An Introduction to Bayesian Inference, Methods and Computation

Nick Heard

ISBN 10: 3030828107 / ISBN 13: 9783030828103

Published by Springer Nature Switzerland AG, CH, 2022

About this Item

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.

Seller Inventory # LU-9783030828103

Bibliographic Details

• Title: An Introduction to Bayesian Inference, Methods and Computation
• Publisher: Springer Nature Switzerland AG, CH
• Publication Date: 2022
• Language: English
• ISBN 10: 3030828107
• ISBN 13: 9783030828103
• Binding: Paperback
• Edition: 2021 ed.
• Condition: New

About this title

Synopsis

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.

 

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.