Bayesian statistical methods (Record no. 210233)

MARC details
000 -LEADER
fixed length control field 05993cam a22002058i 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 190125s2019 flu 000 0 eng
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780815378648
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.542
Item number R3B2
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Reich, Brian J
9 (RLIN) 341685
245 10 - TITLE STATEMENT
Title Bayesian statistical methods
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Boca Raton
Name of publisher, distributor, etc. Chapman and Hall/CRC
Date of publication, distribution, etc. 2019
300 ## - PHYSICAL DESCRIPTION
Extent xii, 275 p.
440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE
Title Chapman and hall/CRC texts in statistical science series
9 (RLIN) 342358
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Table of Contents 1. Basics of Bayesian Inference Probability background Univariate distributions Discrete distributions Continuous distributions Multivariate distributions Marginal and conditional distributions Bayes' Rule Discrete example of Bayes' Rule Continuous example of Bayes' Rule Introduction to Bayesian inference Summarizing the posterior Point estimation Univariate posteriors Multivariate posteriors The posterior predictive distribution Exercises 2. From Prior Information to Posterior Inference Conjugate Priors Beta-binomial model for a proportion Poisson-gamma model for a rate Normal-normal model for a mean Normal-inverse gamma model for a variance Natural conjugate priors Normal-normal model for a mean vector Normal-inverse Wishart model for a covariance matrix Mixtures of conjugate priors Improper Priors Objective Priors Jeffreys prior Reference Priors Maximum Entropy Priors Empirical Bayes Penalized complexity priors Exercises 3. Computational approaches Deterministic methods Maximum a posteriori estimation Numerical integration Bayesian Central Limit Theorem (CLT) Markov Chain Monte Carlo (MCMC) methods Gibbs sampling Metropolis-Hastings (MH) sampling MCMC software options in R Diagnosing and improving convergence Selecting initial values Convergence diagnostics Improving convergence Dealing with large datasets Exercises 4. Linear models Analysis of normal means One-sample/paired analysis Comparison of two normal means Linear regression Jeffreys prior Gaussian prior Continuous shrinkage priors Predictions Example: Factors that affect a home's microbiome Generalized linear models Binary data Count data Example: Logistic regression for NBA clutch free throws Example: Beta regression for microbiome data Random effects Flexible linear models Nonparametric regression Heteroskedastic models Non-Gaussian error models Linear models with correlated data Exercises 5. Model selection and diagnostics Cross validation Hypothesis testing and Bayes factors Stochastic search variable selection Bayesian model averaging Model selection criteria Goodness-of-fit checks Exercises 6. Case studies using hierarchical modeling Overview of hierarchical modeling Case study: Species distribution mapping via data fusion Case study: Tyrannosaurid growth curves Case study: Marathon analysis with missing data 7. Statistical properties of Bayesian methods Decision theory Frequentist properties Bias-variance tradeoff Asymptotics Simulation studies Exercises Appendices Probability distributions Univariate discrete Multivariate discrete Univariate continuous Multivariate continuous List of conjugacy pairs Derivations Normal-normal model for a mean Normal-normal model for a mean vector Normal-inverse Wishart model for a covariance matrix Jeffreys' prior for a normal model Jeffreys' prior for multiple linear regression Convergence of the Gibbs sampler Marginal distribution of a normal mean under Jeffreys' prior Marginal posterior of the regression coefficients under Jeffreys prior Proof of posterior consistency Computational algorithms Integrated nested Laplace approximation (INLA) Metropolis-adjusted Langevin algorithm Hamiltonian Monte Carlo (HMC) Delayed Rejection and Adaptive Metropolis Slice sampling Software comparison Example - Simple linear regression Example - Random slopes model
520 ## - SUMMARY, ETC.
Summary, etc. Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures. In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics: Advice on selecting prior distributions Computational methods including Markov chain Monte Carlo (MCMC) Model-comparison and goodness-of-fit measures, including sensitivity to priors Frequentist properties of Bayesian methods Case studies covering advanced topics illustrate the flexibility of the Bayesian approach: Semiparametric regression Handling of missing data using predictive distributions Priors for high-dimensional regression models Computational techniques for large datasets Spatial data analysis 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 are available on the book's website. Brian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the Journal of Agricultural, Biological, and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award. Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Bayesian statistical decision theory
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Mathematical analysis - Problems, exercises, etc
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Ghosh, Sujit K
9 (RLIN) 341688
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     Non-fiction Nagpur Nagpur On Display 26/11/2019 7 3655.07   519.542 R3B2 IIMN-002072 26/11/2019 5375.10 26/11/2019 Book

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