Overview
- Quickly progresses from fundamental concepts to advanced modelling techniques
- Provides Stan and Python codes for illustrating concepts
- Presents exercises with solutions integrated into each chapter
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Table of contents (12 chapters)
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Front Matter
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Back Matter
Keywords
About this book
These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian processes and latent factor models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn methods for assessing model fit, and to choose between rival modelling approaches.
Authors and Affiliations
About the author
Professor Nick Heard received his PhD degree from the Department of Mathematics at Imperial College London in 2001 and currently holds the position of Chair in Statistics at Imperial. His research interests include developing statistical models for cyber-security applications, finding community structure in large dynamic networks, clustering and changepoint analysis, in each case using computational Bayesian methods.
Bibliographic Information
Book Title: An Introduction to Bayesian Inference, Methods and Computation
Authors: Nick Heard
DOI: https://doi.org/10.1007/978-3-030-82808-0
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-82807-3Published: 18 October 2021
Softcover ISBN: 978-3-030-82810-3Published: 19 October 2022
eBook ISBN: 978-3-030-82808-0Published: 17 October 2021
Edition Number: 1
Number of Pages: XII, 169
Number of Illustrations: 12 b/w illustrations, 70 illustrations in colour
Topics: Bayesian Inference, Statistics and Computing/Statistics Programs, Statistics, general