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An introduction to Bayesian inferenc...

Heard, Nick.

An introduction to Bayesian inference, methods and computation

Record Type:	Electronic resources : Monograph/item
Title/Author:	An introduction to Bayesian inference, methods and computationby Nick Heard.
Author:	Heard, Nick.
Published:	Cham :Springer International Publishing :2021.
Description:	xii, 169 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Bayesian statistical decision theory.
Online resource:	https://doi.org/10.1007/978-3-030-82808-0
ISBN:	9783030828080$q(electronic bk.)

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

An introduction to Bayesian inference, methods and computation[electronic resource] /by Nick Heard. - Cham :Springer International Publishing :2021. - xii, 169 p. :ill. (some col.), digital ;24 cm.

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.

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.

ISBN: 9783030828080$q(electronic bk.)

Standard No.: 10.1007/978-3-030-82808-0doiSubjects--Topical Terms:

182005
Bayesian statistical decision theory.

LC Class. No.: QA279.5 / .H43 2021

Dewey Class. No.: 519.542

An introduction to Bayesian inference, methods and computation
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505 0 $a 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.
520 $a 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.
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