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Found 693 results for '"probability estimation"', showing 1-10
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  1. Weiss, Lionel (1986): A note on small-sample maximum probability estimation
    No abstract is available for this item.
    RePEc:eee:stapro:v:4:y:1986:i:3:p:109-111  Save to MyIDEAS
  2. Yuqian Zhang & Juergen Seufert & Steven Dellaportas (2023): Probability estimation in accounting: subjective numeracy matters
    Modified Bayesian reasoning tasks were applied in an accounting-related probability estimation, manipulating presentation formats. Findings - The study revealed a positive relationship between self-evaluated numeracy and performance in accounting probability estimation. ... Practical implications - Investors' ability to exercise sound judgement depends on the accuracy of their probability estimations. Manipulating the format of probability expressions can improve probability estimation performance in investors with low self-evaluated numeracy. Originality/value - This study identified a significant performance gap among participants in performing accounting probability estimations: those with high self-evaluated numeracy performed better than those with low self-evaluated numeracy.
    RePEc:eme:jaarpp:jaar-08-2022-0198  Save to MyIDEAS
  3. Zou, Yiyuan & Zhang, Honghai & Zhong, Gang & Liu, Hao & Feng, Dikun (2021): Collision probability estimation for small unmanned aircraft systems
    In this paper, the rapid calculating methods of collision probability estimation for sUAS are proposed supposing that the predicted position error follows a certain Gaussian distribution. ... For each type of collision zones, a corresponding algorithm is derived for collision probability estimation based on the univariate conditioning or Laguerre polynomials. ... Numerical simulations are carried out to analyze the differences in the collision probabilities when using different types of collision zones. The simulation results testify that the selection of the collision zones affects the collision probability estimation apparently, especially when the crossing angle of the two sUAS is between 0° and 40°.
    RePEc:eee:reensy:v:213:y:2021:i:c:s0951832021001630  Save to MyIDEAS
  4. Hanan Shteingart & Yonatan Loewenstein (2015): The Effect of Sample Size and Cognitive Strategy on Probability Estimation Bias
    Probability estimation is an essential cognitive function in perception, motor control, and decision making. Many studies have shown that when making decisions in a stochastic operant conditioning task, people and animals behave as if they underestimatethe probability of rare events. It is commonly assumed that this behavior is a natural consequence of estimating a probability from a small sample, also known as sampling bias. ... We show that in fact, probabilities estimated from a small sample can lead to behaviors that will be interpreted as underestimatingor as overestimating the probability of rare events, depending on the cognitive strategy used. ... Finally, we propose an alternative sequential learning model with a resetting of initial conditions for probability estimation and show that this model predicts the experimentally-observed robust underweighting of rare events.
    RePEc:huj:dispap:dp680  Save to MyIDEAS
  5. Morio, Jérôme (2011): Non-parametric adaptive importance sampling for the probability estimation of a launcher impact position
    Importance sampling (IS) is a useful simulation technique to estimate critical probability with a better accuracy than Monte Carlo methods. ... We apply this technique for the probability estimation of spatial launcher impact position since it has currently become a more and more important issue in the field of aeronautics.
    RePEc:eee:reensy:v:96:y:2011:i:1:p:178-183  Save to MyIDEAS
  6. Balesdent, Mathieu & Morio, Jérôme & Marzat, Julien (2015): Recommendations for the tuning of rare event probability estimators
    Being able to accurately estimate rare event probabilities is a challenging issue in order to improve the reliability of complex systems. Several powerful methods such as importance sampling, importance splitting or extreme value theory have been proposed in order to reduce the computational cost and to improve the accuracy of extreme probability estimation. ... It allows to provide a reduced set of tuning parameters that may lead to the reliable estimation of rare event probability for various problems.
    RePEc:eee:reensy:v:133:y:2015:i:c:p:68-78  Save to MyIDEAS
  7. Steven Miller (1999): Swinburne's Challenge: A Note on Probability Estimates for Nontypical Potential Evidence Instances
    No abstract is available for this item.
    RePEc:spr:qualqt:v:33:y:1999:i:4:p:353-360  Save to MyIDEAS
  8. De Capitani, L. & De Martini, D. (2011): On stochastic orderings of the Wilcoxon Rank Sum test statistic--With applications to reproducibility probability estimation testing
    Recently, the possibility of testing statistical hypotheses through the estimate of the reproducibility probability (i.e. the estimate of the power of the statistical test) in a general parametric framework has been introduced. ... This last result is useful in order to obtain a point estimator and lower bounds for the power of the WRS test. In analogy with the parametric setting, we show that these power estimators, alias reproducibility probability estimators, can be used as test statistic, i.e. it is possible to refer directly to the estimate of the reproducibility probability to perform the WRS test. Some reproducibility probability estimators based on asymptotic approximations of the power are provided. A brief simulation shows a very high agreement between the approximated reproducibility probability based tests and the classical one.
    RePEc:eee:stapro:v:81:y:2011:i:8:p:937-946  Save to MyIDEAS
  9. Riege, Anine H. & Teigen, Karl Halvor (2013): Additivity neglect in probability estimates: Effects of numeracy and response format
    When people are asked to estimate the probabilities for an exhaustive set of more than two events, they often produce probabilities that add up to more than 100%. ... Additive responses vary between experimental conditions, mainly as a result of response format, with a scale format leading to fewer additive responses than a list format with self-generated, written probabilities.
    RePEc:eee:jobhdp:v:121:y:2013:i:1:p:41-52  Save to MyIDEAS
  10. Wei-Hsiang Lin & Justin L Gardner & Shih-Wei Wu (2020): Context effects on probability estimation
    Many decisions rely on how we evaluate potential outcomes and estimate their corresponding probabilities of occurrence. ... Here, we show that probability estimation, like outcome evaluation, is subject to context effects that bias probability estimates away from other events present in the same context. However, unlike valuation, these context effects appeared to be scaled by estimated uncertainty, which is largest at intermediate probabilities. ... These results establish VMPFC as the neurocomputational substrate shared between valuation and probability estimation and highlight the additional involvement of dACC and IPS that can be uniquely attributed to probability estimation. Because probability estimation is a required component of computational accounts from sensory inference to higher cognition, the context effects found here may affect a wide array of cognitive computations.This study shows how probability estimation can be affected by the context of our recent experience, namely, how the presence of multiple events experienced closed in time can influence their respective probability estimates.
    RePEc:plo:pbio00:3000634  Save to MyIDEAS
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