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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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
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Change-point model on nonhomogeneous Poisson processes with application in copy number profiling by next-generation DNA sequencing [PDF]
, 2012 We propose a flexible change-point model for inhomogeneous Poisson Processes, which arise naturally from next-generation DNA sequencing, and derive score and generalized likelihood statistics for shifts in intensity functions.
Shen, Jeremy J., Zhang, Nancy R.
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Rejoinder to discussions of "Frequentist coverage of adaptive nonparametric Bayesian credible sets" [PDF]
, 2015 Rejoinder of "Frequentist coverage of adaptive nonparametric Bayesian credible sets" by Szab\'o, van der Vaart and van Zanten [arXiv:1310.4489v5].Comment: Published at http://dx.doi.org/10.1214/15-AOS1270REJ in the Annals of Statistics (http://www ...
Szabó, Botond +2 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
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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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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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Philosophy and the practice of Bayesian statistics [PDF]
, 2010 A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics.
Abbott +138 more
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Improved polygenic prediction by Bayesian multiple regression on summary statistics
Nature Communications, 2019 Accurate prediction of an individual’s phenotype from their DNA sequence is one of the great promises of genomics and precision medicine. We extend a powerful individual-level data Bayesian multiple regression model (BayesR) to one that utilises summary ...
Luke R. Lloyd‐Jones +14 more
semanticscholar +1 more source
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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