Bayesian Statistical Methods (Hardcover)

Langue : anglais

Edité par Taylor & Francis Ltd, 2026

1032486325 / 9781032486321

Série : Livre 112 sur 126 - Chapman & Hall/CRC Texts in Statistical Science

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Hardcover. 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 books website. This book provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. Compared to others, it is more focused on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

N° de réf. du vendeur 9781032486321

Titre
Bayesian Statistical Methods (Hardcover)
Auteur
Brian J. Reich
Éditeur
Taylor & Francis Ltd
Année de publication
2026
État de l'article
new
Reliure
Hardcover
Langue
anglais
ISBN à 10 chiffres
1032486325
ISBN à 13 chiffres
9781032486321
Édition
2ème Édition
Série
Livre 112 sur 126: Chapman & Hall/CRC Texts in Statistical Science

CitiRetail

Stevenage, Royaume-Uni

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Vendeur AbeBooks depuis 29 juin 2022

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