Karagiannis, G. P. (2022) 'Introduction to Bayesian Statistical Inference.', in Uncertainty in Engineering: Introduction to Methods and Applications. Cham: Springer, pp. 1-13. SpringerBriefs in Statistics.
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.
| Item Type: | Book chapter |
|---|---|
| Full text: | (VoR) Version of Record Available under License - Creative Commons Attribution 4.0. Download PDF (309Kb) |
| Status: | Peer-reviewed |
| Publisher Web site: | https://doi.org/10.1007/978-3-030-83640-5_1 |
| Publisher statement: | Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. |
| Date accepted: | No date available |
| Date deposited: | 04 January 2022 |
| Date of first online publication: | 10 December 2021 |
| Date first made open access: | 04 January 2022 |
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