Results for 'Bayesian estimation'

978 found
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  1.  20
    Bayesian Estimations under the Weighted LINEX Loss Function Based on Upper Record Values.Fuad S. Al-Duais - 2021 - Complexity 2021:1-7.
    The essential objective of this research is to develop a linear exponential loss function to estimate the parameters and reliability function of the Weibull distribution based on upper record values when both shape and scale parameters are unknown. We perform this by merging a weight into LINEX to produce a new loss function called the weighted linear exponential loss function. Then, we utilized WLINEX to derive the parameters and reliability function of the WD. Next, we compared the performance of the (...)
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  2.  65
    Bayesian estimation and testing of structural equation models.Richard Scheines - unknown
    The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution over the parameters of a structural equation model (SEM) given covariance data and a prior distribution over the parameters. Point estimates, standard deviations and interval estimates for the parameters can be computed from these samples. If the prior distribution over the parameters is uninformative, the posterior is proportional to the likelihood, and asymptotically the inferences based on the Gibbs sample are the same as those (...)
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  3.  21
    Bayesian estimation of Shannon entropy.Lin Yuan & H. K. Kesavan - 1997 - History and Philosophy of Logic 26 (1):139-148.
  4. Truthlikeness and bayesian estimation.Ilkka Niiniluoto - 1986 - Synthese 67 (2):321 - 346.
  5.  88
    Prior Specification for More Stable Bayesian Estimation of Multilevel Latent Variable Models in Small Samples: A Comparative Investigation of Two Different Approaches.Steffen Zitzmann, Christoph Helm & Martin Hecht - 2021 - Frontiers in Psychology 11.
    Bayesian approaches for estimating multilevel latent variable models can be beneficial in small samples. Prior distributions can be used to overcome small sample problems, for example, when priors that increase the accuracy of estimation are chosen. This article discusses two different but not mutually exclusive approaches for specifying priors. Both approaches aim at stabilizing estimators in such a way that the Mean Squared Error of the estimator of the between-group slope will be small. In the first approach, the (...)
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  6.  22
    Systematically Defined Informative Priors in Bayesian Estimation: An Empirical Application on the Transmission of Internalizing Symptoms Through Mother-Adolescent Interaction Behavior.Susanne Schulz, Mariëlle Zondervan-Zwijnenburg, Stefanie A. Nelemans, Duco Veen, Albertine J. Oldehinkel, Susan Branje & Wim Meeus - 2021 - Frontiers in Psychology 12.
    BackgroundBayesian estimation with informative priors permits updating previous findings with new data, thus generating cumulative knowledge. To reduce subjectivity in the process, the present study emphasizes how to systematically weigh and specify informative priors and highlights the use of different aggregation methods using an empirical example that examined whether observed mother-adolescent positive and negative interaction behavior mediate the associations between maternal and adolescent internalizing symptoms across early to mid-adolescence in a 3-year longitudinal multi-method design.MethodsThe sample consisted of 102 mother-adolescent (...)
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  7.  25
    Bayesian Prior Choice in IRT Estimation Using MCMC and Variational Bayes.Prathiba Natesan, Ratna Nandakumar, Tom Minka & Jonathan D. Rubright - 2016 - Frontiers in Psychology 7:214660.
    This study investigated the impact of three prior distributions: matched, standard vague, and hierarchical in Bayesian estimation parameter recovery in two and one parameter models. Two Bayesian estimation methods were utilized: Markov chain Monte Carlo (MCMC) and the relatively new, Variational Bayesian (VB). Conditional (CML) and Marginal Maximum Likelihood (MML) estimates were used as baseline methods for comparison. Vague priors produced large errors or convergence issues and are not recommended. For both MCMC and VB, the (...)
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  8.  23
    Cultural Differences in Strength of Conformity Explained Through Pathogen Stress: A Statistical Test Using Hierarchical Bayesian Estimation.Yutaka Horita & Masanori Takezawa - 2018 - Frontiers in Psychology 9.
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  9.  51
    Estimating latent causal influences: Tetrad III variable selection and bayesian parameter estimation.Richard Scheines - unknown
    The statistical evidence for the detrimental effect of exposure to low levels of lead on the cognitive capacities of children has been debated for several decades. In this paper I describe how two techniques from artificial intelligence and statistics help make the statistical evidence for the accepted epidemiological conclusion seem decisive. The first is a variable-selection routine in TETRAD III for finding causes, and the second a Bayesian estimation of the parameter reflecting the causal influence of Actual Lead (...)
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  10.  52
    Estimation of a Bernouilli Parameter: A Normative Approach to Replace the Bayesian One.Jean-franÇois Laslier - 1989 - Theory and Decision 26 (3):253.
  11.  79
    Non-bayesian foundations for statistical estimation, prediction, and the ravens example.Malcolm R. Forster - 1994 - Erkenntnis 40 (3):357 - 376.
    The paper provides a formal proof that efficient estimates of parameters, which vary as as little as possible when measurements are repeated, may be expected to provide more accurate predictions. The definition of predictive accuracy is motivated by the work of Akaike (1973). Surprisingly, the same explanation provides a novel solution for a well known problem for standard theories of scientific confirmation — the Ravens Paradox. This is significant in light of the fact that standard Bayesian analyses of the (...)
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  12. The Estimation of Probabilities: An Essay on Modern Bayesian Methods.I. J. Good, Ian Hacking, R. C. Jeffrey & Håkan Törnebohm - 1966 - Synthese 16 (2):234-244.
     
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  13.  40
    Developing Bayesian adaptive methods for estimating sensitivity thresholds in Yes-No and forced-choice tasks.Luis A. Lesmes, Zhong-Lin Lu, Jongsoo Baek, Nina Tran, Barbara A. Dosher & Thomas D. Albright - 2015 - Frontiers in Psychology 6.
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  14.  12
    A generalized double robust Bayesian model averaging approach to causal effect estimation with application to the study of osteoporotic fractures.Claudia Beaudoin & Denis Talbot - 2022 - Journal of Causal Inference 10 (1):335-371.
    Analysts often use data-driven approaches to supplement their knowledge when selecting covariates for effect estimation. Multiple variable selection procedures for causal effect estimation have been devised in recent years, but additional developments are still required to adequately address the needs of analysts. We propose a generalized Bayesian causal effect estimation algorithm to perform variable selection and produce double robust estimates of causal effects for binary or continuous exposures and outcomes. GBCEE employs a prior distribution that targets (...)
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  15.  36
    Bayesian hierarchical grouping: Perceptual grouping as mixture estimation.Vicky Froyen, Jacob Feldman & Manish Singh - 2015 - Psychological Review 122 (4):575-597.
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  16.  36
    Bayesian probability estimates are not necessary to make choices satisfying Bayes’ rule in elementary situations.Artur Domurat, Olga Kowalczuk, Katarzyna Idzikowska, Zuzanna Borzymowska & Marta Nowak-Przygodzka - 2015 - Frontiers in Psychology 6:130369.
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  17.  19
    An Improved Parameter-Estimating Method in Bayesian Networks Applied for Cognitive Diagnosis Assessment.Ling Ling Wang, Tao Xin & Liu Yanlou - 2021 - Frontiers in Psychology 12.
    Bayesian networks can be employed to cognitive diagnostic assessment. Most of the existing researches on the BNs for CDA utilized the MCMC algorithm to estimate parameters of BNs. When EM algorithm and gradient descending learning method are adopted to estimate the parameters of BNs, some challenges may emerge in educational assessment due to the monotonic constraints cannot be satisfied in the above two methods. This paper proposed to train the BN first based on the ideal response pattern data contained (...)
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  18.  1
    A normative model for Bayesian combination of subjective probability estimates.Susanne Gabriele Trick, Constantin A. Rothkopf & Frank Jäkel - unknown
    Combining experts’ subjective probability estimates is a fundamental task with broad applicability in domains ranging from finance to public health. However, it is still an open question how to combine such estimates optimally. Since the beta distribution is a common choice for modeling uncertainty about probabilities, here we propose a family of normative Bayesian models for aggregating probability estimates based on beta distributions. We systematically derive and compare different variants, including hierarchical and non-hierarchical as well as asymmetric and symmetric (...)
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  19.  47
    Bayesian clinical reasoning: does intuitive estimation of likelihood ratios on an ordinal scale outperform estimation of sensitivities and specificities?Juan Moreira, Zeno Bisoffi, Alberto Narváez & Jef Van den Ende - 2008 - Journal of Evaluation in Clinical Practice 14 (5):934-940.
  20.  64
    Estimation of Reliability Parameters Under Incomplete Primary Information.A. N. Golodnikov, P. S. Knopov & V. A. Pepelyaev - 2004 - Theory and Decision 57 (4):331-344.
    We consider the procedure for small-sample estimation of reliability parameters. The main shortcomings of the classical methods and the Bayesian approach are analyzed. Models that find robust Bayesian estimates are proposed. The sensitivity of the Bayesian estimates to the choice of the prior distribution functions is investigated using models that find upper and lower bounds. The proposed models reduce to optimization problems in the space of distribution functions.
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  21.  45
    The autocorrelated Bayesian sampler: A rational process for probability judgments, estimates, confidence intervals, choices, confidence judgments, and response times.Jian-Qiao Zhu, Joakim Sundh, Jake Spicer, Nick Chater & Adam N. Sanborn - 2024 - Psychological Review 131 (2):456-493.
  22. Estimation and Model Selection in Dirichlet Regression.Julio Michael Stern - 2012 - AIP Conference Proceedings 1443:206-213.
    We study Compositional Models based on Dirichlet Regression where, given a (vector) covariate x, one considers the response variable, y, to be a positive vector with a conditional Dirichlet distribution, y | X We introduce a new method for estimating the parameters of the Dirichlet Covariate Model given a linear model on X, and also propose a Bayesian model selection approach. We present some numerical results which suggest that our proposals are more stable and robust than traditional approaches.
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  23.  27
    Categorization as nonparametric Bayesian density estimation.Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini & Daniel J. Navarro - 2008 - In Nick Chater & Mike Oaksford, The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press.
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  24. Categorization as nonparametric Bayesian density estimation.Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini & Navarro & J. Daniel - 2008 - In Nick Chater & Mike Oaksford, The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press.
     
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  25.  18
    Estimation for Akshaya Failure Model with Competing Risks under Progressive Censoring Scheme with Analyzing of Thymic Lymphoma of Mice Application.Tahani A. Abushal, Jitendra Kumar, Abdisalam Hassan Muse & Ahlam H. Tolba - 2022 - Complexity 2022:1-27.
    In several experiments of survival analysis, the cause of death or failure of any subject may be characterized by more than one cause. Since the cause of failure may be dependent or independent, in this work, we discuss the competing risk lifetime model under progressive type-II censored where the removal follows a binomial distribution. We consider the Akshaya lifetime failure model under independent causes and the number of subjects removed at every failure time when the removal follows the binomial distribution (...)
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  26.  90
    On estimation of functional causal models : general results and application to the post-nonlinear causal model.Kun Zhang, Zhikun Wang, Jiji Zhang & Bernhard Scholkopf - unknown
    Compared to constraint-based causal discovery, causal discovery based on functional causal models is able to identify the whole causal model under appropriate assumptions [Shimizu et al. 2006; Hoyer et al. 2009; Zhang and Hyvärinen 2009b]. Functional causal models represent the effect as a function of the direct causes together with an independent noise term. Examples include the linear non-Gaussian acyclic model, nonlinear additive noise model, and post-nonlinear model. Currently, there are two ways to estimate the parameters in the models: dependence (...)
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  27.  10
    Query efficient posterior estimation in scientific experiments via Bayesian active learning.Kirthevasan Kandasamy, Jeff Schneider & Barnabás Póczos - 2017 - Artificial Intelligence 243:45-56.
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  28.  23
    Estimation for Parameters of Life of the Marshall-Olkin Generalized-Exponential Distribution Using Progressive Type-II Censored Data.Ahmed Elshahhat, Abdisalam Hassan Muse, Omer Mohamed Egeh & Berihan R. Elemary - 2022 - Complexity 2022:1-36.
    A new three-parameter extension of the generalized-exponential distribution, which has various hazard rates that can be increasing, decreasing, bathtub, or inverted tub, known as the Marshall-Olkin generalized-exponential distribution has been considered. So, this article addresses the problem of estimating the unknown parameters and survival characteristics of the three-parameter MOGE lifetime distribution when the sample is obtained from progressive type-II censoring via maximum likelihood and Bayesian approaches. Making use of the s-normality of classical estimators, two types of approximate confidence intervals (...)
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  29. A Comparison of Penalized Maximum Likelihood Estimation and Markov Chain Monte Carlo Techniques for Estimating Confirmatory Factor Analysis Models With Small Sample Sizes.Oliver Lüdtke, Esther Ulitzsch & Alexander Robitzsch - 2021 - Frontiers in Psychology 12.
    With small to modest sample sizes and complex models, maximum likelihood estimation of confirmatory factor analysis models can show serious estimation problems such as non-convergence or parameter estimates outside the admissible parameter space. In this article, we distinguish different Bayesian estimators that can be used to stabilize the parameter estimates of a CFA: the mode of the joint posterior distribution that is obtained from penalized maximum likelihood estimation, and the mean, median, or mode of the marginal (...)
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  30.  38
    Marshall–Olkin Alpha Power Weibull Distribution: Different Methods of Estimation Based on Type-I and Type-II Censoring.Ehab M. Almetwally, Mohamed A. H. Sabry, Randa Alharbi, Dalia Alnagar, Sh A. M. Mubarak & E. H. Hafez - 2021 - Complexity 2021:1-18.
    This paper introduces the new novel four-parameter Weibull distribution named as the Marshall–Olkin alpha power Weibull distribution. Some statistical properties of the distribution are examined. Based on Type-I censored and Type-II censored samples, maximum likelihood estimation, maximum product spacing, and Bayesian estimation for the MOAPW distribution parameters are discussed. Numerical analysis using real data sets and Monte Carlo simulation are accomplished to compare various estimation methods. This novel model’s supremacy upon some famous distributions is explained using (...)
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  31.  24
    The Weibull Generalized Exponential Distribution with Censored Sample: Estimation and Application on Real Data.Hisham M. Almongy, Ehab M. Almetwally, Randa Alharbi, Dalia Alnagar, E. H. Hafez & Marwa M. Mohie El-Din - 2021 - Complexity 2021 (1):6653534.
    This paper is concerned with the estimation of the Weibull generalized exponential distribution parameters based on the adaptive Type-II progressive censored sample. Maximum likelihood estimation, maximum product spacing, and Bayesian estimation based on Markov chain Monte Carlo methods have been determined to find the best estimation method. The Monte Carlo simulation is used to compare the three methods of estimation based on the ATIIP-censored sample, and also, we made a bootstrap confidence interval estimation. (...)
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  32.  37
    From Data to Causes III: Bayesian Priors for General Cross-Lagged Panel Models (GCLM).Michael J. Zyphur, Ellen L. Hamaker, Louis Tay, Manuel Voelkle, Kristopher J. Preacher, Zhen Zhang, Paul D. Allison, Dean C. Pierides, Peter Koval & Edward F. Diener - 2021 - Frontiers in Psychology 12:612251.
    This article describes some potential uses of Bayesian estimation for time-series and panel data models by incorporating information from prior probabilities (i.e., priors) in addition to observed data. Drawing on econometrics and other literatures we illustrate the use of informative “shrinkage” or “small variance” priors (including so-called “Minnesota priors”) while extending prior work on the general cross-lagged panel model (GCLM). Using a panel dataset of national income and subjective well-being (SWB) we describe three key benefits of these priors. (...)
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  33.  16
    Bayes Optimal Integration of Social and Endogenous Uncertainty in Numerosity Estimation.Tutku Öztel & Fuat Balcı - 2024 - Cognitive Science 48 (4):e13447.
    One of the most prominent social influences on human decision making is conformity, which is even more prominent when the perceptual information is ambiguous. The Bayes optimal solution to this problem entails weighting the relative reliability of cognitive information and perceptual signals in constructing the percept from self‐sourced/endogenous and social sources, respectively. The current study investigated whether humans integrate the statistics (i.e., mean and variance) of endogenous perceptual and social information in a Bayes optimal way while estimating numerosities. Our results (...)
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  34.  25
    Finding the optimal exploration-exploitation trade-off online through Bayesian risk estimation and minimization.Stewart Jamieson, Jonathan P. How & Yogesh Girdhar - 2024 - Artificial Intelligence 330 (C):104096.
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  35.  12
    Probabilistic conflicts in a search algorithm for estimating posterior probabilities in Bayesian networks.David Poole - 1996 - Artificial Intelligence 88 (1-2):69-100.
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  36. Predictive Processing and the Phenomenology of Time Consciousness: A Hierarchical Extension of Rick Grush’s Trajectory Estimation Model.Wanja Wiese - 2017 - Philosophy and Predictive Processing.
    This chapter explores to what extent some core ideas of predictive processing can be applied to the phenomenology of time consciousness. The focus is on the experienced continuity of consciously perceived, temporally extended phenomena (such as enduring processes and successions of events). The main claim is that the hierarchy of representations posited by hierarchical predictive processing models can contribute to a deepened understanding of the continuity of consciousness. Computationally, such models show that sequences of events can be represented as states (...)
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  37.  13
    Dangers of the Defaults: A Tutorial on the Impact of Default Priors When Using Bayesian SEM With Small Samples.Sanne C. Smid & Sonja D. Winter - 2020 - Frontiers in Psychology 11.
    When Bayesian estimation is used to analyze Structural Equation Models, prior distributions need to be specified for all parameters in the model. Many popular software programs offer default prior distributions, which is helpful for novel users and makes Bayesian SEM accessible for a broad audience. However, when the sample size is small, those prior distributions are not always suitable and can lead to untrustworthy results. In this tutorial, we provide a non-technical discussion of the risks associated with (...)
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  38. Cointegration: Bayesian Significance Test Communications in Statistics.Julio Michael Stern, Marcio Alves Diniz & Carlos Alberto de Braganca Pereira - 2012 - Communications in Statistics 41 (19):3562-3574.
    To estimate causal relationships, time series econometricians must be aware of spurious correlation, a problem first mentioned by Yule (1926). To deal with this problem, one can work either with differenced series or multivariate models: VAR (VEC or VECM) models. These models usually include at least one cointegration relation. Although the Bayesian literature on VAR/VEC is quite advanced, Bauwens et al. (1999) highlighted that “the topic of selecting the cointegrating rank has not yet given very useful and convincing results”. (...)
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  39.  30
    Learning curves and bootstrap estimates for inference with Gaussian processes: A statistical mechanics study.Dörthe Malzahn & Manfred Opper - 2003 - Complexity 8 (4):57-63.
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  40. Locating uncertainty in stochastic evolutionary models: divergence time estimation.Charles H. Pence - 2019 - Biology and Philosophy 34 (2):21.
    Philosophers of biology have worked extensively on how we ought best to interpret the probabilities which arise throughout evolutionary theory. In spite of this substantial work, however, much of the debate has remained persistently intractable. I offer the example of Bayesian models of divergence time estimation as a case study in how we might bring further resources from the biological literature to bear on these debates. These models offer us an example in which a number of different sources (...)
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  41.  17
    Adaptive Gaussian Incremental Expectation Stadium Parameter Estimation Algorithm for Sports Video Analysis.Lizhi Geng - 2021 - Complexity 2021:1-10.
    In this paper, we propose an adaptive Gaussian incremental expectation stadium parameter estimation algorithm for sports video analysis and prediction through the study and analysis of sports videos. The features with more discriminative power are selected from the set of positive and negative templates using a feature selection mechanism, and a sparse discriminative model is constructed by combining a confidence value metric strategy. The sparse generative model is constructed by combining L1 regularization and subspace representation, which retains sufficient representational (...)
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  42. Bayesians Commit the Gambler's Fallacy.Kevin Dorst - manuscript
    The gambler’s fallacy is the tendency to expect random processes to switch more often than they actually do—for example, to think that after a string of tails, a heads is more likely. It’s often taken to be evidence for irrationality. It isn’t. Rather, it’s to be expected from a group of Bayesians who begin with causal uncertainty, and then observe unbiased data from an (in fact) statistically independent process. Although they converge toward the truth, they do so in an asymmetric (...)
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  43.  17
    A New Lifetime Distribution: Properties, Copulas, Applications, and Different Classical Estimation Methods.Naif Alotaibi - 2021 - Complexity 2021:1-18.
    A new continuous version of the inverse flexible Weibull model is proposed and studied. Some of its properties such as quantile function, moments and generating functions, incomplete moments, mean deviation, Lorenz and Bonferroni curves, the mean residual life function, the mean inactivity time, and the strong mean inactivity time are derived. The failure rate of the new model can be “increasing-constant,” “bathtub-constant,” “bathtub,” “constant,” “J-HRF,” “upside down bathtub,” “increasing,” “upside down-increasing-constant,” and “upside down.” Different copulas are used for deriving many (...)
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  44.  16
    The Importance of Prior Sensitivity Analysis in Bayesian Statistics: Demonstrations Using an Interactive Shiny App.Sarah Depaoli, Sonja D. Winter & Marieke Visser - 2020 - Frontiers in Psychology 11.
    The current paper highlights a new, interactive Shiny App that can be used to aid in understanding and teaching the important task of conducting a prior sensitivity analysis when implementing Bayesian estimation methods. In this paper, we discuss the importance of examining prior distributions through a sensitivity analysis. We argue that conducting a prior sensitivity analysis is equally important when so-called diffuse priors are implemented as it is with subjective priors. As a proof of concept, we conducted a (...)
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  45.  25
    A Bayesian model of the jumping-to-conclusions bias and its relationship to psychopathology.Nicole Tan, Yiyun Shou, Junwen Chen & Bruce K. Christensen - 2024 - Cognition and Emotion 38 (3):315-331.
    The mechanisms by which delusion and anxiety affect the tendency to make hasty decisions (Jumping-to-Conclusions bias) remain unclear. This paper proposes a Bayesian computational model that explores the assignment of evidence weights as a potential explanation of the Jumping-to-Conclusions bias using the Beads Task. We also investigate the Beads Task as a repeated measure by varying the key aspects of the paradigm. The Bayesian model estimations from two online studies showed that higher delusional ideation promoted reduced belief updating (...)
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  46.  15
    Bayesian Analysis of Aberrant Response and Response Time Data.Zhaoyuan Zhang, Jiwei Zhang & Jing Lu - 2022 - Frontiers in Psychology 13:841372.
    In this article, a highly effective Bayesian sampling algorithm based on auxiliary variables is proposed to analyze aberrant response and response time data. The new algorithm not only avoids the calculation of multidimensional integrals by the marginal maximum likelihood method but also overcomes the dependence of the traditional Metropolis–Hastings algorithm on the tuning parameter in terms of acceptance probability. A simulation study shows that the new algorithm is accurate for parameter estimation under simulation conditions with different numbers of (...)
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  47.  66
    Bayesian rationality for the Wason selection task? A test of optimal data selection theory.Klaus Oberauer, Oliver Wilhelm & Ricardo Rosas Diaz - 1999 - Thinking and Reasoning 5 (2):115 – 144.
    Oaksford and Chater (1994) proposed to analyse the Wason selection task as an inductive instead of a deductive task. Applying Bayesian statistics, they concluded that the cards that participants tend to select are those with the highest expected information gain. Therefore, their choices seem rational from the perspective of optimal data selection. We tested a central prediction from the theory in three experiments: card selection frequencies should be sensitive to the subjective probability of occurrence for individual cards. In Experiment (...)
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  48.  38
    Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation.Antti Kangasrääsiö, Jussi P. P. Jokinen, Antti Oulasvirta, Andrew Howes & Samuel Kaski - 2019 - Cognitive Science 43 (6):e12738.
    This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress of (...)
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  49.  93
    Good fences make for good neighbors but bad science: a review of what improves Bayesian reasoning and why. [REVIEW]Gary L. Brase & W. Trey Hill - 2015 - Frontiers in Psychology 6:133410.
    Bayesian reasoning, defined here as the updating of a posterior probability following new information, has historically been problematic for humans. Classic psychology experiments have tested human Bayesian reasoning through the use of word problems and have evaluated each participant’s performance against the normatively correct answer provided by Bayes’ theorem. The standard finding is of generally poor performance. Over the past two decades, though, progress has been made on how to improve Bayesian reasoning. Most notably, research has demonstrated (...)
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  50.  71
    Bayesian statistics and biased procedures.Ronald N. Giere - 1969 - Synthese 20 (3):371 - 387.
    A comparison of Neyman's theory of interval estimation with the corresponding subjective Bayesian theory of credible intervals shows that the Bayesian approach to the estimation of statistical parameters allows experimental procedures which, from the orthodox objective viewpoint, are clearly biased and clearly inadmissible. This demonstrated methodological difference focuses attention on the key difference in the two general theories, namely, that the orthodox theory is supposed to provide a known average frequency of successful estimates, whereas the (...) account provides only a coherent ordering of degrees of belief and a subsequent maximization of subjective expected utilities. To rebut the charge of allowing biased procedures, the Bayesian must attack the foundations of orthodox, objectivist methods. Two apparently popular avenues of attack are briefly considered and found wanting. The first is that orthodox methods fail to apply to the single case. The second is that orthodox methods are subject to a typical Humean regress. The conclusion is that orthodox objectivist methods remain viable in the face of the subjective Bayesian alternative — at least with respect to the problem of statistical estimation. (shrink)
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