Introduction to Bayesian Statistical Inference

Karagiannis, G.P.

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: Jan 4, 2022
• 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
• Public URL: https://durham-repository.worktribe.com/output/1622956