An introduction to Bayesian inference, methods and computation / Nick Heard.
2021
QA279.5
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Online Access
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Unlimited
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Can lend chapters, not whole ebooks
Details
Title
An introduction to Bayesian inference, methods and computation / Nick Heard.
Author
Heard, Nicholas, author.
ISBN
9783030828080 (electronic bk.)
3030828085 (electronic bk.)
9783030828073 (print)
3030828077
3030828085 (electronic bk.)
9783030828073 (print)
3030828077
Published
Cham, Switzerland : Springer, 2021.
Language
English
Description
1 online resource (xii, 169 pages) : illustrations (some color)
Other Standard Identifiers
10.1007/978-3-030-82808-0 doi
Call Number
QA279.5
Dewey Decimal Classification
519.5/42
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed October 22, 2021).
Available in Other Form
Introduction to Bayesian inference, methods and computation.
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Online Access
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Online Resources > Ebooks
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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.
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.