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Sample records for probability learning

  1. Probabilities in implicit learning.

    PubMed

    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.

  2. Probability Machines: Consistent Probability Estimation Using Nonparametric Learning Machines

    PubMed Central

    Malley, J. D.; Kruppa, J.; Dasgupta, A.; Malley, K. G.; Ziegler, A.

    2011-01-01

    Summary Background Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. Objectives The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Methods Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Results Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Conclusions Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications. PMID:21915433

  3. 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…

  4. Location probability learning requires focal attention.

    PubMed

    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.

  5. 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.

  6. Probability detection mechanisms and motor learning.

    PubMed

    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.

  7. Rethinking the learning of belief network probabilities

    SciTech Connect

    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.

  8. Implicit learning of fifth- and sixth-order sequential probabilities.

    PubMed

    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.

  9. Probability learning and Piagetian probability conceptions in children 5 to 12 years old.

    PubMed

    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.

  10. Choice strategies in multiple-cue probability learning.

    PubMed

    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.

  11. Are baboons learning "orthographic" representations? Probably not.

    PubMed

    Linke, Maja; Bröker, Franziska; Ramscar, Michael; Baayen, Harald

    2017-01-01

    The ability of Baboons (papio papio) to distinguish between English words and nonwords has been modeled using a deep learning convolutional network model that simulates a ventral pathway in which lexical representations of different granularity develop. However, given that pigeons (columba livia), whose brain morphology is drastically different, can also be trained to distinguish between English words and nonwords, it appears that a less species-specific learning algorithm may be required to explain this behavior. Accordingly, we examined whether the learning model of Rescorla and Wagner, which has proved to be amazingly fruitful in understanding animal and human learning could account for these data. We show that a discrimination learning network using gradient orientation features as input units and word and nonword units as outputs succeeds in predicting baboon lexical decision behavior-including key lexical similarity effects and the ups and downs in accuracy as learning unfolds-with surprising precision. The models performance, in which words are not explicitly represented, is remarkable because it is usually assumed that lexicality decisions, including the decisions made by baboons and pigeons, are mediated by explicit lexical representations. By contrast, our results suggest that in learning to perform lexical decision tasks, baboons and pigeons do not construct a hierarchy of lexical units. Rather, they make optimal use of low-level information obtained through the massively parallel processing of gradient orientation features. Accordingly, we suggest that reading in humans first involves initially learning a high-level system building on letter representations acquired from explicit instruction in literacy, which is then integrated into a conventionalized oral communication system, and that like the latter, fluent reading involves the massively parallel processing of the low-level features encoding semantic contrasts.

  12. 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…

  13. 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…

  14. 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…

  15. 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…

  16. 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…

  17. 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…

  18. 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…

  19. 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…

  20. 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…

  1. 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…

  2. 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)

  3. Probably good diagrams for learning: representational epistemic recodification of probability theory.

    PubMed

    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.

  4. Feedback valence affects auditory perceptual learning independently of feedback probability.

    PubMed

    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.

  5. Feedback Valence Affects Auditory Perceptual Learning Independently of Feedback Probability

    PubMed Central

    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. PMID:25946173

  6. Visual search and location probability learning from variable perspectives.

    PubMed

    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.

  7. Statistical learning of action: the role of conditional probability.

    PubMed

    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.

  8. 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…

  9. 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…

  10. 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…

  11. 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…

  12. Probability Learning: Changes in Behavior Across Time and Development.

    PubMed

    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.

  13. Reduced reward-related probability learning in schizophrenia patients.

    PubMed

    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.

  14. Dual-Processes in Learning and Judgment: Evidence from the Multiple Cue Probability Learning Paradigm

    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…

  15. Dual-Processes in Learning and Judgment: Evidence from the Multiple Cue Probability Learning Paradigm

    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…

  16. 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.

  17. 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.

  18. Online Reinforcement Learning Using a Probability Density Estimation.

    PubMed

    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.

  19. Not All Probabilities Are Equivalent: Evidence From Orientation Versus Spatial Probability Learning.

    PubMed

    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

  20. What is learned about fragments in artificial grammar learning? A transitional probabilities approach.

    PubMed

    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.

  1. Judgments of learning index relative confidence, not subjective probability.

    PubMed

    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.

  2. 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…

  3. 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…

  4. 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…

  5. 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…

  6. Quantum probability and cognitive modeling: some cautions and a promising direction in modeling physics learning.

    PubMed

    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.

  7. [Stochastic simulation of the instrumental reflex in probability learning].

    PubMed

    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.

  8. The feedback-related negativity is modulated by feedback probability in observational learning.

    PubMed

    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.

  9. Sequence Learning in Infancy: The Independent Contributions of Conditional Probability and Pair Frequency Information

    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…

  10. 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…

  11. Sequence Learning in Infancy: The Independent Contributions of Conditional Probability and Pair Frequency Information

    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…

  12. The Effect of Simulation-Based Learning on Prospective Teachers' Inference Skills in Teaching Probability

    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…

  13. 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…

  14. METAPHOR: Probability density estimation for machine learning based photometric redshifts

    NASA Astrophysics Data System (ADS)

    Amaro, V.; Cavuoti, S.; Brescia, M.; Vellucci, C.; Tortora, C.; Longo, G.

    2017-06-01

    We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z's and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF's derived from a traditional SED template fitting method (Le Phare).

  15. 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

  16. 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…

  17. 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…

  18. 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…

  19. Pure perceptual-based learning of second-, third-, and fourth-order sequential probabilities.

    PubMed

    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.

  20. The Effects of Phonotactic Probability and Neighborhood Density on Adults' Word Learning in Noisy Conditions

    PubMed Central

    Storkel, Holly L.; Lee, Jaehoon; Cox, Casey

    2016-01-01

    Purpose Noisy conditions make auditory processing difficult. This study explores whether noisy conditions influence the effects of phonotactic probability (the likelihood of occurrence of a sound sequence) and neighborhood density (phonological similarity among words) on adults' word learning. Method Fifty-eight adults learned nonwords varying in phonotactic probability and neighborhood density in either an unfavorable (0-dB signal-to-noise ratio [SNR]) or a favorable (+8-dB SNR) listening condition. Word learning was assessed using a picture naming task by scoring the proportion of phonemes named correctly. Results The unfavorable 0-dB SNR condition showed a significant interaction between phonotactic probability and neighborhood density in the absence of main effects. In particular, adults learned more words when phonotactic probability and neighborhood density were both low or both high. The +8-dB SNR condition did not show this interaction. These results are inconsistent with those from a prior adult word learning study conducted under quiet listening conditions that showed main effects of word characteristics. Conclusions As the listening condition worsens, adult word learning benefits from a convergence of phonotactic probability and neighborhood density. Clinical implications are discussed for potential populations who experience difficulty with auditory perception or processing, making them more vulnerable to noise. PMID:27788276

  1. Development of probabilistic thinking-oriented learning tools for probability materials at junior high school students

    NASA Astrophysics Data System (ADS)

    Sari, Dwi Ivayana; Hermanto, Didik

    2017-08-01

    This research is a developmental research of probabilistic thinking-oriented learning tools for probability materials at ninth grade students. This study is aimed to produce a good probabilistic thinking-oriented learning tools. The subjects were IX-A students of MTs Model Bangkalan. The stages of this development research used 4-D development model which has been modified into define, design and develop. Teaching learning tools consist of lesson plan, students' worksheet, learning teaching media and students' achievement test. The research instrument used was a sheet of learning tools validation, a sheet of teachers' activities, a sheet of students' activities, students' response questionnaire and students' achievement test. The result of those instruments were analyzed descriptively to answer research objectives. The result was teaching learning tools in which oriented to probabilistic thinking of probability at ninth grade students which has been valid. Since teaching and learning tools have been revised based on validation, and after experiment in class produced that teachers' ability in managing class was effective, students' activities were good, students' responses to the learning tools were positive and the validity, sensitivity and reliability category toward achievement test. In summary, this teaching learning tools can be used by teacher to teach probability for develop students' probabilistic thinking.

  2. The change probability effect: incidental learning, adaptability, and shared visual working memory resources.

    PubMed

    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.

  3. Probability estimation with machine learning methods for dichotomous and multicategory outcome: theory.

    PubMed

    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).

  4. Incidental learning of probability information is differentially affected by the type of visual working memory representation.

    PubMed

    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.

  5. A Cross-Sectional Comparison of the Effects of Phonotactic Probability and Neighborhood Density on Word Learning by Preschool Children

    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…

  6. A Cross-Sectional Comparison of the Effects of Phonotactic Probability and Neighborhood Density on Word Learning by Preschool Children

    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…

  7. Blind Students' Learning of Probability through the Use of a Tactile Model

    ERIC Educational Resources Information Center

    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…

  8. 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…

  9. Effects of Multiple Simulation Presentation among Students of Different Anxiety Levels in the Learning of Probability

    ERIC Educational Resources Information Center

    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…

  10. 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…

  11. The Influence of Phonotactic Probability and Neighborhood Density on Children's Production of Newly Learned Words

    ERIC Educational Resources Information Center

    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…

  12. 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…

  13. Word Learning by Children with Phonological Delays: Differentiating Effects of Phonotactic Probability and Neighborhood Density

    ERIC Educational Resources Information Center

    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).…

  14. Blind Students' Learning of Probability through the Use of a Tactile Model

    ERIC Educational Resources Information Center

    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…

  15. The Influence of Phonotactic Probability and Neighborhood Density on Children's Production of Newly Learned Words

    ERIC Educational Resources Information Center

    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…

  16. Probability cueing of distractor locations: both intertrial facilitation and statistical learning mediate interference reduction.

    PubMed

    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.

  17. Deep learning of support vector machines with class probability output networks.

    PubMed

    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. Copyright © 2014 Elsevier Ltd. All rights reserved.

  18. The influence of phonotactic probability and neighborhood density on children's production of newly learned words.

    PubMed

    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.

  19. Splitting the variance of statistical learning performance: A parametric investigation of exposure duration and transitional probabilities.

    PubMed

    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.

  20. The influence of phonotactic probability and neighborhood density on children's production of newly learned words

    PubMed Central

    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

  1. Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets.

    PubMed

    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.

  2. Web-based experiments controlled by JavaScript: an example from probability learning.

    PubMed

    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.

  3. Short-term motor learning of dynamic balance control in children with probable Developmental Coordination Disorder.

    PubMed

    Jelsma, Dorothee; Ferguson, Gillian D; Smits-Engelsman, Bouwien C M; Geuze, Reint H

    2015-03-01

    To explore the differences in learning a dynamic balance task between children with and without probable Developmental Coordination Disorder (p-DCD) from different cultural backgrounds. Twenty-eight Dutch children with DCD (p-DCD-NL), a similar group of 17 South African children (p-DCD-SA) and 21 Dutch typically developing children (TD-NL) participated in the study. All children performed the Wii Fit protocol. The slope of the learning curve was used to estimate motor learning for each group. The protocol was repeated after six weeks. Level of motor skill was assessed with the Movement ABC-2. No significant difference in motor learning rate was found between p-DCD-NL and p-DCD-SA, but the learning rate of children with p-DCD was slower than the learning rate of TD children. Speed-accuracy trade off, as a way to improve performance by slowing down in the beginning was only seen in the TD children, indicating that TD children and p-DCD children used different strategies. Retention of the level of learned control of the game after six weeks was found in all three groups after six weeks. The learning slope was associated with the level of balance skill for all children. This study provides evidence that children with p-DCD have limitations in motor learning on a complex balance task. In addition, the data do not support the contention that learning in DCD differs depending on cultural background. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. Computational Modeling of Statistical Learning: Effects of Transitional Probability versus Frequency and Links to Word Learning

    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…

  5. Computational Modeling of Statistical Learning: Effects of Transitional Probability versus Frequency and Links to Word Learning

    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…

  6. Medial orbitofrontal cortex modulates associative learning between environmental cues and reward probability.

    PubMed

    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

  7. More than words: Adults learn probabilities over categories and relationships between them

    PubMed Central

    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

  8. Human brainstem plasticity: the interaction of stimulus probability and auditory learning.

    PubMed

    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.

  9. Impact of Statistical Learning Methods on the Predictive Power of Multivariate Normal Tissue Complication Probability Models

    SciTech Connect

    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.

  10. Choice as a function of reinforcer "hold": from probability learning to concurrent reinforcement.

    PubMed

    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.

  11. Probability estimation with machine learning methods for dichotomous and multicategory outcome: applications.

    PubMed

    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.

  12. A Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.

    PubMed

    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.

  13. Under the hood of statistical learning: A statistical MMN reflects the magnitude of transitional probabilities in auditory sequences

    PubMed Central

    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

  14. Under the hood of statistical learning: A statistical MMN reflects the magnitude of transitional probabilities in auditory sequences.

    PubMed

    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.

  15. The effect of incremental changes in phonotactic probability and neighborhood density on word learning by preschool children.

    PubMed

    Storkel, Holly L; Bontempo, Daniel E; Aschenbrenner, Andrew J; Maekawa, Junko; Lee, Su-Yeon

    2013-10-01

    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. The authors examined the full range of probability or density by sampling 5 nonwords from each of 4 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 through the use of multilevel modeling. 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 mid-low quartile, and then remained stable and poor in the 2 highest quartiles. An ordinary linear model sufficiently described neighborhood density. Here, word learning improved as density increased across all quartiles. 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.

  16. The effect of incremental changes in phonotactic probability and neighborhood density on word learning by preschool children

    PubMed Central

    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

  17. What subject matter questions motivate the use of machine learning approaches compared to statistical models for probability prediction?

    PubMed

    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.

  18. The Effect of Incremental Changes in Phonotactic Probability and Neighborhood Density on Word Learning by Preschool Children

    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…

  19. The Effect of Incremental Changes in Phonotactic Probability and Neighborhood Density on Word Learning by Preschool Children

    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…

  20. Triggering word learning in children with Language Impairment: the effect of phonotactic probability and neighbourhood density.

    PubMed

    McKean, Cristina; Letts, Carolyn; Howard, David

    2014-11-01

    The effect of phonotactic probability (PP) and neighbourhood density (ND) on triggering word learning was examined in children with Language Impairment (3;04-6;09) and compared to Typically Developing children. Nonwords, varying PP and ND orthogonally, were presented in a story context and their learning tested using a referent identification task. Group comparisons with receptive vocabulary as a covariate found no group differences in overall scores or in the influence of PP or ND. Therefore, there was no evidence of atypical lexical or phonological processing. 'Convergent' PP/ND (High PP/High ND; Low PP/Low ND) was optimal for word learning in both groups. This bias interacted with vocabulary knowledge. 'Divergent' PP/ND word scores (High PP/Low ND; Low PP/High ND) were positively correlated with vocabulary so the 'divergence disadvantage' reduced as vocabulary knowledge grew; an interaction hypothesized to represent developmental changes in lexical-phonological processing linked to the emergence of phonological representations.

  1. Curiosity and demographic factors as determinants of children's probability-learning strategies.

    PubMed

    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.

  2. METAPHOR: a machine-learning-based method for the probability density estimation of photometric redshifts

    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.

  3. Fall risk probability estimation based on supervised feature learning using public fall datasets.

    PubMed

    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.

  4. Probability Weighted Ensemble Transfer Learning for Predicting Interactions between HIV-1 and Human Proteins

    PubMed Central

    Mei, Suyu

    2013-01-01

    Reconstruction of host-pathogen protein interaction networks is of great significance to reveal the underlying microbic pathogenesis. However, the current experimentally-derived networks are generally small and should be augmented by computational methods for less-biased biological inference. From the point of view of computational modelling, data scarcity, data unavailability and negative data sampling are the three major problems for host-pathogen protein interaction networks reconstruction. In this work, we are motivated to address the three concerns and propose a probability weighted ensemble transfer learning model for HIV-human protein interaction prediction (PWEN-TLM), where support vector machine (SVM) is adopted as the individual classifier of the ensemble model. In the model, data scarcity and data unavailability are tackled by homolog knowledge transfer. The importance of homolog knowledge is measured by the ROC-AUC metric of the individual classifiers, whose outputs are probability weighted to yield the final decision. In addition, we further validate the assumption that only the homolog knowledge is sufficient to train a satisfactory model for host-pathogen protein interaction prediction. Thus the model is more robust against data unavailability with less demanding data constraint. As regards with negative data construction, experiments show that exclusiveness of subcellular co-localized proteins is unbiased and more reliable than random sampling. Last, we conduct analysis of overlapped predictions between our model and the existing models, and apply the model to novel host-pathogen PPIs recognition for further biological research. PMID:24260261

  5. 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.

  6. Probability weighted ensemble transfer learning for predicting interactions between HIV-1 and human proteins.

    PubMed

    Mei, Suyu

    2013-01-01

    Reconstruction of host-pathogen protein interaction networks is of great significance to reveal the underlying microbic pathogenesis. However, the current experimentally-derived networks are generally small and should be augmented by computational methods for less-biased biological inference. From the point of view of computational modelling, data scarcity, data unavailability and negative data sampling are the three major problems for host-pathogen protein interaction networks reconstruction. In this work, we are motivated to address the three concerns and propose a probability weighted ensemble transfer learning model for HIV-human protein interaction prediction (PWEN-TLM), where support vector machine (SVM) is adopted as the individual classifier of the ensemble model. In the model, data scarcity and data unavailability are tackled by homolog knowledge transfer. The importance of homolog knowledge is measured by the ROC-AUC metric of the individual classifiers, whose outputs are probability weighted to yield the final decision. In addition, we further validate the assumption that only the homolog knowledge is sufficient to train a satisfactory model for host-pathogen protein interaction prediction. Thus the model is more robust against data unavailability with less demanding data constraint. As regards with negative data construction, experiments show that exclusiveness of subcellular co-localized proteins is unbiased and more reliable than random sampling. Last, we conduct analysis of overlapped predictions between our model and the existing models, and apply the model to novel host-pathogen PPIs recognition for further biological research.

  7. A Cross-Sectional Comparison of the Effects of Phonotactic Probability and Neighborhood Density on Word Learning by Preschool Children

    PubMed Central

    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 context. Experiment 2 used an across-story probability/within-story density exposure context. Results showed that probability and density interacted to create optimal learning conditions. Specifically, rare/sparse sound sequences appeared to facilitate triggering of word learning. In contrast, the optimal convergence for lexical configuration and engagement was dependent on exposure context. In particular, common sound sequences and dense neighborhoods were optimal when density was manipulated across stories, whereas rare sound sequences and sparse neighborhoods were optimal when density was manipulated within a story. Taken together, children’s phonological and lexical representations were hypothesized to be interdependent on one another resulting in a convergence of form characteristics for optimal word learning. PMID:20563243

  8. Increasing probability of sign language learning by severely mentally retarded individuals: a discussion of learner, sign production, and linguistic variables.

    PubMed

    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.

  9. Activity in inferior parietal and medial prefrontal cortex signals the accumulation of evidence in a probability learning task.

    PubMed

    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.

  10. Activity in Inferior Parietal and Medial Prefrontal Cortex Signals the Accumulation of Evidence in a Probability Learning Task

    PubMed Central

    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. PMID:23401673

  11. A nonparametric Bayesian method of translating machine learning scores to probabilities in clinical decision support.

    PubMed

    Connolly, Brian; Cohen, K Bretonnel; Santel, Daniel; Bayram, Ulya; Pestian, John

    2017-08-07

    Probabilistic assessments of clinical care are essential for quality care. Yet, machine learning, which supports this care process has been limited to categorical results. To maximize its usefulness, it is important to find novel approaches that calibrate the ML output with a likelihood scale. Current state-of-the-art calibration methods are generally accurate and applicable to many ML models, but improved granularity and accuracy of such methods would increase the information available for clinical decision making. This novel non-parametric Bayesian approach is demonstrated on a variety of data sets, including simulated classifier outputs, biomedical data sets from the University of California, Irvine (UCI) Machine Learning Repository, and a clinical data set built to determine suicide risk from the language of emergency department patients. The method is first demonstrated on support-vector machine (SVM) models, which generally produce well-behaved, well understood scores. The method produces calibrations that are comparable to the state-of-the-art Bayesian Binning in Quantiles (BBQ) method when the SVM models are able to effectively separate cases and controls. However, as the SVM models' ability to discriminate classes decreases, our approach yields more granular and dynamic calibrated probabilities comparing to the BBQ method. Improvements in granularity and range are even more dramatic when the discrimination between the classes is artificially degraded by replacing the SVM model with an ad hoc k-means classifier. The method allows both clinicians and patients to have a more nuanced view of the output of an ML model, allowing better decision making. The method is demonstrated on simulated data, various biomedical data sets and a clinical data set, to which diverse ML methods are applied. Trivially extending the method to (non-ML) clinical scores is also discussed.

  12. 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

  13. Executive functions, categorization of probabilities, and learning from feedback: what does really matter for decision making under explicit risk conditions?

    PubMed

    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.

  14. 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.

  15. Computer-Based Graphical Displays for Enhancing Mental Animation and Improving Reasoning in Novice Learning of Probability

    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…

  16. The Effect of Phonotactic Probability and Neighbourhood Density on Pseudoword Learning in 6- and 7-Year-Old Children

    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…

  17. The Effect of Phonotactic Probability and Neighbourhood Density on Pseudoword Learning in 6- and 7-Year-Old Children

    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…

  18. Learning Binomial Probability Concepts with Simulation, Random Numbers and a Spreadsheet

    ERIC Educational Resources Information Center

    Rochowicz, John A., Jr.

    2005-01-01

    This paper introduces the reader to the concepts of binomial probability and simulation. A spreadsheet is used to illustrate these concepts. Random number generators are great technological tools for demonstrating the concepts of probability. Ideas of approximation, estimation, and mathematical usefulness provide numerous ways of learning…

  19. 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…

  20. Value and probability coding in a feedback-based learning task utilizing food rewards.

    PubMed

    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.

  1. Calibrating perceived understanding and competency in probability concepts: A diagnosis of learning difficulties based on Rasch probabilistic model

    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.

  2. Simulation of rat behavior by a reinforcement learning algorithm in consideration of appearance probabilities of reinforcement signals.

    PubMed

    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.

  3. Effects of phonotactic and orthotactic probabilities during fast mapping on 5-year-olds' learning to spell.

    PubMed

    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).

  4. Value and probability coding in a feedback-based learning task utilizing food rewards

    PubMed Central

    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

  5. Learning in reverse: eight-month-old infants track backward transitional probabilities.

    PubMed

    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.

  6. Learning in Reverse: Eight-Month-Old Infants Track Backward Transitional Probabilities

    ERIC Educational Resources Information Center

    Pelucchi, Bruna; Hay, Jessica F.; Saffran, Jenny R.

    2009-01-01

    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…

  7. An investigation of the role of some person and situation variables in multiple cue probability learning.

    PubMed

    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.

  8. More than just finding color: strategy in global visual search is shaped by learned target probabilities.

    PubMed

    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.

  9. Mice plan decision strategies based on previously learned time intervals, locations, and probabilities.

    PubMed

    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.

  10. Mice plan decision strategies based on previously learned time intervals, locations, and probabilities

    PubMed Central

    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

  11. Unification of field theory and maximum entropy methods for learning probability densities.

    PubMed

    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.

  12. The Influence of Part-Word Phonotactic Probability/Neighborhood Density on Word Learning by Preschool Children Varying in Expressive Vocabulary

    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…

  13. The Influence of Part-Word Phonotactic Probability/Neighborhood Density on Word Learning by Preschool Children Varying in Expressive Vocabulary

    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…

  14. A Study of Students' Learning Styles, Discipline Attitudes and Knowledge Acquisition in Technology-Enhanced Probability and Statistics Education

    PubMed Central

    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

  15. A Study of Students' Learning Styles, Discipline Attitudes and Knowledge Acquisition in Technology-Enhanced Probability and Statistics Education.

    PubMed

    Christou, Nicolas; Dinov, Ivo D

    2010-09-01

    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.

  16. [The dependence of the rate of learning on the probability of the random performance of an acquired reaction under different regimens of aversive reinforcement in rats].

    PubMed

    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.

  17. Modulation of Complex-Spike Duration and Probability during Cerebellar Motor Learning in Visually Guided Smooth-Pursuit Eye Movements of Monkeys

    PubMed Central

    2017-01-01

    Abstract Activation of an inferior olivary neuron powerfully excites Purkinje cells via its climbing fiber input and triggers a characteristic high-frequency burst, known as the complex spike (CS). The theory of cerebellar learning postulates that the CS induces long-lasting depression of the strength of synapses from active parallel fibers onto Purkinje cells, and that synaptic depression leads to changes in behavior. Prior reports showed that a CS on one learning trial is linked to a properly timed depression of simple spikes on the subsequent trial, as well as a learned change in pursuit eye movement. Further, the duration of a CS is a graded instruction for single-trial plasticity and behavioral learning. We now show across multiple learning paradigms that both the probability and duration of CS responses are correlated with the magnitudes of neural and behavioral learning in awake behaving monkeys. When the direction of the instruction for learning repeatedly was in the same direction or alternated directions, the duration and probability of CS responses decreased over a learning block along with the magnitude of trial-over-trial neural learning. When the direction of the instruction was randomized, CS duration, CS probability, and neural and behavioral learning remained stable across time. In contrast to depression, potentiation of simple-spike firing rate for ON-direction learning instructions follows a longer time course and plays a larger role as depression wanes. Computational analysis provides a model that accounts fully for the detailed statistics of a complex set of data. PMID:28698888

  18. Psychomotor development and learning difficulties in preschool children with probable attention deficit hyperactivity disorder: An epidemiological study in Navarre and La Rioja.

    PubMed

    Marín-Méndez, J J; Borra-Ruiz, M C; Álvarez-Gómez, M J; Soutullo Esperón, C

    2017-10-01

    ADHD symptoms begin to appear at preschool age. ADHD may have a significant negative impact on academic performance. In Spain, there are no standardized tools for detecting ADHD at preschool age, nor is there data about the incidence of this disorder. To evaluate developmental factors and learning difficulties associated with probable ADHD and to assess the impact of ADHD in school performance. We conducted a population-based study with a stratified multistage proportional cluster sample design. We found significant differences between probable ADHD and parents' perception of difficulties in expressive language, comprehension, and fine motor skills, as well as in emotions, concentration, behaviour, and relationships. Around 34% of preschool children with probable ADHD showed global learning difficulties, mainly in patients with the inattentive type. According to the multivariate analysis, learning difficulties were significantly associated with both delayed psychomotor development during the first 3 years of life (OR: 5.57) as assessed by parents, and probable ADHD (OR: 2.34) CONCLUSIONS: There is a connection between probable ADHD in preschool children and parents' perception of difficulties in several dimensions of development and learning. Early detection of ADHD at preschool ages is necessary to start prompt and effective clinical and educational interventions. Copyright © 2016 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.

  19. The influence of part-word phonotactic probability/neighborhood density on word learning by preschool children varying in expressive vocabulary

    PubMed Central

    Storkel, Holly L.; Hoover, Jill R.

    2010-01-01

    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 (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. PMID:20609282

  20. The influence of part-word phonotactic probability/neighborhood density on word learning by preschool children varying in expressive vocabulary.

    PubMed

    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.

  1. Effect of Phonotactic Probability and Neighborhood Density on Word-Learning Configuration by Preschoolers with Typical Development and Specific Language Impairment

    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…

  2. Effect of Phonotactic Probability and Neighborhood Density on Word-Learning Configuration by Preschoolers with Typical Development and Specific Language Impairment

    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…

  3. Probability Theory

    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.

  4. Learning about Probability from Text and Tables: Do Color Coding and Labeling through an Interactive-User Interface Help?

    ERIC Educational Resources Information Center

    Clinton, Virginia; Morsanyi, Kinga; Alibali, Martha W.; Nathan, Mitchell J.

    2016-01-01

    Learning from visual representations is enhanced when learners appropriately integrate corresponding visual and verbal information. This study examined the effects of two methods of promoting integration, color coding and labeling, on learning about probabilistic reasoning from a table and text. Undergraduate students (N = 98) were randomly…

  5. Prediction of seizure incidence probability in PTZ model of kindling through spatial learning ability in male and female rats.

    PubMed

    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.

  6. Lexicographic Probability, Conditional Probability, and Nonstandard Probability

    DTIC Science & Technology

    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

  7. Retention of Probability Concepts: A Pilot Study into the Effects of Mastery Learning with Sixth-Grade Students

    ERIC Educational Resources Information Center

    Romberg, Thomas A.; Shepler, Jack

    1973-01-01

    Tests were given after instruction in probability and four weeks later to 25 sixth graders. Correlation between achievement scores for each student was .78. Retention ratios for individuals, total tests, each objective, and each item indicated high initial performance may contribute to high retention. (Author/DT)

  8. Confidence Probability versus Detection Probability

    SciTech Connect

    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.

  9. 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.

  10. Bayesian Brains without Probabilities.

    PubMed

    Sanborn, Adam N; Chater, Nick

    2016-12-01

    Bayesian explanations have swept through cognitive science over the past two decades, from intuitive physics and causal learning, to perception, motor control and language. Yet people flounder with even the simplest probability questions. What explains this apparent paradox? How can a supposedly Bayesian brain reason so poorly with probabilities? In this paper, we propose a direct and perhaps unexpected answer: that Bayesian brains need not represent or calculate probabilities at all and are, indeed, poorly adapted to do so. Instead, the brain is a Bayesian sampler. Only with infinite samples does a Bayesian sampler conform to the laws of probability; with finite samples it systematically generates classic probabilistic reasoning errors, including the unpacking effect, base-rate neglect, and the conjunction fallacy. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  11. Uncertainty quantification and integration of machine learning techniques for predicting acid rock drainage chemistry: a probability bounds approach.

    PubMed

    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.

  12. Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting.

    PubMed

    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.

  13. Evaluation of the probability of arrester failure in a high-voltage transmission line using a Q learning artificial neural network model

    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.

  14. 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.

  15. Probability 1/e

    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.

  16. Probability 1/e

    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.

  17. Risk estimation using probability machines

    PubMed Central

    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

  18. Class probability estimation for medical studies.

    PubMed

    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.

  19. 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.

  20. Experience matters: information acquisition optimizes probability gain.

    PubMed

    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.

  1. What Are Probability Surveys?

    EPA Pesticide Factsheets

    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.

  2. Evolution and Probability.

    ERIC Educational Resources Information Center

    Bailey, David H.

    2000-01-01

    Some of the most impressive-sounding criticisms of the conventional theory of biological evolution involve probability. Presents a few examples of how probability should and should not be used in discussing evolution. (ASK)

  3. Dependent Probability Spaces

    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…

  4. The Role of Cooperative Learning Type Team Assisted Individualization to Improve the Students' Mathematics Communication Ability in the Subject of Probability Theory

    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…

  5. 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.

  6. 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.

  7. 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…

  8. 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…

  9. Probability and radical behaviorism

    PubMed Central

    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

  10. 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.

  11. Statistics and Probability

    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.

  12. PROBABILITY AND STATISTICS.

    DTIC Science & Technology

    STATISTICAL ANALYSIS, REPORTS), (*PROBABILITY, REPORTS), INFORMATION THEORY, DIFFERENTIAL EQUATIONS, STATISTICAL PROCESSES, STOCHASTIC PROCESSES, MULTIVARIATE ANALYSIS, DISTRIBUTION THEORY , DECISION THEORY, MEASURE THEORY, OPTIMIZATION

  13. Probability and Statistics.

    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…

  14. 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…

  15. Normal tissue complication probability (NTCP) modelling using spatial dose metrics and machine learning methods for severe acute oral mucositis resulting from head and neck radiotherapy

    PubMed Central

    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

  16. A Comprehensive Probability Project for the Upper Division One-Semester Probability Course Using Yahtzee

    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…

  17. A Comprehensive Probability Project for the Upper Division One-Semester Probability Course Using Yahtzee

    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…

  18. Guide star probabilities

    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.

  19. Probability with Roulette

    ERIC Educational Resources Information Center

    Marshall, Jennings B.

    2007-01-01

    This article describes how roulette can be used to teach basic concepts of probability. Various bets are used to illustrate the computation of expected value. A betting system shows variations in patterns that often appear in random events.

  20. 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.

  1. 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.

  2. 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.

  3. Quantum computing and probability.

    PubMed

    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.

  4. Launch Collision Probability

    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.

  5. The perception of probability.

    PubMed

    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.

  6. 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.

  7. 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.

  8. Estimating tail probabilities

    SciTech Connect

    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.

  9. Varga: On Probability.

    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…

  10. 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…

  11. 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…

  12. 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…

  13. 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…

  14. 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…

  15. Message Receipt Probabilities

    DTIC Science & Technology

    1975-11-01

    character strings, the length includes one space. Hence x ■ •ttW) - ifr 𔃻-ř + 5<>-*>4 *»)♦ 2(»-*>4 ♦ «>-*>31 The probability of accepting an incorrect...LETTERS SPELLED OUT ALFA NOVEMBER BRAVO OSCAR CHARLIE PAPA DELTA QUEBEC ECHO ROMEO FOXTROT SIERRA GOLF TANGO HOTEL UNIFORM INDIA VICTOR JULIET

  16. 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…

  17. 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®…

  18. 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®…

  19. Bayesian Probability Theory

    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.

  20. 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.

  1. Efficient Probability Sequences

    DTIC Science & Technology

    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

  2. Searching with Probabilities

    DTIC Science & Technology

    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

  3. Regional flood probabilities

    USGS Publications Warehouse

    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.

  4. Probability of causation approach

    SciTech Connect

    Jose, D.E.

    1988-08-01

    Probability of causation (PC) is sometimes viewed as a great improvement by those persons who are not happy with the present rulings of courts in radiation cases. The author does not share that hope and expects that PC will not play a significant role in these issues for at least the next decade. If it is ever adopted in a legislative compensation scheme, it will be used in a way that is unlikely to please most scientists. Consequently, PC is a false hope for radiation scientists, and its best contribution may well lie in some of the spin-off effects, such as an influence on medical practice.

  5. Retrieve Tether Survival Probability

    DTIC Science & Technology

    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

  6. Origin of Quantum Probabilities

    NASA Astrophysics Data System (ADS)

    Khrennikov, Andrei

    2001-12-01

    We demonstrate that the origin of the quantum probabilistic rule (which differs from the conventional Bayes' formula by the presence of cos θ-factor) might be explained by perturbation effects of preparation and measurement procedures. The main consequence of our investigation is that interference could be produced by purely corpuscular objects. In particular, the quantum rule for probabilities (with nontrivial cos θ-factor) could be simulated for macroscopic physical systems via preparation procedures producing statistical deviations of a special form. We discuss preparation and measurement procedures which may produce probabilistic rules which are neither classical nor quantum; in particular, hyperbolic 'quantum theory.'

  7. Seismicity alert probabilities at Parkfield, California, revisited

    USGS Publications Warehouse

    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.

  8. Calibrating random forests for probability estimation.

    PubMed

    Dankowski, Theresa; Ziegler, Andreas

    2016-09-30

    Probabilities can be consistently estimated using random forests. It is, however, unclear how random forests should be updated to make predictions for other centers or at different time points. In this work, we present two approaches for updating random forests for probability estimation. The first method has been proposed by Elkan and may be used for updating any machine learning approach yielding consistent probabilities, so-called probability machines. The second approach is a new strategy specifically developed for random forests. Using the terminal nodes, which represent conditional probabilities, the random forest is first translated to logistic regression models. These are, in turn, used for re-calibration. The two updating strategies were compared in a simulation study and are illustrated with data from the German Stroke Study Collaboration. In most simulation scenarios, both methods led to similar improvements. In the simulation scenario in which the stricter assumptions of Elkan's method were not met, the logistic regression-based re-calibration approach for random forests outperformed Elkan's method. It also performed better on the stroke data than Elkan's method. The strength of Elkan's method is its general applicability to any probability machine. However, if the strict assumptions underlying this approach are not met, the logistic regression-based approach is preferable for updating random forests for probability estimation. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

  9. Probabilities for Solar Siblings

    NASA Astrophysics Data System (ADS)

    Valtonen, Mauri; Bajkova, A. T.; Bobylev, V. V.; Mylläri, A.

    2015-02-01

    We have shown previously (Bobylev et al. Astron Lett 37:550-562, 2011) that some of the stars in the solar neighborhood today may have originated in the same star cluster as the Sun, and could thus be called Solar Siblings. In this work we investigate the sensitivity of this result to galactic models and to parameters of these models, and also extend the sample of orbits. There are a number of good candidates for the sibling category, but due to the long period of orbit evolution since the break-up of the birth cluster of the Sun, one can only attach probabilities of membership. We find that up to 10 % (but more likely around 1 %) of the members of the Sun's birth cluster could be still found within 100 pc from the Sun today.

  10. People's conditional probability judgments follow probability theory (plus noise).

    PubMed

    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.

  11. Familiarity and preference for pitch probability profiles.

    PubMed

    Cui, Anja-Xiaoxing; Collett, Meghan J; Troje, Niko F; Cuddy, Lola L

    2015-05-01

    We investigated familiarity and preference judgments of participants toward a novel musical system. We exposed participants to tone sequences generated from a novel pitch probability profile. Afterward, we either asked participants to identify more familiar or we asked participants to identify preferred tone sequences in a two-alternative forced-choice task. The task paired a tone sequence generated from the pitch probability profile they had been exposed to and a tone sequence generated from another pitch probability profile at three levels of distinctiveness. We found that participants identified tone sequences as more familiar if they were generated from the same pitch probability profile which they had been exposed to. However, participants did not prefer these tone sequences. We interpret this relationship between familiarity and preference to be consistent with an inverted U-shaped relationship between knowledge and affect. The fact that participants identified tone sequences as even more familiar if they were generated from the more distinctive (caricatured) version of the pitch probability profile which they had been exposed to suggests that the statistical learning of the pitch probability profile is involved in gaining of musical knowledge.

  12. Probability state modeling theory.

    PubMed

    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.

  13. Probability distributions for magnetotellurics

    SciTech Connect

    Stodt, John A.

    1982-11-01

    Estimates of the magnetotelluric transfer functions can be viewed as ratios of two complex random variables. It is assumed that the numerator and denominator are governed approximately by a joint complex normal distribution. Under this assumption, probability distributions are obtained for the magnitude, squared magnitude, logarithm of the squared magnitude, and the phase of the estimates. Normal approximations to the distributions are obtained by calculating mean values and variances from error propagation, and the distributions are plotted with their normal approximations for different percentage errors in the numerator and denominator of the estimates, ranging from 10% to 75%. The distribution of the phase is approximated well by a normal distribution for the range of errors considered, while the distribution of the logarithm of the squared magnitude is approximated by a normal distribution for a much larger range of errors than is the distribution of the squared magnitude. The distribution of the squared magnitude is most sensitive to the presence of noise in the denominator of the estimate, in which case the true distribution deviates significantly from normal behavior as the percentage errors exceed 10%. In contrast, the normal approximation to the distribution of the logarithm of the magnitude is useful for errors as large as 75%.

  14. Pointwise probability reinforcements for robust statistical inference.

    PubMed

    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.

  15. Adult Learning.

    ERIC Educational Resources Information Center

    Brunner, Edmund deS.; And Others

    Research in adult learning has probably progressed further and produced more definitive results than in any other area of adult education. E. L. Thorndike's study, "Adult Learning" and other studies have shown that--(1) adults can learn, and, given their own time, can learn as effectively in later maturity as in earlier adulthood, unless…

  16. A Tale of Two Probabilities

    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.

  17. A Tale of Two Probabilities

    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.

  18. 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…

  19. 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…

  20. 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…

  1. 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…

  2. Coherent Assessment of Subjective Probability

    DTIC Science & Technology

    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

  3. The Probability of Causal Conditionals

    ERIC Educational Resources Information Center

    Over, David E.; Hadjichristidis, Constantinos; Evans, Jonathan St. B. T.; Handley, Simon J.; Sloman, Steven A.

    2007-01-01

    Conditionals in natural language are central to reasoning and decision making. A theoretical proposal called the Ramsey test implies the conditional probability hypothesis: that the subjective probability of a natural language conditional, P(if p then q), is the conditional subjective probability, P(q [such that] p). We report three experiments on…

  4. Learning.

    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…

  5. Probabilities of transversions and transitions.

    PubMed

    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.

  6. 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.

  7. The Probabilities of Unique Events

    DTIC Science & Technology

    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

  8. PROBABILITY SURVEYS , CONDITIONAL PROBABILITIES AND ECOLOGICAL RISK ASSESSMENT

    EPA Science Inventory

    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 ...

  9. PROBABILITY SURVEYS, CONDITIONAL PROBABILITIES, AND ECOLOGICAL RISK ASSESSMENT

    EPA Science Inventory

    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...

  10. PROBABILITY SURVEYS , CONDITIONAL PROBABILITIES AND ECOLOGICAL RISK ASSESSMENT

    EPA Science Inventory

    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 ...

  11. Probability Surveys, Conditional Probability, and Ecological Risk Assessment

    EPA Science Inventory

    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 ...

  12. Probability Surveys, Conditional Probability, and Ecological Risk Assessment

    EPA Science Inventory

    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 ...

  13. 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.

  14. 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.

  15. The relationship between species detection probability and local extinction probability

    USGS Publications Warehouse

    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.

  16. The relationship between species detection probability and local extinction probability

    USGS Publications Warehouse

    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.

  17. 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.

  18. Training Teachers to Teach Probability

    ERIC Educational Resources Information Center

    Batanero, Carmen; Godino, Juan D.; Roa, Rafael

    2004-01-01

    In this paper we analyze the reasons why the teaching of probability is difficult for mathematics teachers, describe the contents needed in the didactical preparation of teachers to teach probability and analyze some examples of activities to carry out this training. These activities take into account the experience at the University of Granada,…

  19. The Probabilities of Conditionals Revisited

    ERIC Educational Resources Information Center

    Douven, Igor; Verbrugge, Sara

    2013-01-01

    According to what is now commonly referred to as "the Equation" in the literature on indicative conditionals, the probability of any indicative conditional equals the probability of its consequent of the conditional given the antecedent of the conditional. Philosophers widely agree in their assessment that the triviality arguments of…

  20. Linear positivity and virtual probability

    NASA Astrophysics Data System (ADS)

    Hartle, James B.

    2004-08-01

    We investigate the quantum theory of closed systems based on the linear positivity decoherence condition of Goldstein and Page. The objective of any quantum theory of a closed system, most generally the universe, is the prediction of probabilities for the individual members of sets of alternative coarse-grained histories of the system. Quantum interference between members of a set of alternative histories is an obstacle to assigning probabilities that are consistent with the rules of probability theory. A quantum theory of closed systems therefore requires two elements: (1) a condition specifying which sets of histories may be assigned probabilities and (2) a rule for those probabilities. The linear positivity condition of Goldstein and Page is the weakest of the general conditions proposed so far. Its general properties relating to exact probability sum rules, time neutrality, and conservation laws are explored. Its inconsistency with the usual notion of independent subsystems in quantum mechanics is reviewed. Its relation to the stronger condition of medium decoherence necessary for classicality is discussed. The linear positivity of histories in a number of simple model systems is investigated with the aim of exhibiting linearly positive sets of histories that are not decoherent. The utility of extending the notion of probability to include values outside the range of 0-1 is described. Alternatives with such virtual probabilities cannot be measured or recorded, but can be used in the intermediate steps of calculations of real probabilities. Extended probabilities give a simple and general way of formulating quantum theory. The various decoherence conditions are compared in terms of their utility for characterizing classicality and the role they might play in further generalizations of quantum mechanics.

  1. Failure probability under parameter uncertainty.

    PubMed

    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.

  2. 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.

  3. 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.

  4. Holographic probabilities in eternal inflation.

    PubMed

    Bousso, Raphael

    2006-11-10

    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.

  5. On quantum vs. classical probability

    SciTech Connect

    Rau, Jochen

    2009-12-15

    Quantum theory shares with classical probability theory many important properties. I show that this common core regards at least the following six areas, and I provide details on each of these: the logic of propositions, symmetry, probabilities, composition of systems, state preparation and reductionism. The essential distinction between classical and quantum theory, on the other hand, is shown to be joint decidability versus smoothness; for the latter in particular I supply ample explanation and motivation. Finally, I argue that beyond quantum theory there are no other generalisations of classical probability theory that are relevant to physics.

  6. 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.

  7. Logic, probability, and human reasoning.

    PubMed

    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.

  8. 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)

  9. 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)

  10. 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.

  11. Joint probabilities and quantum cognition

    SciTech Connect

    Acacio de Barros, J.

    2012-12-18

    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.

  12. Local estimation of posterior class probabilities to minimize classification errors.

    PubMed

    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.

  13. A Quantum Probability Model of Causal Reasoning

    PubMed Central

    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

  14. A quantum probability model of causal reasoning.

    PubMed

    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.

  15. Normal probability plots with confidence.

    PubMed

    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.

  16. Detonation probabilities of high explosives

    SciTech Connect

    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.

  17. Updating: learning versus supposing.

    PubMed

    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.

  18. Incompatible Stochastic Processes and Complex Probabilities

    NASA Technical Reports Server (NTRS)

    Zak, Michail

    1997-01-01

    The definition of conditional probabilities is based upon the existence of a joint probability. However, a reconstruction of the joint probability from given conditional probabilities imposes certain constraints upon the latter, so that if several conditional probabilities are chosen arbitrarily, the corresponding joint probability may not exist.

  19. Knowledge typology for imprecise probabilities.

    SciTech Connect

    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.

  20. Interference of probabilities in dynamics

    SciTech Connect

    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.

  1. 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…

  2. 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…

  3. Probability Distributome: A Web Computational Infrastructure for Exploring the Properties, Interrelations, and Applications of Probability Distributions.

    PubMed

    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

  4. Probability Distributome: A Web Computational Infrastructure for Exploring the Properties, Interrelations, and Applications of Probability Distributions

    PubMed Central

    Dinov, Ivo D.; Siegrist, Kyle; Pearl, Dennis K.; Kalinin, Alexandr; Christou, Nicolas

    2015-01-01

    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

  5. 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…

  6. 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…

  7. 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,…

  8. Comments on quantum probability theory.

    PubMed

    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.

  9. 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)

  10. 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,…

  11. Conditional Independence in Applied Probability.

    ERIC Educational Resources Information Center

    Pfeiffer, Paul E.

    This material assumes the user has the background provided by a good undergraduate course in applied probability. It is felt that introductory courses in calculus, linear algebra, and perhaps some differential equations should provide the requisite experience and proficiency with mathematical concepts, notation, and argument. The document is…

  12. 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…

  13. 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.

  14. 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…

  15. Probability surveys, conditional probability, and ecological risk assessment.

    PubMed

    Paul, John F; Munns, Wayne R

    2011-06-01

    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 broad geographic areas. Under certain conditions (including appropriate stratification of the sampled population, sufficient density of samples, and sufficient range of exposure levels paired with concurrent response values), this empirical approach provides estimates of risk using extant field-derived monitoring data. The monitoring data were used to prescribe the exposure field and to model the exposure-response relationship. We illustrate this approach by estimating risks to benthic communities from low dissolved oxygen (DO) in freshwater streams of the mid-Atlantic region and in estuaries of the Virginian Biogeographical Province of the United States. In both cases, the estimates of risk are consistent with the U.S. EPA's ambient water quality criteria for DO. Copyright © 2011 SETAC.

  16. Time-dependent earthquake probabilities

    NASA Astrophysics Data System (ADS)

    Gomberg, J.; Belardinelli, M. E.; Cocco, M.; Reasenberg, P.

    2005-05-01

    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 developed a general 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 failure of a single fault. In the first application, 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 or 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.

  17. Time-dependent earthquake probabilities

    USGS Publications Warehouse

    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.

  18. 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…

  19. 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…

  20. Understanding Y haplotype matching probability.

    PubMed

    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

  1. Probability distributions for multimeric systems.

    PubMed

    Albert, Jaroslav; Rooman, Marianne

    2016-01-01

    We propose a fast and accurate method of obtaining the equilibrium mono-modal joint probability distributions for multimeric systems. The method necessitates only two assumptions: the copy number of all species of molecule may be treated as continuous; and, the probability density functions (pdf) are well-approximated by multivariate skew normal distributions (MSND). Starting from the master equation, we convert the problem into a set of equations for the statistical moments which are then expressed in terms of the parameters intrinsic to the MSND. Using an optimization package on Mathematica, we minimize a Euclidian distance function comprising of a sum of the squared difference between the left and the right hand sides of these equations. Comparison of results obtained via our method with those rendered by the Gillespie algorithm demonstrates our method to be highly accurate as well as efficient.

  2. Probability summation--a critique.

    PubMed

    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.

  3. 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…

  4. 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

  5. Empirical and Computational Tsunami Probability

    NASA Astrophysics Data System (ADS)

    Geist, E. L.; Parsons, T.; ten Brink, U. S.; Lee, H. J.

    2008-12-01

    A key component in assessing the hazard posed by tsunamis is quantification of tsunami likelihood or probability. To determine tsunami probability, one needs to know the distribution of tsunami sizes and the distribution of inter-event times. Both empirical and computational methods can be used to determine these distributions. Empirical methods rely on an extensive tsunami catalog and hence, the historical data must be carefully analyzed to determine whether the catalog is complete for a given runup or wave height range. Where site-specific historical records are sparse, spatial binning techniques can be used to perform a regional, empirical analysis. Global and site-specific tsunami catalogs suggest that tsunami sizes are distributed according to a truncated or tapered power law and inter-event times are distributed according to an exponential distribution modified to account for clustering of events in time. Computational methods closely follow Probabilistic Seismic Hazard Analysis (PSHA), where size and inter-event distributions are determined for tsunami sources, rather than tsunamis themselves as with empirical analysis. In comparison to PSHA, a critical difference in the computational approach to tsunami probabilities is the need to account for far-field sources. The three basic steps in computational analysis are (1) determination of parameter space for all potential sources (earthquakes, landslides, etc.), including size and inter-event distributions; (2) calculation of wave heights or runup at coastal locations, typically performed using numerical propagation models; and (3) aggregation of probabilities from all sources and incorporation of uncertainty. It is convenient to classify two different types of uncertainty: epistemic (or knowledge-based) and aleatory (or natural variability). Correspondingly, different methods have been traditionally used to incorporate uncertainty during aggregation, including logic trees and direct integration. Critical

  6. [Biometric bases: basic concepts of probability calculation].

    PubMed

    Dinya, E

    1998-04-26

    The author gives or outline of the basic concepts of probability theory. The bases of the event algebra, definition of the probability, the classical probability model and the random variable are presented.

  7. 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

  8. 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.

  9. Probability of Detection Demonstration Transferability

    NASA Technical Reports Server (NTRS)

    Parker, Bradford H.

    2008-01-01

    The ongoing Mars Science Laboratory (MSL) Propellant Tank Penetrant Nondestructive Evaluation (NDE) Probability of Detection (POD) Assessment (NESC activity) has surfaced several issues associated with liquid penetrant POD demonstration testing. This presentation lists factors that may influence the transferability of POD demonstration tests. Initial testing will address the liquid penetrant inspection technique. Some of the factors to be considered in this task are crack aspect ratio, the extent of the crack opening, the material and the distance between the inspection surface and the inspector's eye.

  10. Modality, probability, and mental models.

    PubMed

    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.

  11. Network of sensors: acquisition probability.

    PubMed

    Arnon, Shlomi

    2007-09-01

    A network of sensors is considered one of the most attractive remote sensing technologies available at present. In the system under consideration a network of sensors and a remote base station communicate using optical wireless links. This is accomplished by a base station that acquires and identifies sensors using a unique subcarrier frequency. The sensors use an active retroreflector to communicate with the base station, which reduces the complexity, cost, and power consumption of the sensors. The base station employs an imaging receiver (detector matrix), in which signals arriving from different directions are detected by different pixels. The imaging receiver mitigates ambient light noise and interference between simultaneous uplink transmissions from different sensors, provided that the transmissions are imaged onto disjoint sets of pixels. We describe a scheme that allows simultaneous acquisition and identification of a sensor in a network by an imaging receiver. A probability model of erroneous acquisition of this scheme due to noise is derived. The model's results indicate that the matrix size, the signal, and the noise powers have the greatest influence in determining acquisition probability.

  12. Lectures on probability and statistics

    SciTech Connect

    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.

  13. MSPI False Indication Probability Simulations

    SciTech Connect

    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

  14. Associativity and normative credal probability.

    PubMed

    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.

  15. 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)

  16. Classifier calibration using splined empirical probabilities in clinical risk prediction.

    PubMed

    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.

  17. 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, , is undertaken to obtain a better understanding of its dependence on various reaction parameters. The study may also help to clearly demarcate onset of non-CN fission (NCNF), which causes fusion probability, PCN, to deviate from unity. Method: ER excitation functions for 52 reactions leading to CN in the mass region 170-220, which are available in the literature, have been compared with statistical model (SM) calculations. Capture cross sections have been obtained from a coupled-channels code. In the SM, shell corrections in both the level density and the fission barrier have been included. for these reactions has been extracted by comparing experimental and theoretical ER excitation functions in the energy range ˜5 %-35% above the potential barrier, where known effects of nuclear structure are insignificant. Results: has been shown to vary with entrance channel mass asymmetry, η (or charge product, ZpZt ), as well as with fissility of the CN, χCN. No parameter has been found to be adequate as a single scaling variable to determine . Approximate boundaries have been obtained from where starts deviating from unity. Conclusions: This study quite clearly reveals the limits of applicability of the SM in interpreting experimental observables from fusion reactions involving two massive nuclei. Deviation of from unity marks the beginning of the domain of dynamical models of fusion. Availability of precise ER cross

  18. Trajectory versus probability density entropy.

    PubMed

    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.

  19. 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.

  20. THE BLACK HOLE FORMATION PROBABILITY

    SciTech Connect

    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.

  1. 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.

  2. Task specificity of attention training: the case of probability cuing.

    PubMed

    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.

  3. Task specificity of attention training: the case of probability cuing

    PubMed Central

    Jiang, Yuhong V.; Swallow, Khena M.; Won, Bo-Yeong; Cistera, Julia D.; Rosenbaum, Gail M.

    2014-01-01

    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. PMID:25113853

  4. Variable selection in large margin classifier-based probability estimation with high-dimensional predictors.

    PubMed

    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.

  5. The Probability Distribution for a Biased Spinner

    ERIC Educational Resources Information Center

    Foster, Colin

    2012-01-01

    This article advocates biased spinners as an engaging context for statistics students. Calculating the probability of a biased spinner landing on a particular side makes valuable connections between probability and other areas of mathematics. (Contains 2 figures and 1 table.)

  6. 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…

  7. 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…

  8. Illustrating Basic Probability Calculations Using "Craps"

    ERIC Educational Resources Information Center

    Johnson, Roger W.

    2006-01-01

    Instructors may use the gambling game of craps to illustrate the use of a number of fundamental probability identities. For the "pass-line" bet we focus on the chance of winning and the expected game length. To compute these, probabilities of unions of disjoint events, probabilities of intersections of independent events, conditional probabilities…

  9. 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.

  10. The trajectory of the target probability effect.

    PubMed

    Hon, Nicholas; Yap, Melvin J; Jabar, Syaheed B

    2013-05-01

    The effect of target probability on detection times is well-established: Even when detection accuracy is high, lower probability targets are detected more slowly than higher probability ones. Although this target probability effect on detection times has been well-studied, one aspect of it has remained largely unexamined: How the effect develops over the span of an experiment. Here, we investigated this issue with two detection experiments that assessed different target probability ratios. Conventional block segment analysis and linear mixed-effects modeling converged on two key findings. First, we found that the magnitude of the target probability effect increases as one progresses through a block of trials. Second, we found, by examining the trajectories of the low- and high-probability targets, that this increase in effect magnitude was driven by the low-probability targets. Specifically, we found that low-probability targets were detected more slowly as a block of trials progressed. Performance to high-probability targets, on the other hand, was largely invariant across the block. The latter finding is of particular interest because it cannot be reconciled with accounts that propose that the target probability effect is driven by the high-probability targets.

  11. 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.

  12. Pre-Service Teachers' Conceptions of Probability

    ERIC Educational Resources Information Center

    Odafe, Victor U.

    2011-01-01

    Probability knowledge and skills are needed in science and in making daily decisions that are sometimes made under uncertain conditions. Hence, there is the need to ensure that the pre-service teachers of our children are well prepared to teach probability. Pre-service teachers' conceptions of probability are identified, and ways of helping them…

  13. Calibrating Subjective Probabilities Using Hierarchical Bayesian Models

    NASA Astrophysics Data System (ADS)

    Merkle, Edgar C.

    A body of psychological research has examined the correspondence between a judge's subjective probability of an event's outcome and the event's actual outcome. The research generally shows that subjective probabilities are noisy and do not match the "true" probabilities. However, subjective probabilities are still useful for forecasting purposes if they bear some relationship to true probabilities. The purpose of the current research is to exploit relationships between subjective probabilities and outcomes to create improved, model-based probabilities for forecasting. Once the model has been trained in situations where the outcome is known, it can then be used in forecasting situations where the outcome is unknown. These concepts are demonstrated using experimental psychology data, and potential applications are discussed.

  14. The uncertainty in earthquake conditional probabilities

    USGS Publications Warehouse

    Savage, J.C.

    1992-01-01

    The Working Group on California Earthquake Probabilities (WGCEP) questioned the relevance of uncertainty intervals assigned to earthquake conditional probabilities on the basis that the uncertainty in the probability estimate seemed to be greater the smaller the intrinsic breadth of the recurrence-interval distribution. It is shown here that this paradox depends upon a faulty measure of uncertainty in the conditional probability and that with a proper measure of uncertainty no paradox exists. The assertion that the WGCEP probability assessment in 1988 correctly forecast the 1989 Loma Prieta earthquake is also challenged by showing that posterior probability of rupture inferred after the occurrence of the earthquake from the prior WGCEP probability distribution reverts to a nearly informationless distribution. -Author

  15. Canonical Probability Distributions for Model Building, Learning, and Inference

    DTIC Science & Technology

    2006-07-14

    our software. Over 10,000 people from countries all over the world downloaded it since the release date. We have heard very positive feedback from...s43347 3312855 47.4295 31 Wo Append boundary suffb( E1s5..47J15 47.4295.-5669 .i.... Efl S656 66 56.�. 66.213 5 M s7 66 73 66.213 73.2E81 5

  16. Chance Encounters: Probability in Games and Simulations. Seeing and Thinking Mathematically in the Middle Grades.

    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…

  17. A Probability Distribution over Latent Causes, in the Orbitofrontal Cortex

    PubMed Central

    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

  18. Dynamic Encoding of Speech Sequence Probability in Human Temporal Cortex

    PubMed Central

    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

  19. Dynamic encoding of speech sequence probability in human temporal cortex.

    PubMed

    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.

  20. Bell Could Become the Copernicus of Probability

    NASA Astrophysics Data System (ADS)

    Khrennikov, Andrei

    2016-07-01

    Our aim is to emphasize the role of mathematical models in physics, especially models of geometry and probability. We briefly compare developments of geometry and probability by pointing to similarities and differences: from Euclid to Lobachevsky and from Kolmogorov to Bell. In probability, Bell could play the same role as Lobachevsky in geometry. In fact, violation of Bell’s inequality can be treated as implying the impossibility to apply the classical probability model of Kolmogorov (1933) to quantum phenomena. Thus the quantum probabilistic model (based on Born’s rule) can be considered as the concrete example of the non-Kolmogorovian model of probability, similarly to the Lobachevskian model — the first example of the non-Euclidean model of geometry. This is the “probability model” interpretation of the violation of Bell’s inequality. We also criticize the standard interpretation—an attempt to add to rigorous mathematical probability models additional elements such as (non)locality and (un)realism. Finally, we compare embeddings of non-Euclidean geometries into the Euclidean space with embeddings of the non-Kolmogorovian probabilities (in particular, quantum probability) into the Kolmogorov probability space. As an example, we consider the CHSH-test.

  1. Simple artificial neural networks that match probability and exploit and explore when confronting a multiarmed bandit.

    PubMed

    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.

  2. Prior probabilities modulate cortical surprise responses: A study of event-related potentials.

    PubMed

    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

  3. Probability and Quantum Paradigms: the Interplay

    NASA Astrophysics Data System (ADS)

    Kracklauer, A. F.

    2007-12-01

    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.

  4. UT Biomedical Informatics Lab (BMIL) Probability Wheel

    PubMed Central

    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

  5. 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.

  6. 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.

  7. Probability and Quantum Paradigms: the Interplay

    SciTech Connect

    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.

  8. UT Biomedical Informatics Lab (BMIL) Probability Wheel.

    PubMed

    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.

  9. 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).

  10. 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.

  11. The Foundations of Probability and Mathematical Statistics

    DTIC Science & Technology

    1975-03-01

    Lipschutz , Theory and Problems of Probability, Schaum*s Outline Series, McGraw Hill, New York, 1968, Chapters 1 and 3. Emanuel Parzen, Modern...Wesley, 1970, Chapter 3. Seymour Lipschutz , Theory and Problems of Probability, Schaum1s Outline Series, McGraw-Hill, New York, 1968, Chapter 4...Addison-Wesley, 1970, Chapter 4. Seymour Lipschutz , Theory and Problems of Probability, Schaum’s Outline Series, McGraw-Hill, New York, 1968, Chapter

  12. Psychophysics of the probability weighting function

    NASA Astrophysics Data System (ADS)

    Takahashi, Taiki

    2011-03-01

    A probability weighting function w(p) for an objective probability p in decision under risk plays a pivotal role in Kahneman-Tversky prospect theory. Although recent studies in econophysics and neuroeconomics widely utilized probability weighting functions, psychophysical foundations of the probability weighting functions have been unknown. Notably, a behavioral economist Prelec (1998) [4] axiomatically derived the probability weighting function w(p)=exp(-() (0<α<1 and w(0)=1,w(1e)=1e,w(1)=1), which has extensively been studied in behavioral neuroeconomics. The present study utilizes psychophysical theory to derive Prelec's probability weighting function from psychophysical laws of perceived waiting time in probabilistic choices. Also, the relations between the parameters in the probability weighting function and the probability discounting function in behavioral psychology are derived. Future directions in the application of the psychophysical theory of the probability weighting function in econophysics and neuroeconomics are discussed.

  13. 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.

  14. Cost functions to estimate a posteriori probabilities in multiclass problems.

    PubMed

    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.

  15. Neural Correlates of the Divergence of Instrumental Probability Distributions

    PubMed Central

    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

  16. Estimation of probability densities using scale-free field theories.

    PubMed

    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.

  17. 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.

  18. 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…

  19. 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…

  20. 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.

  1. 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…

  2. 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…

  3. Simplicity and probability in causal explanation.

    PubMed

    Lombrozo, Tania

    2007-11-01

    What makes some explanations better than others? This paper explores the roles of simplicity and probability in evaluating competing causal explanations. Four experiments investigate the hypothesis that simpler explanations are judged both better and more likely to be true. In all experiments, simplicity is quantified as the number of causes invoked in an explanation, with fewer causes corresponding to a simpler explanation. Experiment 1 confirms that all else being equal, both simpler and more probable explanations are preferred. Experiments 2 and 3 examine how explanations are evaluated when simplicity and probability compete. The data suggest that simpler explanations are assigned a higher prior probability, with the consequence that disproportionate probabilistic evidence is required before a complex explanation will be favored over a simpler alternative. Moreover, committing to a simple but unlikely explanation can lead to systematic overestimation of the prevalence of the cause invoked in the simple explanation. Finally, Experiment 4 finds that the preference for simpler explanations can be overcome when probability information unambiguously supports a complex explanation over a simpler alternative. Collectively, these findings suggest that simplicity is used as a basis for evaluating explanations and for assigning prior probabilities when unambiguous probability information is absent. More broadly, evaluating explanations may operate as a mechanism for generating estimates of subjective probability.

  4. 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...

  5. 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…

  6. 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…

  7. 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) All...

  8. 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…

  9. 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.

  10. 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…

  11. 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…

  12. 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…

  13. 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…

  14. 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.

  15. 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…

  16. 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.

  17. 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…

  18. 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.

  19. 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…

  20. 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…

  1. Alternative probability theories for cognitive psychology.

    PubMed

    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.

  2. 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.

  3. 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.

  4. Time-dependent landslide probability mapping

    USGS Publications Warehouse

    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.

  5. Multinomial mixture model with heterogeneous classification probabilities

    USGS Publications Warehouse

    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.

  6. 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.

  7. Executable Code Recognition in Network Flows Using Instruction Transition Probabilities

    NASA Astrophysics Data System (ADS)

    Kim, Ikkyun; Kang, Koohong; Choi, Yangseo; Kim, Daewon; Oh, Jintae; Jang, Jongsoo; Han, Kijun

    The ability to recognize quickly inside network flows to be executable is prerequisite for malware detection. For this purpose, we introduce an instruction transition probability matrix (ITPX) which is comprised of the IA-32 instruction sets and reveals the characteristics of executable code's instruction transition patterns. And then, we propose a simple algorithm to detect executable code inside network flows using a reference ITPX which is learned from the known Windows Portable Executable files. We have tested the algorithm with more than thousands of executable and non-executable codes. The results show that it is very promising enough to use in real world.

  8. Predetonation probability of a fission-bomb core

    NASA Astrophysics Data System (ADS)

    Reed, B. Cameron

    2010-08-01

    An undergraduate-level derivation of the probability that a uranium or plutonium fission bomb will suffer an uncontrolled predetonation due to neutrons liberated in spontaneous fissions in the fissile material is developed. Consistent with what was learned by Los Alamos bomb designers during World War II, it is shown why uncontrolled predetonation was not a problem for a U-235 bomb of the Little Boy "gun" design but necessitated development of implosion engineering for the Pu-239 Trinity and Fat Man bombs where the cores were contaminated with highly spontaneously fissile Pu-240.

  9. 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

  10. Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability.

    PubMed

    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.

  11. Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability

    PubMed Central

    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

  12. Negative probabilities, Fine's theorem, and linear positivity

    NASA Astrophysics Data System (ADS)

    Halliwell, J. J.; Yearsley, J. M.

    2013-02-01

    Many situations in quantum theory and other areas of physics lead to quasiprobabilities which seem to be physically useful but can be negative. The interpretation of such objects is not at all clear. In this paper, we show that quasiprobabilities naturally fall into two qualitatively different types, according to whether their non-negative marginals can or cannot be matched to a non-negative probability. The former type of quasiprobabilities, which we call viable, are qualitatively similar to true probabilities, but the latter type, which we call nonviable, may not have a sensible interpretation. Determining the existence of a probability matching given marginals is a nontrivial question in general. In simple examples, Fine's theorem indicates that inequalities of the Bell and Clauser-Horne-Shimony-Holt (CHSH) types provide criteria for its existence, and these examples are considered in detail. Our results have consequences for the linear positivity condition of Goldstein and Page in the context of the histories approach to quantum theory. Although it is a very weak condition for the assignment of probabilities, it fails in some important cases where our results indicate that probabilities clearly exist. We speculate that our method, of matching probabilities to a given set of marginals, provides a general method of assigning probabilities to histories and we show that it passes the Diósi test for the statistical independence of subsystems.

  13. Liquefaction probability curves for surficial geologic deposits

    USGS Publications Warehouse

    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.

  14. Prior probability modulates anticipatory activity in category-specific areas.

    PubMed

    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.

  15. Contact probability scaling of the Hilbert curve

    NASA Astrophysics Data System (ADS)

    Sanborn, Adrian; Li, Jian; Aiden, Erez L.

    2012-02-01

    Using Hi-C experiments, it has become possible to measure contact probability scalings for genomic polymers. However, the theoretical analysis of such scalings remains in its infancy. Here, we prove that contact probability scales with linear distance for lattice approximations of the Hilbert curve. These results point to the potential for new theoretical approaches to the study of contact probability, and shed light on the analysis behind the fractal globule, a recent model for the three-dimensional structure of the human genome.

  16. Objective and subjective probability in gene expression.

    PubMed

    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.

  17. 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.

  18. Using Microcomputers to Solve Probability Problems.

    ERIC Educational Resources Information Center

    Haigh, William E.

    1985-01-01

    Use of the computer to simulate or imitate probability problems that are difficult to analyze in any other way is discussed. How the Monte Carlo method works is clarified, with sample problems and programs. (MNS)

  19. 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)

  20. The low synaptic release probability in vivo.

    PubMed

    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.

  1. 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)

  2. 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)

  3. 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.

  4. 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.

  5. Inclusion probability with dropout: an operational formula.

    PubMed

    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.

  6. Zika Probably Not Spread Through Saliva: Study

    MedlinePlus

    ... page: https://medlineplus.gov/news/fullstory_167531.html Zika Probably Not Spread Through Saliva: Study Research with ... HealthDay News) -- Scientists have some interesting news about Zika: You're unlikely to get the virus from ...

  7. 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.

  8. Probability distribution of the vacuum energy density

    SciTech Connect

    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.

  9. Non-Gaussian Photon Probability Distribution

    NASA Astrophysics Data System (ADS)

    Solomon, Benjamin T.

    2010-01-01

    This paper investigates the axiom that the photon's probability distribution is a Gaussian distribution. The Airy disc empirical evidence shows that the best fit, if not exact, distribution is a modified Gamma mΓ distribution (whose parameters are α = r, βr/√u ) in the plane orthogonal to the motion of the photon. This modified Gamma distribution is then used to reconstruct the probability distributions along the hypotenuse from the pinhole, arc from the pinhole, and a line parallel to photon motion. This reconstruction shows that the photon's probability distribution is not a Gaussian function. However, under certain conditions, the distribution can appear to be Normal, thereby accounting for the success of quantum mechanics. This modified Gamma distribution changes with the shape of objects around it and thus explains how the observer alters the observation. This property therefore places additional constraints to quantum entanglement experiments. This paper shows that photon interaction is a multi-phenomena effect consisting of the probability to interact Pi, the probabilistic function and the ability to interact Ai, the electromagnetic function. Splitting the probability function Pi from the electromagnetic function Ai enables the investigation of the photon behavior from a purely probabilistic Pi perspective. The Probabilistic Interaction Hypothesis is proposed as a consistent method for handling the two different phenomena, the probability function Pi and the ability to interact Ai, thus redefining radiation shielding, stealth or cloaking, and invisibility as different effects of a single phenomenon Pi of the photon probability distribution. Sub wavelength photon behavior is successfully modeled as a multi-phenomena behavior. The Probabilistic Interaction Hypothesis provides a good fit to Otoshi's (1972) microwave shielding, Schurig et al. (2006) microwave cloaking, and Oulton et al. (2008) sub wavelength confinement; thereby providing a strong case that

  10. Imprecise Probability Methods for Weapons UQ

    SciTech Connect

    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.

  11. Grounding quantum probability in psychological mechanism.

    PubMed

    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.

  12. Probability, clinical decision making and hypothesis testing

    PubMed Central

    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

  13. A Manual for Encoding Probability Distributions.

    DTIC Science & Technology

    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

  14. Survival probability for open spherical billiards

    NASA Astrophysics Data System (ADS)

    Dettmann, Carl P.; Rahman, Mohammed R.

    2014-12-01

    We study the survival probability for long times in an open spherical billiard, extending previous work on the circular billiard. We provide details of calculations regarding two billiard configurations, specifically a sphere with a circular hole and a sphere with a square hole. The constant terms of the long-time survival probability expansions have been derived analytically. Terms that vanish in the long time limit are investigated analytically and numerically, leading to connections with the Riemann hypothesis.

  15. Survival probability for open spherical billiards.

    PubMed

    Dettmann, Carl P; Rahman, Mohammed R

    2014-12-01

    We study the survival probability for long times in an open spherical billiard, extending previous work on the circular billiard. We provide details of calculations regarding two billiard configurations, specifically a sphere with a circular hole and a sphere with a square hole. The constant terms of the long-time survival probability expansions have been derived analytically. Terms that vanish in the long time limit are investigated analytically and numerically, leading to connections with the Riemann hypothesis.

  16. 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.

  17. 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.

  18. Probabilities and health risks: a qualitative approach.

    PubMed

    Heyman, B; Henriksen, M; Maughan, K

    1998-11-01

    Health risks, defined in terms of the probability that an individual will suffer a particular type of adverse health event within a given time period, can be understood as referencing either natural entities or complex patterns of belief which incorporate the observer's values and knowledge, the position adopted in the present paper. The subjectivity inherent in judgements about adversity and time frames can be easily recognised, but social scientists have tended to accept uncritically the objectivity of probability. Most commonly in health risk analysis, the term probability refers to rates established by induction, and so requires the definition of a numerator and denominator. Depending upon their specification, many probabilities may be reasonably postulated for the same event, and individuals may change their risks by deciding to seek or avoid information. These apparent absurdities can be understood if probability is conceptualised as the projection of expectation onto the external world. Probabilities based on induction from observed frequencies provide glimpses of the future at the price of acceptance of the simplifying heuristic that statistics derived from aggregate groups can be validly attributed to individuals within them. The paper illustrates four implications of this conceptualisation of probability with qualitative data from a variety of sources, particularly a study of genetic counselling for pregnant women in a U.K. hospital. Firstly, the official selection of a specific probability heuristic reflects organisational constraints and values as well as predictive optimisation. Secondly, professionals and service users must work to maintain the facticity of an established heuristic in the face of alternatives. Thirdly, individuals, both lay and professional, manage probabilistic information in ways which support their strategic objectives. Fourthly, predictively sub-optimum schema, for example the idea of AIDS as a gay plague, may be selected because

  19. Site occupancy models with heterogeneous detection probabilities

    USGS Publications Warehouse

    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.

  20. Machine Learning

    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.

  1. 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…

  2. 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…

  3. The Estimation of Tree Posterior Probabilities Using Conditional Clade Probability Distributions

    PubMed Central

    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

  4. Tsunami probability in the Caribbean region

    NASA Astrophysics Data System (ADS)

    Parsons, T.; Geist, E. L.

    2008-12-01

    We calculated tsunami runup probability 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 20km by 20km cells, and the mean tsunami runup rate was determined for each cell. A remarkable ~500-year empirical record was used to calculate an empirical tsunami probability map, the first of three constructed for this study. However, it's 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 back-arc 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 20km by 20km coastal cell. Our best-estimate map gives a range of 30-year runup probability from 0-30 percent regionally.

  5. Tsunami probability in the Caribbean Region

    USGS Publications Warehouse

    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.

  6. 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

  7. Minimal entropy probability paths between genome families.

    PubMed

    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

  8. Causal inference, probability theory, and graphical insights.

    PubMed

    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.

  9. 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.

  10. The role of probabilities in physics.

    PubMed

    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.

  11. 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.

  12. Probabilistic thinking of elementary school students in solving probability tasks based on math ability

    NASA Astrophysics Data System (ADS)

    Sari, Dwi Ivayana; Budayasa, I. Ketut; Juniati, Dwi

    2017-08-01

    Probabilistic thinking is very important in human life especially in responding to situation which possibly occured or situation containing uncertainty elements. It is necessary to develop students' probabilistic thinking since in elementary school by teaching probability. Based on mathematics curriculum in Indonesia, probability is firstly introduced to ninth grade students. Though, some research showed that low-grade students were successful in solving probability tasks, even in pre school. This study is aimed to explore students' probabilistic thinking of elementary school; high and low math ability in solving probability tasks. Qualitative approach was chosen to describe in depth related to students' probabilistic thinking. The results showed that high and low math ability students were difference in responding to 1 and 2 dimensional sample space tasks, and probability comparison tasks of drawing marker and contextual. Representation used by high and low math ability students were also difference in responding to contextual probability of an event task and probability comparison task of rotating spinner. This study is as reference to mathematics curriculum developers of elementary school in Indonesia. In this case to introduce probability material and teach probability through spinner, as media in learning.

  13. 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.

  14. 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.

  15. Match probabilities in racially admixed populations.

    PubMed Central

    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

  16. Assessing semantic coherence in conditional probability estimates

    PubMed Central

    Fisher, Christopher R.

    2013-01-01

    Semantic coherence is a higher-order coherence benchmark that assesses whether a constellation of estimates—P(A), P(B), P(B | A), and P(A | B)—maps onto the relationship between sets implied by the description of a given problem. We present an automated method for evaluating semantic coherence in conditional probability estimates that efficiently reduces a large problem space into five meaningful patterns: identical sets, subsets, mutually exclusive sets, overlapping sets, and independent sets. It also identifies three theoretically interesting nonfallacious errors. We discuss unique issues in evaluating semantic coherence in conditional probabilities that are not present in joint probability judgments, such as errors resulting from dividing by zero and the use of a tolerance parameter to manage rounding errors. A spreadsheet implementing the methods described above can be downloaded as a supplement from www.springerlink.com. PMID:21512870

  17. Predicting the probability of outbreeding depression.

    PubMed

    Frankham, Richard; Ballou, Jonathan D; Eldridge, Mark D B; Lacy, Robert C; Ralls, Katherine; Dudash, Michele R; Fenster, Charles B

    2011-06-01

    Fragmentation of animal and plant populations typically leads to genetic erosion and increased probability of extirpation. Although these effects can usually be reversed by re-establishing gene flow between population fragments, managers sometimes fail to do so due to fears of outbreeding depression (OD). Rapid development of OD is due primarily to adaptive differentiation from selection or fixation of chromosomal variants. Fixed chromosomal variants can be detected empirically. We used an extended form of the breeders' equation to predict the probability of OD due to adaptive differentiation between recently isolated population fragments as a function of intensity of selection, genetic diversity, effective population sizes, and generations of isolation. Empirical data indicated that populations in similar environments had not developed OD even after thousands of generations of isolation. To predict the probability of OD, we developed a decision tree that was based on the four variables from the breeders' equation, taxonomic status, and gene flow within the last 500 years. The predicted probability of OD in crosses between two populations is elevated when the populations have at least one of the following characteristics: are distinct species, have fixed chromosomal differences, exchanged no genes in the last 500 years, or inhabit different environments. Conversely, the predicted probability of OD in crosses between two populations of the same species is low for populations with the same karyotype, isolated for <500 years, and that occupy similar environments. In the former case, we recommend crossing be avoided or tried on a limited, experimental basis. In the latter case, crossing can be carried out with low probability of OD. We used crosses with known results to test the decision tree and found that it correctly identified cases where OD occurred. Current concerns about OD in recently fragmented populations are almost certainly excessive. ©2011 Society for

  18. Explosion probability of unexploded ordnance: expert beliefs.

    PubMed

    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

  19. 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.

  20. Modeling gap probability in discontinuous vegetation canopies

    NASA Technical Reports Server (NTRS)

    Li, Xiaowen; Strahler, Alan H.

    1987-01-01

    In the present model for the gap probability of a discontinuous vegetation canopy, the assumption of a negative exponential attenuation within individual plant canopies will yield a problem involving the distribution distances within canopies through which a ray will pass. If, however, the canopies intersect and/or overlap, so that foliage density remains constant within the overlap area, the problem can be approached with two types of approximations. Attention is presently given to the case of a comparison of modeled gap probabilities with those observed for a stand of Maryland pine, which shows good agreement for zenith angles of illumination up to about 45 deg.

  1. Non-Gaussian Photon Probability Distribution

    SciTech Connect

    Solomon, Benjamin T.

    2010-01-28

    This paper investigates the axiom that the photon's probability distribution is a Gaussian distribution. The Airy disc empirical evidence shows that the best fit, if not exact, distribution is a modified Gamma mGAMMA distribution (whose parameters are alpha = r, betar/sq root(u)) in the plane orthogonal to the motion of the photon. This modified Gamma distribution is then used to reconstruct the probability distributions along the hypotenuse from the pinhole, arc from the pinhole, and a line parallel to photon motion. This reconstruction shows that the photon's probability distribution is not a Gaussian function. However, under certain conditions, the distribution can appear to be Normal, thereby accounting for the success of quantum mechanics. This modified Gamma distribution changes with the shape of objects around it and thus explains how the observer alters the observation. This property therefore places additional constraints to quantum entanglement experiments. This paper shows that photon interaction is a multi-phenomena effect consisting of the probability to interact P{sub i}, the probabilistic function and the ability to interact A{sub i}, the electromagnetic function. Splitting the probability function P{sub i} from the electromagnetic function A{sub i} enables the investigation of the photon behavior from a purely probabilistic P{sub i} perspective. The Probabilistic Interaction Hypothesis is proposed as a consistent method for handling the two different phenomena, the probability function P{sub i} and the ability to interact A{sub i}, thus redefining radiation shielding, stealth or cloaking, and invisibility as different effects of a single phenomenon P{sub i} of the photon probability distribution. Sub wavelength photon behavior is successfully modeled as a multi-phenomena behavior. The Probabilistic Interaction Hypothesis provides a good fit to Otoshi's (1972) microwave shielding, Schurig et al.(2006) microwave cloaking, and Oulton et al.(2008) sub

  2. 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.

  3. Probabilities for separating sets of order statistics.

    PubMed

    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.

  4. Probable Gastrointestinal Toxicity of Kombucha Tea

    PubMed Central

    Srinivasan, Radhika; Smolinske, Susan; Greenbaum, David

    1997-01-01

    Kombucha tea is a health beverage made by incubating the Kombucha “mushroom” in tea and sugar. Although therapeutic benefits have been attributed to the drink, neither its beneficial effects nor adverse side effects have been reported widely in the scientific literature. Side effects probably related to consumption of Kombucha tea are reported in four patients. Two presented with symptoms of allergic reaction, the third with jaundice, and the fourth with nausea, vomiting, and head and neck pain. In all four, use of Kombucha tea in proximity to onset of symptoms and symptom resolution on cessation of tea drinking suggest a probable etiologic association. PMID:9346462

  5. 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.

  6. 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.

  7. Nonstationary envelope process and first excursion probability.

    NASA Technical Reports Server (NTRS)

    Yang, J.-N.

    1972-01-01

    The definition of stationary random envelope proposed by Cramer and Leadbetter, is extended to the envelope of nonstationary random process possessing evolutionary power spectral densities. The density function, the joint density function, the moment function, and the crossing rate of a level of the nonstationary envelope process are derived. Based on the envelope statistics, approximate solutions to the first excursion probability of nonstationary random processes are obtained. In particular, applications of the first excursion probability to the earthquake engineering problems are demonstrated in detail.

  8. Random walks with similar transition probabilities

    NASA Astrophysics Data System (ADS)

    Schiefermayr, Klaus

    2003-04-01

    We consider random walks on the nonnegative integers with a possible absorbing state at -1. A random walk is called [alpha]-similar to a random walk if there exist constants Cij such that for the corresponding n-step transition probabilities , i,j[greater-or-equal, slanted]0, hold. We give necessary and sufficient conditions for the [alpha]-similarity of two random walks both in terms of the parameters and in terms of the corresponding spectral measures which appear in the spectral representation of the n-step transition probabilities developed by Karlin and McGregor.

  9. Nonequilibrium random matrix theory: Transition probabilities

    NASA Astrophysics Data System (ADS)

    Pedro, Francisco Gil; Westphal, Alexander

    2017-03-01

    In this paper we present an analytic method for calculating the transition probability between two random Gaussian matrices with given eigenvalue spectra in the context of Dyson Brownian motion. We show that in the Coulomb gas language, in large N limit, memory of the initial state is preserved in the form of a universal linear potential acting on the eigenvalues. We compute the likelihood of any given transition as a function of time, showing that as memory of the initial state is lost, transition probabilities converge to those of the static ensemble.

  10. Quantum probability and quantum decision-making.

    PubMed

    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.

  11. Determining system maintainability as a probability

    SciTech Connect

    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.

  12. Probability in biology: overview of a comprehensive theory of probability in living systems.

    PubMed

    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.

  13. The estimation of tree posterior probabilities using conditional clade probability distributions.

    PubMed

    Larget, Bret

    2013-07-01

    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.

  14. The Development of Probability Material using Edmodo

    NASA Astrophysics Data System (ADS)

    Sujadi, I.; Kurniasih, R.; Subanti, S.

    2017-04-01

    This research is aimed to describe the process and to get product development of learning material using Edmodo. Learning material is developed in the form of interactive learning material using Edmodo. This research belongs to Research and Development (R&D). The procedure includes the steps of Borg and Gall such as conducting research, collecting information, planning and developing preliminary form product, preliminary field testing and main product revision, main field testing and operational product revision, operational field testing and final product revision, dissemination and implementation. Expert judgment conducts data validity with the average score of media expert 3.57 and 3.67 from the material expert with a maximum score of 4 on a Likert scale. Practicability is obtained by the implementation of learning materials in each meeting which has exceeded 75%. The effectiveness of the response questionnaire of the student has achieved 80%. The students have received instructional materials well. The result of focus group discussion (FGD) can be concluded that learning material can be used in the teaching-learning process in curriculum 2013.

  15. Quantum probabilities as Dempster-Shafer probabilities in the lattice of subspaces

    SciTech Connect

    Vourdas, A.

    2014-08-15

    The orthocomplemented modular lattice of subspaces L[H(d)], of a quantum system with d-dimensional Hilbert space H(d), is considered. A generalized additivity relation which holds for Kolmogorov probabilities is violated by quantum probabilities in the full lattice L[H(d)] (it is only valid within the Boolean subalgebras of L[H(d)]). This suggests the use of more general (than Kolmogorov) probability theories, and here the Dempster-Shafer probability theory is adopted. An operator D(H{sub 1},H{sub 2}), which quantifies deviations from Kolmogorov probability theory is introduced, and it is shown to be intimately related to the commutator of the projectors P(H{sub 1}),P(H{sub 2}), to the subspaces H{sub 1}, H{sub 2}. As an application, it is shown that the proof of the inequalities of Clauser, Horne, Shimony, and Holt for a system of two spin 1/2 particles is valid for Kolmogorov probabilities, but it is not valid for Dempster-Shafer probabilities. The violation of these inequalities in experiments supports the interpretation of quantum probabilities as Dempster-Shafer probabilities.

  16. The analysis of probability task completion; Taxonomy of probabilistic thinking-based across gender in elementary school students

    NASA Astrophysics Data System (ADS)

    Sari, Dwi Ivayana; Budayasa, I. Ketut; Juniati, Dwi

    2017-08-01

    Formulation of mathematical learning goals now is not only oriented on cognitive product, but also leads to cognitive process, which is probabilistic thinking. Probabilistic thinking is needed by students to make a decision. Elementary school students are required to develop probabilistic thinking as foundation to learn probability at higher level. A framework of probabilistic thinking of students had been developed by using SOLO taxonomy, which consists of prestructural probabilistic thinking, unistructural probabilistic thinking, multistructural probabilistic thinking and relational probabilistic thinking. This study aimed to analyze of probability task completion based on taxonomy of probabilistic thinking. The subjects were two students of fifth grade; boy and girl. Subjects were selected by giving test of mathematical ability and then based on high math ability. Subjects were given probability tasks consisting of sample space, probability of an event and probability comparison. The data analysis consisted of categorization, reduction, interpretation and conclusion. Credibility of data used time triangulation. The results was level of boy's probabilistic thinking in completing probability tasks indicated multistructural probabilistic thinking, while level of girl's probabilistic thinking in completing probability tasks indicated unistructural probabilistic thinking. The results indicated that level of boy's probabilistic thinking was higher than level of girl's probabilistic thinking. The results could contribute to curriculum developer in developing probability learning goals for elementary school students. Indeed, teachers could teach probability with regarding gender difference.

  17. 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.

  18. Probability of boundary conditions in quantum cosmology

    NASA Astrophysics Data System (ADS)

    Nambu, Yasusada; Suenobu, Hiroshi

    2017-08-01

    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. 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.

  19. 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…

  20. 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)

  1. Simplicity and Probability in Causal Explanation

    ERIC Educational Resources Information Center

    Lombrozo, Tania

    2007-01-01

    What makes some explanations better than others? This paper explores the roles of simplicity and probability in evaluating competing causal explanations. Four experiments investigate the hypothesis that simpler explanations are judged both better and more likely to be true. In all experiments, simplicity is quantified as the number of causes…

  2. 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…

  3. 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.

  4. 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],…

  5. 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…

  6. 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…

  7. 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…

  8. Contributions of Probability to Everyday Living

    ERIC Educational Resources Information Center

    Koop, A. J.

    1977-01-01

    The contributions made by including probability theory in the primary grade curriculum are examined with respect to the day to day living of the developing child and his preparation for later life. In particular, language and decision making, misconceptions, research and systematic thinking, occupations, and the future are discussed. (MN)

  9. 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.

  10. 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.

  11. 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)

  12. 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…

  13. 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…

  14. 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…

  15. Estimating the Probability of Negative Events

    ERIC Educational Resources Information Center

    Harris, Adam J. L.; Corner, Adam; Hahn, Ulrike

    2009-01-01

    How well we are attuned to the statistics of our environment is a fundamental question in understanding human behaviour. It seems particularly important to be able to provide accurate assessments of the probability with which negative events occur so as to guide rational choice of preventative actions. One question that arises here is whether or…

  16. 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…

  17. 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…

  18. 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…

  19. On the bound of first excursion probability

    NASA Technical Reports Server (NTRS)

    Yang, J. N.

    1969-01-01

    Method has been developed to improve the lower bound of the first excursion probability that can apply to the problem with either constant or time-dependent barriers. The method requires knowledge of the joint density function of the random process at two arbitrary instants.

  20. 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.

  1. Time Required to Compute A Posteriori Probabilities,

    DTIC Science & Technology

    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)

  2. Probability Matching in the Right Hemisphere

    ERIC Educational Resources Information Center

    Miller, M.B.; Valsangkar-Smyth, M.

    2005-01-01

    Previously it has been shown that the left hemisphere, but not the right, of split-brain patients tends to match the frequency of previous occurrences in probability-guessing paradigms (Wolford, Miller, & Gazzaniga, 2000). This phenomenon has been attributed to an ''interpreter,'' a mechanism for making interpretations and forming hypotheses,…

  3. 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.

  4. Interstitial lung disease probably caused by imipramine.

    PubMed

    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.

  5. 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…

  6. Large Deviations: Advanced Probability for Undergrads

    ERIC Educational Resources Information Center

    Rolls, David A.

    2007-01-01

    In the branch of probability called "large deviations," rates of convergence (e.g. of the sample mean) are considered. The theory makes use of the moment generating function. So, particularly for sums of independent and identically distributed random variables, the theory can be made accessible to senior undergraduates after a first course in…

  7. 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…

  8. 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…

  9. 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…

  10. Toward an Objectivistic Theory of Probability

    DTIC Science & Technology

    1956-01-01

    permanent is already shown by psychoanalysis , in my opinion. The fact that in nature ’all is woven into one whole,’ that space, matter, gravitation...the comprehensive probability distribution of the process. For any eventuality An E tn we shall write simply u(An) to denote the number u(Ql X * X A

  11. 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

  12. Quantum temporal probabilities in tunneling systems

    SciTech Connect

    Anastopoulos, Charis Savvidou, Ntina

    2013-09-15

    We study the temporal aspects of quantum tunneling as manifested in time-of-arrival experiments in which the detected particle tunnels through a potential barrier. In particular, we present a general method for constructing temporal probabilities in tunneling systems that (i) defines ‘classical’ time observables for quantum systems and (ii) applies to relativistic particles interacting through quantum fields. We show that the relevant probabilities are defined in terms of specific correlation functions of the quantum field associated with tunneling particles. We construct a probability distribution with respect to the time of particle detection that contains all information about the temporal aspects of the tunneling process. In specific cases, this probability distribution leads to the definition of a delay time that, for parity-symmetric potentials, reduces to the phase time of Bohm and Wigner. We apply our results to piecewise constant potentials, by deriving the appropriate junction conditions on the points of discontinuity. For the double square potential, in particular, we demonstrate the existence of (at least) two physically relevant time parameters, the delay time and a decay rate that describes the escape of particles trapped in the inter-barrier region. Finally, we propose a resolution to the paradox of apparent superluminal velocities for tunneling particles. We demonstrate that the idea of faster-than-light speeds in tunneling follows from an inadmissible use of classical reasoning in the description of quantum systems. -- Highlights: •Present a general methodology for deriving temporal probabilities in tunneling systems. •Treatment applies to relativistic particles interacting through quantum fields. •Derive a new expression for tunneling time. •Identify new time parameters relevant to tunneling. •Propose a resolution of the superluminality paradox in tunneling.

  13. An Alternative Version of Conditional Probabilities and Bayes' Rule: An Application of Probability Logic

    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…

  14. 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…

  15. Killeen's Probability of Replication and Predictive Probabilities: How to Compute, Use, and Interpret Them

    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…

  16. An Alternative Version of Conditional Probabilities and Bayes' Rule: An Application of Probability Logic

    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…

  17. You Say "Probable" and I Say "Likely": Improving Interpersonal Communication With Verbal Probability Phrases

    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…

  18. Adding a visual linear scale probability to the PIOPED probability of pulmonary embolism.

    PubMed

    Christiansen, F; Nilsson, T; Måre, K; Carlsson, A

    1997-05-01

    Reporting a lung scintigraphy diagnosis as a PIOPED categorical probability of pulmonary embolism offers the clinician a wide range of interpretation. Therefore the purpose of this study was to analyze the impact on lung scintigraphy reporting of adding a visual linear scale (VLS) probability assessment to the ordinary PIOPED categorical probability. The study material was a re-evaluation of lung scintigrams from a prospective study of 170 patients. All patients had been examined by lung scintigraphy and pulmonary angiography. The scintigrams were re-evaluated by 3 raters, and the probability of pulmonary embolism was estimated by the PIOPED categorization and by a VLS probability. The test was repeated after 6 months. There was no significant difference (p > 0.05) in the area under the ROC curve between the PIOPED categorization and the VLS for any of the 3 raters. Analysis of agreement among raters and for repeatability demonstrated low agreement in the mid-range of probabilities. A VLS probability estimate did not significantly improve the overall accuracy of the diagnosis compared to the categorical PIOPED probability assessment alone. From the data of our present study we cannot recommend the addition of a VLS score to the PIOPED categorization.

  19. 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…

  20. A Computational Model of Word Segmentation from Continuous Speech Using Transitional Probabilities of Atomic Acoustic Events

    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…

  1. 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)

  2. 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…

  3. The Independent Effects of Phonotactic Probability and Neighbourhood Density on Lexical Acquisition by Preschool Children

    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…

  4. 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…

  5. 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)

  6. A Computational Model of Word Segmentation from Continuous Speech Using Transitional Probabilities of Atomic Acoustic Events

    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…

  7. 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…

  8. The Independent Effects of Phonotactic Probability and Neighbourhood Density on Lexical Acquisition by Preschool Children

    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…

  9. 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)

  10. Developing a Model and Applications for Probabilities of Student Success: A Case Study of Predictive Analytics

    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…

  11. Developing a Model and Applications for Probabilities of Student Success: A Case Study of Predictive Analytics

    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…

  12. 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)

  13. Probability Map Viewer: near real-time probability map generator of serial block electron microscopy collections.

    PubMed

    Churas, Christopher; Perez, Alex J; Hakozaki, Hiroyuki; Wong, Willy; Lee, David; Peltier, Steven T; Ellisman, Mark H

    2017-10-01

    To expedite the review of semi-automated probability maps of organelles and other features from 3D electron microscopy data we have developed Probability Map Viewer, a Java-based web application that enables the computation and visualization of probability map generation results in near real-time as the data are being collected from the microscope. Probability Map Viewer allows the user to select one or more voxel classifiers, apply them on a sub-region of an active collection, and visualize the results as overlays on the raw data via any web browser using a personal computer or mobile device. Thus, Probability Map Viewer accelerates and informs the image analysis workflow by providing a tool for experimenting with and optimizing dataset-specific segmentation strategies during imaging. https://github.com/crbs/probabilitymapviewer. mellisman@ucsd.edu. Supplementary data are available at Bioinformatics online.

  14. 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,…

  15. 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,…

  16. VOLCANIC RISK ASSESSMENT - PROBABILITY AND CONSEQUENCES

    SciTech Connect

    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

  17. 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…

  18. 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

  19. 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

  20. Probability of identity by descent in metapopulations.

    PubMed Central

    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