Bayesian Hierarchical Models

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Item specifics

Condition
Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
subject_code
PBT
gpsr_safety_attestation
true
target_audience
Professional and scholarly
is_adult_product
false
edition_number
2
binding
paperback
edition
2
MPN
N/A
batteries_required
false
manufacturer
Chapman and Hall/CRC
Brand
N/A
number_of_items
1
pages
592
genre
Probability and statistics
publication_date
2021-09-30T00:00:01Z
unspsc_code
55101500
batteries_included
false
ISBN
9781032177151
Category

About this product

Product Identifiers

Publisher
CRC Press LLC
ISBN-10
1032177152
ISBN-13
9781032177151
eBay Product ID (ePID)
27064610426

Product Key Features

Number of Pages
592 Pages
Language
English
Publication Name
Bayesian Hierarchical Models : with Applications Using R, Second Edition
Publication Year
2021
Subject
Mathematical & Statistical Software, Probability & Statistics / General, General
Type
Textbook
Subject Area
Mathematics, Computers
Author
Peter D. Congdon
Format
Trade Paperback

Dimensions

Item Weight
16 Oz
Item Length
10 in
Item Width
7 in

Additional Product Features

Edition Number
2
Intended Audience
Scholarly & Professional
Reviews
"...The material covered in the almost 600 pages is broad, rich, and presented in a dense and conciseway. There is a notable emphasis on longitudinal models, spatial applications as well as structural equations models, which seems natural given the focus on hierarchicalmodels...The readership that will benefit most from the book might be statisticians with intermediateor advanced-level expertise in Bayesian statistics and at least some basic experience in the software implementation of Bayesian modeling techniques. The second edition is particularly worthwhile since it catches up with the computational developments of the last decade. Overall, the book nicely illustrates the richness and the flexibility of hierarchical modeling options within the Bayesian framework." - Christian Stock, Biometrical Journal, October 2020
Illustrated
Yes
Table Of Content
Contents Preface 1. Bayesian Methods for Complex Data: Estimation and Inference 2. Bayesian Analysis Options in R, and Coding for BUGS, JAGS, and Stan 3. Model Fit, Comparison, and Checking 4. Borrowing Strength via Hierarchical Estimation 5. Time Structured Priors 6. Representing Spatial Dependence 7. Regression Techniques Using Hierarchical Priors 8. Bayesian Multilevel Models 9. Factor Analysis, Structural Equation Models, and Multivariate Priors 10. Hierarchical Models for Longitudinal Data 11. Survival and Event History Models 12. Hierarchical Methods for Nonlinear and Quantile Regression
Synopsis
An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods. The new edition is a revision of the book Applied Bayesian Hierarchical Methods . It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples. The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities. Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book's website, This is the second edition of a book on applied Bayesian modelling using WinBUGS. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies.
LC Classification Number
QA279.5

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