Results 21 to 30 of about 2,194,170 (284)
Statistical Information: A Bayesian Perspective [PDF]
Entropy, 2011 We explore the meaning of information about quantities of interest. Our approach is divided in two scenarios: the analysis of observations and the planning of an experiment. First, we review the Sufficiency, Conditionality and Likelihood principles and how they relate to trivial experiments.
Rafael Bassi Stern +1 more
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, 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
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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
Systematic search of Bayesian statistics in the field of psychotraumatology. [PDF]
Eur J Psychotraumatol, 2017 In many different disciplines there is a recent increase in interest of Bayesian analysis. Bayesian methods implement Bayes' theorem, which states that prior beliefs are updated with data, and this process produces updated beliefs about model parameters.
van de Schoot R, Schalken N, Olff M.
europepmc +3 more sources
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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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
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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
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A Bayesian Statistics Course for Undergraduates: Bayesian Thinking, Computing, and Research
Journal of Statistics Education, 2020 We propose a semester-long Bayesian statistics course for undergraduate students with calculus and probability background. We cultivate students’ Bayesian thinking with Bayesian methods applied to real data problems. We leverage modern Bayesian computing
Jingchen Hu
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Reporting Standards for Bayesian Network Modelling
EntropyReproducibility is a key measure of the veracity of a modelling result or finding. In other research areas, notably in medicine, reproducibility is supported by mandating the inclusion of an agreed set of details into every research publication ...
Martine J. Barons +3 more
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