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An Introduction to Bayesian Inference, Methods and Computation / by Nick Heard

1st ed. 2021.
出版者 Cham : Springer International Publishing : Imprint: Springer
出版年 2021
本文言語 英語
大きさ XII, 169 p. 82 illus., 70 illus. in color : online resource
著者標目 *Heard, Nick author
SpringerLink (Online service)
件 名 LCSH:Statistics 
LCSH:Mathematical statistics -- Data processing  全ての件名で検索
LCSH:Quantitative research
FREE:Bayesian Inference
FREE:Statistics and Computing
FREE:Data Analysis and Big Data
一般注記 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.
HTTP:URL=https://doi.org/10.1007/978-3-030-82808-0
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Springer eBooks 9783030828080
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EB00238642

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データ種別 電子ブック
分 類 LCC:QA279.5
DC23:519.542
書誌ID 4000140944
ISBN 9783030828080

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