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＜電子ブック＞
An Introduction to Bayesian Inference, Methods and Computation / by Nick Heard

版	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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