kline, Jeffrey A; Johnson, Charles L; Pollack, Charles V; Diercks, Deborah B; Hollander, Judd E; Newgard, Craig D; Garvey, J Lee
2005-01-01
Background Pretest probability (PTP) assessment plays a central role in diagnosis. This report compares a novel attribute-matching method to generate a PTP for acute coronary syndrome (ACS). We compare the new method with a validated logistic regression equation (LRE). Methods Eight clinical variables (attributes) were chosen by classification and regression tree analysis of a prospectively collected reference database of 14,796 emergency department (ED) patients evaluated for possible ACS. For attribute matching, a computer program identifies patients within the database who have the exact profile defined by clinician input of the eight attributes. The novel method was compared with the LRE for ability to produce PTP estimation <2% in a validation set of 8,120 patients evaluated for possible ACS and did not have ST segment elevation on ECG. 1,061 patients were excluded prior to validation analysis because of ST-segment elevation (713), missing data (77) or being lost to follow-up (271). Results In the validation set, attribute matching produced 267 unique PTP estimates [median PTP value 6%, 1st–3rd quartile 1–10%] compared with the LRE, which produced 96 unique PTP estimates [median 24%, 1st–3rd quartile 10–30%]. The areas under the receiver operating characteristic curves were 0.74 (95% CI 0.65 to 0.82) for the attribute matching curve and 0.68 (95% CI 0.62 to 0.77) for LRE. The attribute matching system categorized 1,670 (24%, 95% CI = 23–25%) patients as having a PTP < 2.0%; 28 developed ACS (1.7% 95% CI = 1.1–2.4%). The LRE categorized 244 (4%, 95% CI = 3–4%) with PTP < 2.0%; four developed ACS (1.6%, 95% CI = 0.4–4.1%). Conclusion Attribute matching estimated a very low PTP for ACS in a significantly larger proportion of ED patients compared with a validated LRE. PMID:16095534
Representation of Odds in Terms of Frequencies Reduces Probability Discounting
ERIC Educational Resources Information Center
Yi, Richard; Bickel, Warren K.
2005-01-01
In studies of probability discounting, the reduction in the value of an outcome as a result of its degree of uncertainty is calculated. Decision making studies suggest two issues with probability that may play a role in data obtained in probability discounting studies. The first issue involves the reduction of risk aversion via subdivision of…
Probability Constructs in Preschool Education and How they Are Taught
ERIC Educational Resources Information Center
Antonopoulos, Konstantinos; Zacharos, Konstantinos
2013-01-01
The teaching of Probability Theory constitutes a new trend in mathematics education internationally. The purpose of this research project was to explore the degree to which preschoolers understand key concepts of probabilistic thinking, such as sample space, the probability of an event and probability comparisons. At the same time, we evaluated an…
Typical Versus Atypical Unpacking and Superadditive Probability Judgment
ERIC Educational Resources Information Center
Sloman, Steven; Rottenstreich, Yuval; Wisniewski, Edward; Hadjichristidis, Constantinos; Fox, Craig R.
2004-01-01
Probability judgments for packed descriptions of events (e.g., the probability that a businessman does business with a European country) are compared with judgments for unpacked descriptions of the same events (e.g., the probability that a businessman does business with England, France, or some other European country). The prediction that…
Factors influencing reporting and harvest probabilities in North American geese
Zimmerman, G.S.; Moser, T.J.; Kendall, W.L.; Doherty, P.F.; White, Gary C.; Caswell, D.F.
2009-01-01
We assessed variation in reporting probabilities of standard bands among species, populations, harvest locations, and size classes of North American geese to enable estimation of unbiased harvest probabilities. We included reward (US10,20,30,50, or100) and control (0) banded geese from 16 recognized goose populations of 4 species: Canada (Branta canadensis), cackling (B. hutchinsii), Ross's (Chen rossii), and snow geese (C. caerulescens). We incorporated spatially explicit direct recoveries and live recaptures into a multinomial model to estimate reporting, harvest, and band-retention probabilities. We compared various models for estimating harvest probabilities at country (United States vs. Canada), flyway (5 administrative regions), and harvest area (i.e., flyways divided into northern and southern sections) scales. Mean reporting probability of standard bands was 0.73 (95 CI 0.690.77). Point estimates of reporting probabilities for goose populations or spatial units varied from 0.52 to 0.93, but confidence intervals for individual estimates overlapped and model selection indicated that models with species, population, or spatial effects were less parsimonious than those without these effects. Our estimates were similar to recently reported estimates for mallards (Anas platyrhynchos). We provide current harvest probability estimates for these populations using our direct measures of reporting probability, improving the accuracy of previous estimates obtained from recovery probabilities alone. Goose managers and researchers throughout North America can use our reporting probabilities to correct recovery probabilities estimated from standard banding operations for deriving spatially explicit harvest probabilities.
Pig Data and Bayesian Inference on Multinomial Probabilities
ERIC Educational Resources Information Center
Kern, John C.
2006-01-01
Bayesian inference on multinomial probabilities is conducted based on data collected from the game Pass the Pigs[R]. Prior information on these probabilities is readily available from the instruction manual, and is easily incorporated in a Dirichlet prior. Posterior analysis of the scoring probabilities quantifies the discrepancy between empirical…
NASA Astrophysics Data System (ADS)
Marshman, Emily; Singh, Chandralekha
2017-03-01
A solid grasp of the probability distributions for measuring physical observables is central to connecting the quantum formalism to measurements. However, students often struggle with the probability distributions of measurement outcomes for an observable and have difficulty expressing this concept in different representations. Here we first describe the difficulties that upper-level undergraduate and PhD students have with the probability distributions for measuring physical observables in quantum mechanics. We then discuss how student difficulties found in written surveys and individual interviews were used as a guide in the development of a quantum interactive learning tutorial (QuILT) to help students develop a good grasp of the probability distributions of measurement outcomes for physical observables. The QuILT strives to help students become proficient in expressing the probability distributions for the measurement of physical observables in Dirac notation and in the position representation and be able to convert from Dirac notation to position representation and vice versa. We describe the development and evaluation of the QuILT and findings about the effectiveness of the QuILT from in-class evaluations.
Contingency bias in probability judgement may arise from ambiguity regarding additional causes.
Mitchell, Chris J; Griffiths, Oren; More, Pranjal; Lovibond, Peter F
2013-09-01
In laboratory contingency learning tasks, people usually give accurate estimates of the degree of contingency between a cue and an outcome. However, if they are asked to estimate the probability of the outcome in the presence of the cue, they tend to be biased by the probability of the outcome in the absence of the cue. This bias is often attributed to an automatic contingency detection mechanism, which is said to act via an excitatory associative link to activate the outcome representation at the time of testing. We conducted 3 experiments to test alternative accounts of contingency bias. Participants were exposed to the same outcome probability in the presence of the cue, but different outcome probabilities in the absence of the cue. Phrasing the test question in terms of frequency rather than probability and clarifying the test instructions reduced but did not eliminate contingency bias. However, removal of ambiguity regarding the presence of additional causes during the test phase did eliminate contingency bias. We conclude that contingency bias may be due to ambiguity in the test question, and therefore it does not require postulation of a separate associative link-based mechanism.
Generating prior probabilities for classifiers of brain tumours using belief networks
Reynolds, Greg M; Peet, Andrew C; Arvanitis, Theodoros N
2007-01-01
Background Numerous methods for classifying brain tumours based on magnetic resonance spectra and imaging have been presented in the last 15 years. Generally, these methods use supervised machine learning to develop a classifier from a database of cases for which the diagnosis is already known. However, little has been published on developing classifiers based on mixed modalities, e.g. combining imaging information with spectroscopy. In this work a method of generating probabilities of tumour class from anatomical location is presented. Methods The method of "belief networks" is introduced as a means of generating probabilities that a tumour is any given type. The belief networks are constructed using a database of paediatric tumour cases consisting of data collected over five decades; the problems associated with using this data are discussed. To verify the usefulness of the networks, an application of the method is presented in which prior probabilities were generated and combined with a classification of tumours based solely on MRS data. Results Belief networks were constructed from a database of over 1300 cases. These can be used to generate a probability that a tumour is any given type. Networks are presented for astrocytoma grades I and II, astrocytoma grades III and IV, ependymoma, pineoblastoma, primitive neuroectodermal tumour (PNET), germinoma, medulloblastoma, craniopharyngioma and a group representing rare tumours, "other". Using the network to generate prior probabilities for classification improves the accuracy when compared with generating prior probabilities based on class prevalence. Conclusion Bayesian belief networks are a simple way of using discrete clinical information to generate probabilities usable in classification. The belief network method can be robust to incomplete datasets. Inclusion of a priori knowledge is an effective way of improving classification of brain tumours by non-invasive methods. PMID:17877822
Applications of Quantum Probability Theory to Dynamic Decision Making
2015-08-13
quantum learning algorithm for the dynamic environments; and most importantly, (c) To experimentally test whether the quantum reinforcement learning...seeking tasks, which are relevant to Air Force applications. In particular, we developed a new quantum reinforcement learning algorithm for MDP’s. The... quantum reinforcement-learning algorithm does not require a quantum computer, and can be directly used to learn to perform practical sequential
Total probabilities of ensemble runoff forecasts
NASA Astrophysics Data System (ADS)
Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian
2016-04-01
Ensemble forecasting has for a long time been used as a method in meteorological modelling to indicate the uncertainty of the forecasts. However, as the ensembles often exhibit both bias and dispersion errors, it is necessary to calibrate and post-process them. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters which are different in space and time, but still can give a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, and cannot directly be regionalized in the way we would like, so we suggest a different path below. The target of our work is to create a mean forecast with uncertainty bounds for a large number of locations in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu) We are therefore more interested in improving the forecast skill for high-flows rather than the forecast skill of lower runoff levels. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different forecasters to force a distributed hydrologic model and to compute runoff ensembles for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast ensemble individually, we will now post-process all model outputs to find a total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but assuring that they have some spatial correlation, by adding a spatial penalty in the calibration process. This can in some cases have a slight negative
Pattern formation, logistics, and maximum path probability
NASA Astrophysics Data System (ADS)
Kirkaldy, J. S.
1985-05-01
The concept of pattern formation, which to current researchers is a synonym for self-organization, carries the connotation of deductive logic together with the process of spontaneous inference. Defining a pattern as an equivalence relation on a set of thermodynamic objects, we establish that a large class of irreversible pattern-forming systems, evolving along idealized quasisteady paths, approaches the stable steady state as a mapping upon the formal deductive imperatives of a propositional function calculus. In the preamble the classical reversible thermodynamics of composite systems is analyzed as an externally manipulated system of space partitioning and classification based on ideal enclosures and diaphragms. The diaphragms have discrete classification capabilities which are designated in relation to conserved quantities by descriptors such as impervious, diathermal, and adiabatic. Differentiability in the continuum thermodynamic calculus is invoked as equivalent to analyticity and consistency in the underlying class or sentential calculus. The seat of inference, however, rests with the thermodynamicist. In the transition to an irreversible pattern-forming system the defined nature of the composite reservoirs remains, but a given diaphragm is replaced by a pattern-forming system which by its nature is a spontaneously evolving volume partitioner and classifier of invariants. The seat of volition or inference for the classification system is thus transferred from the experimenter or theoretician to the diaphragm, and with it the full deductive facility. The equivalence relations or partitions associated with the emerging patterns may thus be associated with theorems of the natural pattern-forming calculus. The entropy function, together with its derivatives, is the vehicle which relates the logistics of reservoirs and diaphragms to the analog logistics of the continuum. Maximum path probability or second-order differentiability of the entropy in isolation are
Probability Distribution for Flowing Interval Spacing
S. Kuzio
2004-09-22
Fracture spacing is a key hydrologic parameter in analyses of matrix diffusion. Although the individual fractures that transmit flow in the saturated zone (SZ) cannot be identified directly, it is possible to determine the fractured zones that transmit flow from flow meter survey observations. The fractured zones that transmit flow as identified through borehole flow meter surveys have been defined in this report as flowing intervals. The flowing interval spacing is measured between the midpoints of each flowing interval. The determination of flowing interval spacing is important because the flowing interval spacing parameter is a key hydrologic parameter in SZ transport modeling, which impacts the extent of matrix diffusion in the SZ volcanic matrix. The output of this report is input to the ''Saturated Zone Flow and Transport Model Abstraction'' (BSC 2004 [DIRS 170042]). Specifically, the analysis of data and development of a data distribution reported herein is used to develop the uncertainty distribution for the flowing interval spacing parameter for the SZ transport abstraction model. Figure 1-1 shows the relationship of this report to other model reports that also pertain to flow and transport in the SZ. Figure 1-1 also shows the flow of key information among the SZ reports. It should be noted that Figure 1-1 does not contain a complete representation of the data and parameter inputs and outputs of all SZ reports, nor does it show inputs external to this suite of SZ reports. Use of the developed flowing interval spacing probability distribution is subject to the limitations of the assumptions discussed in Sections 5 and 6 of this analysis report. The number of fractures in a flowing interval is not known. Therefore, the flowing intervals are assumed to be composed of one flowing zone in the transport simulations. This analysis may overestimate the flowing interval spacing because the number of fractures that contribute to a flowing interval cannot be
Surprisingly rational: probability theory plus noise explains biases in judgment.
Costello, Fintan; Watts, Paul
2014-07-01
The systematic biases seen in people's probability judgments are typically taken as evidence that people do not use the rules of probability theory when reasoning about probability but instead use heuristics, which sometimes yield reasonable judgments and sometimes yield systematic biases. This view has had a major impact in economics, law, medicine, and other fields; indeed, the idea that people cannot reason with probabilities has become a truism. We present a simple alternative to this view, where people reason about probability according to probability theory but are subject to random variation or noise in the reasoning process. In this account the effect of noise is canceled for some probabilistic expressions. Analyzing data from 2 experiments, we find that, for these expressions, people's probability judgments are strikingly close to those required by probability theory. For other expressions, this account produces systematic deviations in probability estimates. These deviations explain 4 reliable biases in human probabilistic reasoning (conservatism, subadditivity, conjunction, and disjunction fallacies). These results suggest that people's probability judgments embody the rules of probability theory and that biases in those judgments are due to the effects of random noise.
Probability expression for changeable and changeless uncertainties: an implicit test
Wang, Yun; Du, Xue-Lei; Rao, Li-Lin; Li, Shu
2014-01-01
“Everything changes and nothing remains still.”We designed three implicit studies to understand how people react or adapt to a rapidly changing world by testing whether verbal probability is better in expressing changeable uncertainty while numerical probability is better in expressing unchangeable uncertainty. We found that the “verbal-changeable” combination in implicit tasks was more compatible than the “numerical-changeable” combination. Furthermore, the “numerical-changeless” combination was more compatible than the “verbal-changeless” combination. Thus, a novel feature called “changeability” was proposed to describe the changeable nature of verbal probability. However, numerical probability is a better carrier of changeless uncertainty than verbal probability. These results extend the domain of probability predictions and enrich our general understanding of communication with verbal and numerical probabilities. Given that the world around us is constantly changing, this “changeability” feature may play a major role in preparing for uncertainty. PMID:25431566
Partial avoidance contingencies: Absolute omission and punishment probabilities1
Flye, Barbaba L.; Gibbon, John
1979-01-01
Avoidance contingencies were defined by the absolute probability of the conjunction of responding or not responding with shock or no shock. The “omission” probability (ρ00) is the probability of no response and no shock. The “punishment” probability (ρ11) is the probability of both a response and a shock. The traditional avoidance contingency never omits shock on nonresponse trials (ρ00=0) and never presents shock on response trials (ρ11=0). Rats were trained on a discrete-trial paradigm with no intertrial interval. The first lever response changed an auditory stimulus for the remainder of the trial. Shocks were delivered only at the end of each trial cycle. After initial training under the traditional avoidance contingency, one group of rats experienced changes in omission probability (ρ00>0), holding punishment probability at zero. The second group of rats were studied under different punishment probability values (ρ11>0), holding omission probability at zero. Data from subjects in the omission group looked similar, showing graded decrements in responding with increasing probability of omission. These subjects approximately “matched” their nonresponse frequencies to the programmed probability of shock omission on nonresponse trials, producing a very low and approximately constant conditional probability of shock given no response. Subjects in the punishment group showed different sensitivity to increasing absolute punishment probability. Some subjects decreased responding to low values as punishment probability increased, while others continued to respond at substantial levels even when shock was inevitable on all trials (noncontingent shock schedule). These results confirm an asymmetry between two dimensions of partial avoidance contingencies. When the consequences of not responding included occasional omission of shock, all subjects showed graded sensitivity to changes in omission frequency. When the consequences of responding included
Conditional Probabilities for Large Events Estimated by Small Earthquake Rate
NASA Astrophysics Data System (ADS)
Wu, Yi-Hsuan; Chen, Chien-Chih; Li, Hsien-Chi
2016-01-01
We examined forecasting quiescence and activation models to obtain the conditional probability that a large earthquake will occur in a specific time period on different scales in Taiwan. The basic idea of the quiescence and activation models is to use earthquakes that have magnitudes larger than the completeness magnitude to compute the expected properties of large earthquakes. We calculated the probability time series for the whole Taiwan region and for three subareas of Taiwan—the western, eastern, and northeastern Taiwan regions—using 40 years of data from the Central Weather Bureau catalog. In the probability time series for the eastern and northeastern Taiwan regions, a high probability value is usually yielded in cluster events such as events with foreshocks and events that all occur in a short time period. In addition to the time series, we produced probability maps by calculating the conditional probability for every grid point at the time just before a large earthquake. The probability maps show that high probability values are yielded around the epicenter before a large earthquake. The receiver operating characteristic (ROC) curves of the probability maps demonstrate that the probability maps are not random forecasts, but also suggest that lowering the magnitude of a forecasted large earthquake may not improve the forecast method itself. From both the probability time series and probability maps, it can be observed that the probability obtained from the quiescence model increases before a large earthquake and the probability obtained from the activation model increases as the large earthquakes occur. The results lead us to conclude that the quiescence model has better forecast potential than the activation model.
Learning foraging thresholds for lizards
Goldberg, L.A.; Hart, W.E.; Wilson, D.B.
1996-01-12
This work gives a proof of convergence for a randomized learning algorithm that describes how anoles (lizards found in the Carribean) learn a foraging threshold distance. This model assumes that an anole will pursue a prey if and only if it is within this threshold of the anole`s perch. This learning algorithm was proposed by the biologist Roughgarden and his colleagues. They experimentally confirmed that this algorithm quickly converges to the foraging threshold that is predicted by optimal foraging theory our analysis provides an analytic confirmation that the learning algorithm converses to this optimal foraging threshold with high probability.
Li, Shu; Du, Xue-Lei; Li, Qi; Xuan, Yan-Hua; Wang, Yun; Rao, Li-Lin
2016-01-01
Two kinds of probability expressions, verbal and numerical, have been used to characterize the uncertainty that people face. However, the question of whether verbal and numerical probabilities are cognitively processed in a similar manner remains unresolved. From a levels-of-processing perspective, verbal and numerical probabilities may be processed differently during early sensory processing but similarly in later semantic-associated operations. This event-related potential (ERP) study investigated the neural processing of verbal and numerical probabilities in risky choices. The results showed that verbal probability and numerical probability elicited different N1 amplitudes but that verbal and numerical probabilities elicited similar N2 and P3 waveforms in response to different levels of probability (high to low). These results were consistent with a levels-of-processing framework and suggest some internal consistency between the cognitive processing of verbal and numerical probabilities in risky choices. Our findings shed light on possible mechanism underlying probability expression and may provide the neural evidence to support the translation of verbal to numerical probabilities (or vice versa). PMID:26834612
GNSS integer ambiguity validation based on posterior probability
NASA Astrophysics Data System (ADS)
Wu, Zemin; Bian, Shaofeng
2015-10-01
GNSS integer ambiguity validation is considered to be a challenge task for decades. Several kinds of validation tests are developed and widely used in these years, but theoretical basis is their weakness. Ambiguity validation theoretically is an issue of hypothesis test. In the frame of Bayesian hypothesis testing, posterior probability is the canonical standard that statistical decision should be based on. In this contribution, (i) we derive the posterior probability of the fixed ambiguity based on the Bayesian principle and modify it for practice ambiguity validation. (ii) The optimal property of the posterior probability test is proved based on an extended Neyman-Pearson lemma. Since validation failure rate is the issue users most concerned about, (iii) we derive the failure rate upper bound of the posterior probability test, so the user can use the posterior probability test either in the fixed posterior probability or in the fixed failure rate way. Simulated as well as real observed data are used for experimental validations. The results show that (i) the posterior probability test is the most effective within the R-ratio test, difference test, ellipsoidal integer aperture test and posterior probability test, (ii) the posterior probability test is computational efficient and (iii) the failure rate estimation for posterior probability test is useful.
The Effect of Conditional Probability of Chord Progression on Brain Response: An MEG Study
Kim, Seung-Goo; Kim, June Sic; Chung, Chun Kee
2011-01-01
Background Recent electrophysiological and neuroimaging studies have explored how and where musical syntax in Western music is processed in the human brain. An inappropriate chord progression elicits an event-related potential (ERP) component called an early right anterior negativity (ERAN) or simply an early anterior negativity (EAN) in an early stage of processing the musical syntax. Though the possible underlying mechanism of the EAN is assumed to be probabilistic learning, the effect of the probability of chord progressions on the EAN response has not been previously explored explicitly. Methodology/Principal Findings In the present study, the empirical conditional probabilities in a Western music corpus were employed as an approximation of the frequencies in previous exposure of participants. Three types of chord progression were presented to musicians and non-musicians in order to examine the correlation between the probability of chord progression and the neuromagnetic response using magnetoencephalography (MEG). Chord progressions were found to elicit early responses in a negatively correlating fashion with the conditional probability. Observed EANm (as a magnetic counterpart of the EAN component) responses were consistent with the previously reported EAN responses in terms of latency and location. The effect of conditional probability interacted with the effect of musical training. In addition, the neural response also correlated with the behavioral measures in the non-musicians. Conclusions/Significance Our study is the first to reveal the correlation between the probability of chord progression and the corresponding neuromagnetic response. The current results suggest that the physiological response is a reflection of the probabilistic representations of the musical syntax. Moreover, the results indicate that the probabilistic representation is related to the musical training as well as the sensitivity of an individual. PMID:21364895
Exponential convergence rates for weighted sums in noncommutative probability space
NASA Astrophysics Data System (ADS)
Choi, Byoung Jin; Ji, Un Cig
2016-11-01
We study exponential convergence rates for weighted sums of successive independent random variables in a noncommutative probability space of which the weights are in a von Neumann algebra. Then we prove a noncommutative extension of the result for the exponential convergence rate by Baum, Katz and Read. As applications, we first study a large deviation type inequality for weighted sums in a noncommutative probability space, and secondly we study exponential convergence rates for weighted free additive convolution sums of probability measures.
Posterior Probability Matching and Human Perceptual Decision Making.
Murray, Richard F; Patel, Khushbu; Yee, Alan
2015-06-01
Probability matching is a classic theory of decision making that was first developed in models of cognition. Posterior probability matching, a variant in which observers match their response probabilities to the posterior probability of each response being correct, is being used increasingly often in models of perception. However, little is known about whether posterior probability matching is consistent with the vast literature on vision and hearing that has developed within signal detection theory. Here we test posterior probability matching models using two tools from detection theory. First, we examine the models' performance in a two-pass experiment, where each block of trials is presented twice, and we measure the proportion of times that the model gives the same response twice to repeated stimuli. We show that at low performance levels, posterior probability matching models give highly inconsistent responses across repeated presentations of identical trials. We find that practised human observers are more consistent across repeated trials than these models predict, and we find some evidence that less practised observers more consistent as well. Second, we compare the performance of posterior probability matching models on a discrimination task to the performance of a theoretical ideal observer that achieves the best possible performance. We find that posterior probability matching is very inefficient at low-to-moderate performance levels, and that human observers can be more efficient than is ever possible according to posterior probability matching models. These findings support classic signal detection models, and rule out a broad class of posterior probability matching models for expert performance on perceptual tasks that range in complexity from contrast discrimination to symmetry detection. However, our findings leave open the possibility that inexperienced observers may show posterior probability matching behaviour, and our methods provide new tools
More Diagnosis of Solar Flare Probability from Chromosphere Image Sequences
2012-09-28
AFRL-RV-PS- AFRL-RV-PS- TR-2012-0194 TR-2012-0194 MORE DIAGNOSIS OF SOLAR FLARE PROBABILITY FROM CHROMOSPHERE IMAGE...1 Oct 2011 to 07 Sep 2012 4. TITLE AND SUBTITLE More Diagnosis of Solar Flare Probability from Chromosphere Image Sequences 5a...We continued our investigation of the utility of optical observations of the solar chromosphere in the diagnosis of flare probability. Because we felt
Posterior Probability Matching and Human Perceptual Decision Making
Murray, Richard F.; Patel, Khushbu; Yee, Alan
2015-01-01
Probability matching is a classic theory of decision making that was first developed in models of cognition. Posterior probability matching, a variant in which observers match their response probabilities to the posterior probability of each response being correct, is being used increasingly often in models of perception. However, little is known about whether posterior probability matching is consistent with the vast literature on vision and hearing that has developed within signal detection theory. Here we test posterior probability matching models using two tools from detection theory. First, we examine the models’ performance in a two-pass experiment, where each block of trials is presented twice, and we measure the proportion of times that the model gives the same response twice to repeated stimuli. We show that at low performance levels, posterior probability matching models give highly inconsistent responses across repeated presentations of identical trials. We find that practised human observers are more consistent across repeated trials than these models predict, and we find some evidence that less practised observers more consistent as well. Second, we compare the performance of posterior probability matching models on a discrimination task to the performance of a theoretical ideal observer that achieves the best possible performance. We find that posterior probability matching is very inefficient at low-to-moderate performance levels, and that human observers can be more efficient than is ever possible according to posterior probability matching models. These findings support classic signal detection models, and rule out a broad class of posterior probability matching models for expert performance on perceptual tasks that range in complexity from contrast discrimination to symmetry detection. However, our findings leave open the possibility that inexperienced observers may show posterior probability matching behaviour, and our methods provide new tools
Path probability of stochastic motion: A functional approach
NASA Astrophysics Data System (ADS)
Hattori, Masayuki; Abe, Sumiyoshi
2016-06-01
The path probability of a particle undergoing stochastic motion is studied by the use of functional technique, and the general formula is derived for the path probability distribution functional. The probability of finding paths inside a tube/band, the center of which is stipulated by a given path, is analytically evaluated in a way analogous to continuous measurements in quantum mechanics. Then, the formalism developed here is applied to the stochastic dynamics of stock price in finance.
New probability table treatment in MCNP for unresolved resonances
Carter, L.L.; Little, R.C.; Hendricks, J.S.; MacFarlane, R.E.
1998-04-01
An upgrade for MCNP has been implemented to sample the neutron cross sections in the unresolved resonance range using probability tables. These probability tables are generated with the cross section processor code NJOY, by using the evaluated statistical information about the resonances to calculate cumulative probability distribution functions for the microscopic total cross section. The elastic, fission, and radiative capture cross sections are also tabulated as the average values of each of these partials conditional upon the value of the total. This paper summarizes how the probability tables are utilized in this MCNP upgrade and compares this treatment with the approximate smooth treatment for some example problems.
The relationship among probability of failure, landslide susceptibility and rainfall
NASA Astrophysics Data System (ADS)
Huang, Chuen Ming; Lee, Chyi-Tyi
2016-04-01
Landslide hazard included spatial probability, temporal probability and size probability. Many researches evaluate spatial probability in landslide susceptibility, but it is not many in temporal probability and size probability. Because of it must own enough landslide inventories that covered entire study area and large time range. In seismology, using Poisson model to calculate temporal probability is a well-known inference. However, it required a long term and complete records to analyze. In Taiwan, the remote sensing technology made us to establish multi landslide inventories easily, but it is still lack in time series. Thus the landslide susceptibility through changed different return period triggering factor was often assumed landslide hazard. Compare with landslide inventory, collected a long tern rainfall gauge records is easy. However, landslide susceptibility is a relative spatial probability. No matter using different event or analyzing in different area, the landslide susceptibility is not equal. So which model is representative that is difficult to be decided. This study adopted histogram matching to construct basic landslide susceptibility of the region. Then the relationship between landslide susceptibility, probability of failure and rainfall in multi-event can be found out.
Forward and backward location probabilities for sorbing solutes in groundwater
NASA Astrophysics Data System (ADS)
Neupauer, R. M.; Wilson, J. L.
Location probability can be used to describe the likely position of a solute particle as it travels through an aquifer. Forward location probability describes the likely future positions of a particle, and can be used to predict the movement of a contaminant plume. Backward location probability describes the likely prior positions of a solute particle, and can be used to identify sources of contamination. For sorbing solutes, the probability distributions must also account for the phase (aqueous or sorbed) of a solute particle. We present new phase-dependent forward and backward location probabilities to describe transport of a solute undergoing linear non-equilibrium sorption. The effects of sorption are incorporated directly into the governing equations that are used to calculate the probability distributions. The shape and magnitude of the distributions depend on the phase of the contamination at both the source (or prior location) and receptor (or future location). These probabilities are related to adjoint states of concentration. Using adjoint theory, Bayes' theorem, and a clever transformation, we develop a model to efficiently calculate backward location probabilities for one or a few receptors. We illustrate important features of backward location probabilities for a sorbing solute with a hypothetical, one-dimensional confined aquifer, and we demonstrate their use in identifying sources of contamination for a trichloroethylene plume at the Massachusetts Military Reservation using a three-dimensional numerical model.
On the Role of Prior Probability in Adiabatic Quantum Algorithms
NASA Astrophysics Data System (ADS)
Sun, Jie; Lu, Songfeng; Yang, Liping
2016-03-01
In this paper, we study the role of prior probability on the efficiency of quantum local adiabatic search algorithm. The following aspects for prior probability are found here: firstly, only the probabilities of marked states affect the running time of the adiabatic evolution; secondly, the prior probability can be used for improving the efficiency of the adiabatic algorithm; thirdly, like the usual quantum adiabatic evolution, the running time for the case of multiple solution states where the number of marked elements are smaller enough than the size of the set assigned that contains them can be significantly bigger than that of the case where the assigned set only contains all the marked states.
Anytime synthetic projection: Maximizing the probability of goal satisfaction
NASA Technical Reports Server (NTRS)
Drummond, Mark; Bresina, John L.
1990-01-01
A projection algorithm is presented for incremental control rule synthesis. The algorithm synthesizes an initial set of goal achieving control rules using a combination of situation probability and estimated remaining work as a search heuristic. This set of control rules has a certain probability of satisfying the given goal. The probability is incrementally increased by synthesizing additional control rules to handle 'error' situations the execution system is likely to encounter when following the initial control rules. By using situation probabilities, the algorithm achieves a computationally effective balance between the limited robustness of triangle tables and the absolute robustness of universal plans.
Measuring local context as context-word probabilities.
Hahn, Lance W
2012-06-01
Context enables readers to quickly recognize a related word but disturbs recognition of unrelated words. The relatedness of a final word to a sentence context has been estimated as the probability (cloze probability) that a participant will complete a sentence with a word. In four studies, I show that it is possible to estimate local context-word relatedness based on common language usage. Conditional probabilities were calculated for sentences with published cloze probabilities. Four-word contexts produced conditional probabilities significantly correlated with cloze probabilities, but usage statistics were unavailable for some sentence contexts. The present studies demonstrate that a composite context measure based on conditional probabilities for one- to four-word contexts and the presence of a final period represents all of the sentences and maintains significant correlations (.25, .52, .53) with cloze probabilities. Finally, the article provides evidence for the effectiveness of this measure by showing that local context varies in ways that are similar to the N400 effect and that are consistent with a role for local context in reading. The Supplemental materials include local context measures for three cloze probability data sets.
Oscillations in probability distributions for stochastic gene expression
Petrosyan, K. G. Hu, Chin-Kun
2014-05-28
The phenomenon of oscillations in probability distribution functions of number of components is found for a model of stochastic gene expression. It takes place in cases of low levels of molecules or strong intracellular noise. The oscillations distinguish between more probable even and less probable odd number of particles. The even-odd symmetry restores as the number of molecules increases with the probability distribution function tending to Poisson distribution. We discuss the possibility of observation of the phenomenon in gene, protein, and mRNA expression experiments.
Probabilistic Cloning of Three Real States with Optimal Success Probabilities
NASA Astrophysics Data System (ADS)
Rui, Pin-shu
2017-03-01
We investigate the probabilistic quantum cloning (PQC) of three real states with average probability distribution. To get the analytic forms of the optimal success probabilities we assume that the three states have only two pairwise inner products. Based on the optimal success probabilities, we derive the explicit form of 1 →2 PQC for cloning three real states. The unitary operation needed in the PQC process is worked out too. The optimal success probabilities are also generalized to the M→N PQC case.
Ordering genes: controlling the decision-error probabilities.
Rogatko, A; Zacks, S
1993-01-01
Determination of the relative gene order on chromosomes is of critical importance in the construction of human gene maps. In this paper we develop a sequential algorithm for gene ordering. We start by comparing three sequential procedures to order three genes on the basis of Bayesian posterior probabilities, maximum-likelihood ratio, and minimal recombinant class. In the second part of the paper we extend sequential procedure based on the posterior probabilities to the general case of g genes. We present a theorem that states that the predicted average probability of committing a decision error, associated with a Bayesian sequential procedure that accepts the hypothesis of a gene-order configuration with posterior probability equal to or greater than pi *, is smaller than 1 - pi *. This theorem holds irrespective of the number of genes, the genetic model, and the source of genetic information. The theorem is an extension of a classical result of Wald, concerning the sum of the actual and the nominal error probabilities in the sequential probability ratio test of two hypotheses. A stepwise strategy for ordering a large number of genes, with control over the decision-error probabilities, is discussed. An asymptotic approximation is provided, which facilitates the calculations with existing computer software for gene mapping, of the posterior probabilities of an order and the error probabilities. We illustrate with some simulations that the stepwise ordering is an efficient procedure. PMID:8488844
ERIC Educational Resources Information Center
Siegler, Robert S.
2004-01-01
The field of children's learning was thriving when the Merrill-Palmer Quarterly was launched; the field later went into eclipse and now is in the midst of a resurgence. This commentary examines reasons for these trends, and describes the emerging field of children's learning. In particular, the new field is seen as differing from the old in its…
Online Pairwise Learning Algorithms.
Ying, Yiming; Zhou, Ding-Xuan
2016-04-01
Pairwise learning usually refers to a learning task that involves a loss function depending on pairs of examples, among which the most notable ones are bipartite ranking, metric learning, and AUC maximization. In this letter we study an online algorithm for pairwise learning with a least-square loss function in an unconstrained setting of a reproducing kernel Hilbert space (RKHS) that we refer to as the Online Pairwise lEaRning Algorithm (OPERA). In contrast to existing works (Kar, Sriperumbudur, Jain, & Karnick, 2013 ; Wang, Khardon, Pechyony, & Jones, 2012 ), which require that the iterates are restricted to a bounded domain or the loss function is strongly convex, OPERA is associated with a non-strongly convex objective function and learns the target function in an unconstrained RKHS. Specifically, we establish a general theorem that guarantees the almost sure convergence for the last iterate of OPERA without any assumptions on the underlying distribution. Explicit convergence rates are derived under the condition of polynomially decaying step sizes. We also establish an interesting property for a family of widely used kernels in the setting of pairwise learning and illustrate the convergence results using such kernels. Our methodology mainly depends on the characterization of RKHSs using its associated integral operators and probability inequalities for random variables with values in a Hilbert space.
Probability Quantization for Multiplication-Free Binary Arithmetic Coding
NASA Technical Reports Server (NTRS)
Cheung, K. -M.
1995-01-01
A method has been developed to improve on Witten's binary arithmetic coding procedure of tracking a high value and a low value. The new method approximates the probability of the less probable symbol, which improves the worst-case coding efficiency.
Extensional versus Intuitive Reasoning: The Conjunction Fallacy in Probability Judgment.
ERIC Educational Resources Information Center
Tversky, Amos; Kahneman, Daniel
1983-01-01
Judgments under uncertainty are often mediated by intuitive heuristics that are not bound by the conjunction rule of probability. Representativeness and availability heuristics can make a conjunction appear more probable than one of its constituents. Alternative interpretations of this conjunction fallacy are discussed and attempts to combat it…
A New Way to Evaluate the Probability and Fresnel Integrals
ERIC Educational Resources Information Center
Khalili, Parviz
2007-01-01
In this article, we show how "Laplace Transform" may be used to evaluate variety of nontrivial improper integrals, including "Probability" and "Fresnel" integrals. The algorithm we have developed here to evaluate "Probability, Fresnel" and other similar integrals seems to be new. This method transforms the evaluation of certain improper integrals…
Misconceptions in Rational Numbers, Probability, Algebra, and Geometry
ERIC Educational Resources Information Center
Rakes, Christopher R.
2010-01-01
In this study, the author examined the relationship of probability misconceptions to algebra, geometry, and rational number misconceptions and investigated the potential of probability instruction as an intervention to address misconceptions in all 4 content areas. Through a review of literature, 5 fundamental concepts were identified that, if…
Probability Theory, Not the Very Guide of Life
ERIC Educational Resources Information Center
Juslin, Peter; Nilsson, Hakan; Winman, Anders
2009-01-01
Probability theory has long been taken as the self-evident norm against which to evaluate inductive reasoning, and classical demonstrations of violations of this norm include the conjunction error and base-rate neglect. Many of these phenomena require multiplicative probability integration, whereas people seem more inclined to linear additive…
21 CFR 1316.10 - Administrative probable cause.
Code of Federal Regulations, 2010 CFR
2010-04-01
... 21 Food and Drugs 9 2010-04-01 2010-04-01 false Administrative probable cause. 1316.10 Section 1316.10 Food and Drugs DRUG ENFORCEMENT ADMINISTRATION, DEPARTMENT OF JUSTICE ADMINISTRATIVE FUNCTIONS, PRACTICES, AND PROCEDURES Administrative Inspections § 1316.10 Administrative probable cause. If the...
Fisher classifier and its probability of error estimation
NASA Technical Reports Server (NTRS)
Chittineni, C. B.
1979-01-01
Computationally efficient expressions are derived for estimating the probability of error using the leave-one-out method. The optimal threshold for the classification of patterns projected onto Fisher's direction is derived. A simple generalization of the Fisher classifier to multiple classes is presented. Computational expressions are developed for estimating the probability of error of the multiclass Fisher classifier.
The Probability Approach to English If-Conditional Sentences
ERIC Educational Resources Information Center
Wu, Mei
2012-01-01
Users of the Probability Approach choose the right one from four basic types of conditional sentences--factual, predictive, hypothetical and counterfactual conditionals, by judging how likely (i.e. the probability) the event in the result-clause will take place when the condition in the if-clause is met. Thirty-three students from the experimental…
Probability Prediction and Classification. Research Report. RR-04-19
ERIC Educational Resources Information Center
Haberman, Shelby J.
2004-01-01
Criteria for prediction of multinomial responses are examined in terms of estimation bias. Logarithmic penalty and least squares are quite similar in behavior but quite different from maximum probability. The differences ultimately reflect deficiencies in the behavior of the criterion of maximum probability.
Stochastic inequality probabilities for adaptively randomized clinical trials.
Cook, John D; Nadarajah, Saralees
2006-06-01
We examine stochastic inequality probabilities of the form P (X > Y) and P (X > max (Y, Z)) where X, Y, and Z are random variables with beta, gamma, or inverse gamma distributions. We discuss the applications of such inequality probabilities to adaptively randomized clinical trials as well as methods for calculating their values.
Teaching Basic Probability in Undergraduate Statistics or Management Science Courses
ERIC Educational Resources Information Center
Naidu, Jaideep T.; Sanford, John F.
2017-01-01
Standard textbooks in core Statistics and Management Science classes present various examples to introduce basic probability concepts to undergraduate business students. These include tossing of a coin, throwing a die, and examples of that nature. While these are good examples to introduce basic probability, we use improvised versions of Russian…
A Quantum Theoretical Explanation for Probability Judgment Errors
ERIC Educational Resources Information Center
Busemeyer, Jerome R.; Pothos, Emmanuel M.; Franco, Riccardo; Trueblood, Jennifer S.
2011-01-01
A quantum probability model is introduced and used to explain human probability judgment errors including the conjunction and disjunction fallacies, averaging effects, unpacking effects, and order effects on inference. On the one hand, quantum theory is similar to other categorization and memory models of cognition in that it relies on vector…
A new method for estimating extreme rainfall probabilities
Harper, G.A.; O'Hara, T.F. ); Morris, D.I. )
1994-02-01
As part of an EPRI-funded research program, the Yankee Atomic Electric Company developed a new method for estimating probabilities of extreme rainfall. It can be used, along with other techniques, to improve the estimation of probable maximum precipitation values for specific basins or regions.
Effectiveness of Incorporating Adversary Probability Perception Modeling in Security Games
2015-01-30
security game (SSG) algorithms. Given recent work on human decision-making, we adjust the existing subjective utility function to account for...data from previous security game experiments with human subjects. Our results show the incorporation of probability perceptions into the SUQR can...provide improvements in the ability to predict probabilities of attack in certain games .
On the Provenance of Judgments of Conditional Probability
ERIC Educational Resources Information Center
Zhao, Jiaying; Shah, Anuj; Osherson, Daniel
2009-01-01
In standard treatments of probability, Pr(A[vertical bar]B) is defined as the ratio of Pr(A[intersection]B) to Pr(B), provided that Pr(B) greater than 0. This account of conditional probability suggests a psychological question, namely, whether estimates of Pr(A[vertical bar]B) arise in the mind via implicit calculation of…
The Influence of Phonotactic Probability on Word Recognition in Toddlers
ERIC Educational Resources Information Center
MacRoy-Higgins, Michelle; Shafer, Valerie L.; Schwartz, Richard G.; Marton, Klara
2014-01-01
This study examined the influence of phonotactic probability on word recognition in English-speaking toddlers. Typically developing toddlers completed a preferential looking paradigm using familiar words, which consisted of either high or low phonotactic probability sound sequences. The participants' looking behavior was recorded in response to…
14 CFR 417.224 - Probability of failure analysis.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 14 Aeronautics and Space 4 2010-01-01 2010-01-01 false Probability of failure analysis. 417.224..., DEPARTMENT OF TRANSPORTATION LICENSING LAUNCH SAFETY Flight Safety Analysis § 417.224 Probability of failure analysis. (a) General. All flight safety analyses for a launch, regardless of hazard or phase of...
How Can Histograms Be Useful for Introducing Continuous Probability Distributions?
ERIC Educational Resources Information Center
Derouet, Charlotte; Parzysz, Bernard
2016-01-01
The teaching of probability has changed a great deal since the end of the last century. The development of technologies is indeed part of this evolution. In France, continuous probability distributions began to be studied in 2002 by scientific 12th graders, but this subject was marginal and appeared only as an application of integral calculus.…
A Probability Model of Accuracy in Deception Detection Experiments.
ERIC Educational Resources Information Center
Park, Hee Sun; Levine, Timothy R.
2001-01-01
Extends the recent work on the veracity effect in deception detection. Explains the probabilistic nature of a receiver's accuracy in detecting deception and analyzes a receiver's detection of deception in terms of set theory and conditional probability. Finds that accuracy is shown to be a function of the relevant conditional probability and the…
Prizes in Cereal Boxes: An Application of Probability.
ERIC Educational Resources Information Center
Litwiller, Bonnie H.; Duncan, David R.
1992-01-01
Presents four cases of real-world probabilistic situations to promote more effective teaching of probability. Calculates the probability of obtaining six of six different prizes successively in six, seven, eight, and nine boxes of cereal, generalizes the problem to n boxes of cereal, and offers suggestions to extend the problem. (MDH)
Probabilities of Natural Events Occurring at Savannah River Plant
Huang, J.C.
2001-07-17
This report documents the comprehensive evaluation of probability models of natural events which are applicable to Savannah River Plant. The probability curves selected for these natural events are recommended to be used by all SRP/SRL safety analysts. This will ensure a consistency in analysis methodology for postulated SAR incidents involving natural phenomena.
A discussion on the origin of quantum probabilities
Holik, Federico; Sáenz, Manuel; Plastino, Angel
2014-01-15
We study the origin of quantum probabilities as arising from non-Boolean propositional-operational structures. We apply the method developed by Cox to non distributive lattices and develop an alternative formulation of non-Kolmogorovian probability measures for quantum mechanics. By generalizing the method presented in previous works, we outline a general framework for the deduction of probabilities in general propositional structures represented by lattices (including the non-distributive case). -- Highlights: •Several recent works use a derivation similar to that of R.T. Cox to obtain quantum probabilities. •We apply Cox’s method to the lattice of subspaces of the Hilbert space. •We obtain a derivation of quantum probabilities which includes mixed states. •The method presented in this work is susceptible to generalization. •It includes quantum mechanics and classical mechanics as particular cases.
Oil spill contamination probability in the southeastern Levantine basin.
Goldman, Ron; Biton, Eli; Brokovich, Eran; Kark, Salit; Levin, Noam
2015-02-15
Recent gas discoveries in the eastern Mediterranean Sea led to multiple operations with substantial economic interest, and with them there is a risk of oil spills and their potential environmental impacts. To examine the potential spatial distribution of this threat, we created seasonal maps of the probability of oil spill pollution reaching an area in the Israeli coastal and exclusive economic zones, given knowledge of its initial sources. We performed simulations of virtual oil spills using realistic atmospheric and oceanic conditions. The resulting maps show dominance of the alongshore northerly current, which causes the high probability areas to be stretched parallel to the coast, increasing contamination probability downstream of source points. The seasonal westerly wind forcing determines how wide the high probability areas are, and may also restrict these to a small coastal region near source points. Seasonal variability in probability distribution, oil state, and pollution time is also discussed.
Probability theory, not the very guide of life.
Juslin, Peter; Nilsson, Håkan; Winman, Anders
2009-10-01
Probability theory has long been taken as the self-evident norm against which to evaluate inductive reasoning, and classical demonstrations of violations of this norm include the conjunction error and base-rate neglect. Many of these phenomena require multiplicative probability integration, whereas people seem more inclined to linear additive integration, in part, at least, because of well-known capacity constraints on controlled thought. In this article, the authors show with computer simulations that when based on approximate knowledge of probabilities, as is routinely the case in natural environments, linear additive integration can yield as accurate estimates, and as good average decision returns, as estimates based on probability theory. It is proposed that in natural environments people have little opportunity or incentive to induce the normative rules of probability theory and, given their cognitive constraints, linear additive integration may often offer superior bounded rationality.
Probability in the Many-Worlds Interpretation of Quantum Mechanics
NASA Astrophysics Data System (ADS)
Vaidman, Lev
It is argued that, although in the Many-Worlds Interpretation of quantum mechanics there is no "probability" for an outcome of a quantum experiment in the usual sense, we can understand why we have an illusion of probability. The explanation involves: (a) A "sleeping pill" gedanken experiment which makes correspondence between an illegitimate question: "What is the probability of an outcome of a quantum measurement?" with a legitimate question: "What is the probability that `I' am in the world corresponding to that outcome?"; (b) A gedanken experiment which splits the world into several worlds which are identical according to some symmetry condition; and (c) Relativistic causality, which together with (b) explain the Born rule of standard quantum mechanics. The Quantum Sleeping Beauty controversy and "caring measure" replacing probability measure are discussed.
ERIC Educational Resources Information Center
Estes, Katharine Graf; Bowen, Sara
2013-01-01
This research investigates how early learning about native language sound structure affects how infants associate sounds with meanings during word learning. Infants (19-month-olds) were presented with bisyllabic labels with high or low phonotactic probability (i.e., sequences of frequent or infrequent phonemes in English). The labels were produced…
Reconstruction of a Collaborative Mathematical Learning Process
ERIC Educational Resources Information Center
Pijls, Monique; Dekker, Rijkje; Van Hout-Wolters, Bernadette
2007-01-01
The study focused on the interaction between two secondary school students while they were working on computerized mathematical investigation tasks related to probability theory. The aim was to establish how such interaction helped the students to learn from one another, and how it may have hindered their learning process. The assumption was that…
Individual Values, Learning Routines and Academic Procrastination
ERIC Educational Resources Information Center
Dietz, Franziska; Hofer, Manfred; Fries, Stefan
2007-01-01
Background: Academic procrastination, the tendency to postpone learning activities, is regarded as a consequence of postmodern values that are prominent in post-industrialized societies. When students strive for leisure goals and have no structured routines for academic tasks, delaying strenuous learning activities becomes probable. Aims: The…
2007-12-01
Implementing Risk Management on Software Intensive Projects. IEEE Software, 14(3):83-89. Fairley , R . (1994). Risk Management for Software Projects...conditional probability and the Bayesian effect is preceded by an introduction to some basic concepts of probability. Though this discussion draws from R ...Engineering Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA. Charette, R . N. (1991). The Risks with Risk Analysis
ERIC Educational Resources Information Center
Maher, Nicole; Muir, Tracey
2014-01-01
This paper reports on one aspect of a wider study that investigated a selection of final year pre-service primary teachers' responses to four probability tasks. The tasks focused on foundational ideas of probability including sample space, independence, variation and expectation. Responses suggested that strongly held intuitions appeared to…
The quantitative estimation of IT-related risk probabilities.
Herrmann, Andrea
2013-08-01
How well can people estimate IT-related risk? Although estimating risk is a fundamental activity in software management and risk is the basis for many decisions, little is known about how well IT-related risk can be estimated at all. Therefore, we executed a risk estimation experiment with 36 participants. They estimated the probabilities of IT-related risks and we investigated the effect of the following factors on the quality of the risk estimation: the estimator's age, work experience in computing, (self-reported) safety awareness and previous experience with this risk, the absolute value of the risk's probability, and the effect of knowing the estimates of the other participants (see: Delphi method). Our main findings are: risk probabilities are difficult to estimate. Younger and inexperienced estimators were not significantly worse than older and more experienced estimators, but the older and more experienced subjects better used the knowledge gained by knowing the other estimators' results. Persons with higher safety awareness tend to overestimate risk probabilities, but can better estimate ordinal ranks of risk probabilities. Previous own experience with a risk leads to an overestimation of its probability (unlike in other fields like medicine or disasters, where experience with a disease leads to more realistic probability estimates and nonexperience to an underestimation).
Electrofishing capture probability of smallmouth bass in streams
Dauwalter, D.C.; Fisher, W.L.
2007-01-01
Abundance estimation is an integral part of understanding the ecology and advancing the management of fish populations and communities. Mark-recapture and removal methods are commonly used to estimate the abundance of stream fishes. Alternatively, abundance can be estimated by dividing the number of individuals sampled by the probability of capture. We conducted a mark-recapture study and used multiple repeated-measures logistic regression to determine the influence of fish size, sampling procedures, and stream habitat variables on the cumulative capture probability for smallmouth bass Micropterus dolomieu in two eastern Oklahoma streams. The predicted capture probability was used to adjust the number of individuals sampled to obtain abundance estimates. The observed capture probabilities were higher for larger fish and decreased with successive electrofishing passes for larger fish only. Model selection suggested that the number of electrofishing passes, fish length, and mean thalweg depth affected capture probabilities the most; there was little evidence for any effect of electrofishing power density and woody debris density on capture probability. Leave-one-out cross validation showed that the cumulative capture probability model predicts smallmouth abundance accurately. ?? Copyright by the American Fisheries Society 2007.
Fixation of strategies driven by switching probabilities in evolutionary games
NASA Astrophysics Data System (ADS)
Xu, Zimin; Zhang, Jianlei; Zhang, Chunyan; Chen, Zengqiang
2016-12-01
We study the evolutionary dynamics of strategies in finite populations which are homogeneous and well mixed by means of the pairwise comparison process, the core of which is the proposed switching probability. Previous studies about this subject are usually based on the known payoff comparison of the related players, which is an ideal assumption. In real social systems, acquiring the accurate payoffs of partners at each round of interaction may be not easy. So we bypass the need of explicit knowledge of payoffs, and encode the payoffs into the willingness of any individual shift from her current strategy to the competing one, and the switching probabilities are wholly independent of payoffs. Along this way, the strategy updating can be performed when game models are fixed and payoffs are unclear, expected to extend ideal assumptions to be more realistic one. We explore the impact of the switching probability on the fixation probability and derive a simple formula which determines the fixation probability. Moreover we find that cooperation dominates defection if the probability of cooperation replacing defection is always larger than the probability of defection replacing cooperation in finite populations. Last, we investigate the influences of model parameters on the fixation of strategies in the framework of three concrete game models: prisoner's dilemma, snowdrift game and stag-hunt game, which effectively portray the characteristics of cooperative dilemmas in real social systems.
Dopamine D₁ receptors and nonlinear probability weighting in risky choice.
Takahashi, Hidehiko; Matsui, Hiroshi; Camerer, Colin; Takano, Harumasa; Kodaka, Fumitoshi; Ideno, Takashi; Okubo, Shigetaka; Takemura, Kazuhisa; Arakawa, Ryosuke; Eguchi, Yoko; Murai, Toshiya; Okubo, Yoshiro; Kato, Motoichiro; Ito, Hiroshi; Suhara, Tetsuya
2010-12-08
Misestimating risk could lead to disadvantaged choices such as initiation of drug use (or gambling) and transition to regular drug use (or gambling). Although the normative theory in decision-making under risks assumes that people typically take the probability-weighted expectation over possible utilities, experimental studies of choices among risks suggest that outcome probabilities are transformed nonlinearly into subjective decision weights by a nonlinear weighting function that overweights low probabilities and underweights high probabilities. Recent studies have revealed the neurocognitive mechanism of decision-making under risk. However, the role of modulatory neurotransmission in this process remains unclear. Using positron emission tomography, we directly investigated whether dopamine D₁ and D₂ receptors in the brain are associated with transformation of probabilities into decision weights in healthy volunteers. The binding of striatal D₁ receptors is negatively correlated with the degree of nonlinearity of weighting function. Individuals with lower striatal D₁ receptor density showed more pronounced overestimation of low probabilities and underestimation of high probabilities. This finding should contribute to a better understanding of the molecular mechanism of risky choice, and extreme or impaired decision-making observed in drug and gambling addiction.
Off-site ignition probability of flammable gases.
Rew, P J; Spencer, H; Daycock, J
2000-01-07
A key step in the assessment of risk for installations where flammable liquids or gases are stored is the estimation of ignition probability. A review of current modelling and data confirmed that ignition probability values used in risk analyses tend to be based on extrapolation of limited incident data or, in many cases, on the judgement of those conducting the safety assessment. Existing models tend to assume that ignition probability is a function of release rate (or flammable gas cloud size) alone and they do not consider location, density or type of ignition source. An alternative mathematical framework for calculating ignition probability is outlined in which the approach used is to model the distribution of likely ignition sources and to calculate ignition probability by considering whether the flammable gas cloud will reach these sources. Data are collated on the properties of ignition sources within three generic land-use types: industrial, urban and rural. These data are then incorporated into a working model for ignition probability in a form capable of being implemented within risk analysis models. The sensitivity of the model results to assumptions made in deriving the ignition source properties is discussed and the model is compared with other available ignition probability methods.
Conditional Probability Analyses of the Spike Activity of Single Neurons
Gray, Peter R.
1967-01-01
With the objective of separating stimulus-related effects from refractory effects in neuronal spike data, various conditional probability analyses have been developed. These analyses are introduced and illustrated with examples based on electrophysiological data from auditory nerve fibers. The conditional probability analyses considered here involve the estimation of the conditional probability of a firing in a specified time interval (defined relative to the time of the stimulus presentation), given that the last firing occurred during an earlier specified time interval. This calculation enables study of the stimulus-related effects in the spike data with the time-since-the-last-firing as a controlled variable. These calculations indicate that auditory nerve fibers “recover” from the refractory effects that follow a firing in the following sense: after a “recovery time” of approximately 20 msec, the firing probabilities no longer depend on the time-since-the-last-firing. Probabilities conditional on this minimum time since the last firing are called “recovered probabilities.” The recovered probabilities presented in this paper are contrasted with the corresponding poststimulus time histograms, and the differences are related to the refractory properties of the nerve fibers. Imagesp[762]-a PMID:19210997