Results 21 to 30 of about 164,931 (293)
A comparative review of dimension reduction methods in approximate Bayesian computation [PDF]
, 2013 Approximate Bayesian computation (ABC) methods make use of comparisons between simulated and observed summary statistics to overcome the problem of computationally intractable likelihood functions.
Prangle, D. +6 more
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Efficient utility-based clustering over high dimensional partition spaces [PDF]
, 2009 Because of the huge number of partitions of even a moderately sized dataset, even when Bayes factors have a closed form, in model-based clustering a comprehensive search for the highest scoring (MAP) partition is usually impossible.
Smith, JQ +9 more
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Power of Probability in Psychometrics. Review of the book “Bayesian Psychometric Modeling“
Вопросы образования, 2023 The emergence and development of Bayesian psychometrics is a result of psychometrics' desire to reduce measurement error. This book is the first to present a systematic description of the Bayesian approach in psychometric research.
Ирина Угланова
doaj +1 more source
Journal of Applied Statistics, 2013 Classical statistics involves ways to test hypotheses and estimate confidence intervals. Bayesian statistics involves methods to calculate probabilities associated with your hypotheses. The result is a posterior distribution that combines information from your data with prior beliefs.
James B. Elsner, Thomas H. Jagger
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Estimation Of Parameters And Selection Of Models Applied To Population Balance Dynamics Via Approximate Bayesian Computational [PDF]
Journal of Heat and Mass Transfer Research, 2022 Population balance models mathematically describe the particle size distribution based on modeling physical phenomena that influence the distribution, such as aggregation, growth, and breakage.
Carlos Moura +4 more
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Learning Summary Statistics for Bayesian Inference with Autoencoders
SciPost Physics Core, 2022 For stochastic models with intractable likelihood functions, approximate Bayesian computation offers a way of approximating the true posterior through repeated comparisons of observations with simulated model outputs in terms of a small set of summary
Carlo Albert, Simone Ulzega, Firat Ozdemir, Fernando Perez-Cruz, Antonietta Mira
doaj +1 more source
, 2021 Abstract The chapter “Bayesian Statistics” gives a brief overview of the Bayesian approach to statistical analysis. It starts off by examining the difference between frequentist statistics and Bayesian statistics. Next, it introduces Bayes’ theorem and explains how the theorem is used in statistics and model selection, with the ...
Göran Kauermann +2 more
openaire +2 more sources
Bayesian Nonparametric Weighted Sampling Inference [PDF]
, 2021 It has historically been a challenge to perform Bayesian inference in a design-based survey context. The present paper develops a Bayesian model for sampling inference in the presence of inverse-probability weights.
Gelman, Andrew +2 more
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Why Bayesian Ideas Should Be Introduced in the Statistics Curricula and How to Do So
Journal of Statistics Education, 2020 While computing has become an important part of the statistics field, course offerings are still influenced by a legacy of mathematically centric thinking.
Andrew Hoegh
doaj +1 more source
Approximation of Bayesian predictive p-values with\ud regression ABC [PDF]
, 2018 In the Bayesian framework a standard approach to model criticism is to compare some function of the observed data to a reference predictive distribution.
Mengersen, Kerrie +7 more
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