| Description |
1 online resource (xi, 579 pages) : illustrations |
| Contents |
Bayesian methods for complex data: estimation and inference -- Computing options and strategies -- Model fit, comparison, and checking -- Borrowing strength estimation for exchangeable units -- Structured priors recognizing similarity over time and space -- Regression techniques using hierarchical priors -- Multilevel models -- Regression for causal effects with observational data -- Hierarchical models for panel data -- Multivariate priors, with a focus on factor and structural equation models -- Survival and event history models -- Hierarchical methods for nonlinear regression |
| Summary |
"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"-- Provided by publisher |
| Notes |
Revised edition of: Applied Bayesian hierarchical methods. c2010 |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Description based on online resource; title from PDF title page (Site, viewed (04/14/21) |
| Subject |
Multilevel models (Statistics)
|
|
Bayesian statistical decision theory.
|
|
MATHEMATICS -- Probability & Statistics -- General.
|
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Multilevel models (Statistics)
|
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Bayesian statistical decision theory
|
| Form |
Electronic book
|
| LC no. |
2019024163 |
| ISBN |
9780429113352 |
|
0429113358 |
|
9781498785914 |
|
1498785913 |
|
9780429532900 |
|
0429532903 |
|
9780429547607 |
|
0429547609 |
|
