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An Introduction to Bayesian Inference, Methods and Computation

Record Type:	Language materials, printed : Monograph/item
Title/Author:	An Introduction to Bayesian Inference, Methods and Computation/ by Nick Heard.
Author:	Heard, Nick.
Description:	XII, 169 p. 82 illus., 70 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Statistics . -
Online resource:	https://doi.org/10.1007/978-3-030-82808-0
ISBN:	9783030828080

An Introduction to Bayesian Inference, Methods and Computation
Heard, Nick.

An Introduction to Bayesian Inference, Methods and Computation[electronic resource] /by Nick Heard. - 1st ed. 2021. - XII, 169 p. 82 illus., 70 illus. in color.online resource.

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

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

1253516
Statistics .

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

An Introduction to Bayesian Inference, Methods and Computation
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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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