An introduction to Bayesian inference, methods and computation
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 …
Read More ↓Title Statements
- Full title :An introduction to Bayesian inference, methods and computation / Nick Heard.
Summary
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.
Read More ↓Edition Statements
- Publication Date:2021
Publication Statements
- Publisher(s):Springer
- Place of Publication:Cham, Switzerland
- Language:eng
- Publication Date:2021
The Physical Object
- Description:xii, 169 pages : : illustrations ; : 25 cm
Edition Identifiers
- ISBN:3030828077, 9783030828073
- oclc:on1259046207
- LC Classification:QA279.5
- Dewey Decimal:519.5/42
Table of Contents
Uncertainty and Decisions -- Prior and Likelihood Representation -- Graphical Modeling -- Parametric Models -- Computational Inference -- Bayesian Software Packages -- Model choice -- Linear Models -- Nonparametric Models -- Nonparametric Regression -- Clustering and Latent Factor Models -- Conjugate Parametric Models.
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