An introduction to Bayesian inference, methods and computation / Nick Heard.

Unlimited

Authorized users

Can lend chapters, not whole ebooks

An introduction to Bayesian inference, methods and computation / Nick Heard.

9783030828080 (electronic bk.)
3030828085 (electronic bk.)
9783030828073 (print)
3030828077

Cham, Switzerland : Springer, 2021.

English

1 online resource (xii, 169 pages) : illustrations (some color)

10.1007/978-3-030-82808-0 doi

QA279.5

519.5/42

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.

Includes bibliographical references and index.

Access limited to authorized users.

text file
PDF

Online resource; title from PDF title page (SpringerLink, viewed October 22, 2021).

Online Resources > Ebooks
All Resources