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Introduction to Bayesian Statistical Inference

Karagiannis, G.P.

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Authors



Contributors

L.J.M. Aslett
Editor

F.P.A. Coolen
Editor

J. De Bock
Editor

Abstract

We present basic concepts of Bayesian statistical inference. We briefly introduce the Bayesian paradigm. We present the conjugate priors; a computational convenient way to quantify prior information for tractable Bayesian statistical analysis. We present tools for parametric and predictive inference, and particularly the design of point estimators, credible sets, and hypothesis tests. These concepts are presented in running examples. Supplementary material is available from GitHub.

Citation

Karagiannis, G. (2022). Introduction to Bayesian Statistical Inference. In L. Aslett, F. Coolen, & J. De Bock (Eds.), Uncertainty in Engineering: Introduction to Methods and Applications (1-13). (1). Springer Verlag. https://doi.org/10.1007/978-3-030-83640-5_1

Online Publication Date Dec 10, 2021
Publication Date 2022
Deposit Date Dec 28, 2021
Publicly Available Date Mar 29, 2024
Publisher Springer Verlag
Pages 1-13
Series Title SpringerBriefs in Statistics
Edition 1
Book Title Uncertainty in Engineering: Introduction to Methods and Applications
Chapter Number 1
ISBN 9783030836399
DOI https://doi.org/10.1007/978-3-030-83640-5_1

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http://creativecommons.org/licenses/by/4.0/

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