Bayesian Statistical Methods

Idioma: inglés

Editorial: Taylor and Francis Ltd, GB, 2026

1032486325 / 9781032486321

Serie: Libro 112 de 126 - Chapman & Hall/CRC Texts in Statistical Science

Librería: Rarewaves.com USA, London, London, Reino UnidoRarewaves.com USA

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Bayesian Statistical Methods: With Applications to Machine Learning provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. Compared to others, this book is more focused on Bayesian methods applied routinely in practice, including multiple linear regression, mixed effects models and generalized linear models. This second edition includes a new chapter on Bayesian machine learning methods to handle large and complex datasets and several new applications to illustrate the benefits of the Bayesian approach in terms of uncertainty quantification. Readers familiar with only introductory statistics will find this book accessible, as it includes many worked examples with complete R code, and comparisons are presented with analogous frequentist procedures. The book can be used as a one-semester course for advanced undergraduate and graduate students and can be used in courses comprising undergraduate statistics majors, as well as non-statistics graduate students from other disciplines such as engineering, ecology and psychology. In addition to thorough treatment of the basic concepts of Bayesian inferential methods, the book covers many general topics:Advice on selecting prior distributionsComputational methods including Markov chain Monte Carlo (MCMC) samplingModel-comparison and goodness-of-fit measures, including sensitivity to priors.To illustrate the flexibility of the Bayesian approaches for complex data structures, the latter chapters provide case studies covering advanced topics:Handling of missing and censored dataPriors for high-dimensional regression modelsMachine learning models including Bayesian adaptive regression trees and deep learningComputational techniques for large datasetsFrequentist properties of Bayesian methods.The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets and complete data analyses is made available on the book's website.

N° de ref. del artículo LU-9781032486321

Título
Bayesian Statistical Methods
Autor
Brian J. Reich, Sujit K. Ghosh
Editorial
Taylor and Francis Ltd, GB
Año de publicación
2026
Estado
New
Encuadernación
Hardback
Idioma
inglés
ISBN 10
1032486325
ISBN 13
9781032486321
Edición
2ª Edición
Peso del artículo
830 gramos
Serie
Libro 112 de 126: Chapman & Hall/CRC Texts in Statistical Science

Rarewaves.com USA

London, London, Reino Unido

Vendedor de 5 estrellas

Vendedor de AbeBooks desde 11 de junio de 2025

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