Bayesian analysis with Stata / John Thompson.
Material type:
TextPublication details: Texas : Stata press pub., c2014.Description: xx, 279 p. : illustrations ; 24 cmISBN: - 9781597181419
- 000SA.161 23 T473
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 000SA.161 T473 (Browse shelf(Opens below)) | Available | 136112 |
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| 000SA.161 R569 Approximation methods for efficient learning of Bayesian networks / | 000SA.161 Sa245 Bayesian filtering and smoothing / | 000SA.161 Sc437 Bayesian networks : | 000SA.161 T473 Bayesian analysis with Stata / | 000SA.161 Up65 Current trends in Bayesian methodology with applications / | 000SA.161 V152 Bayesian statistics 9 : | 000SA.16=3 L926 Wahrscheinlichkeiten und schwankungen |
Includes bibliographical references (pages 265-272) and indexes.
1. The problem of priors --
2. Evaluating the posterior --
3. Metropolis-Hastings --
4. Gibbs sampling --
5. Assessing convergence --
6. Validating the Stata code and summarizing the results --
7. Bayesian analysis with Mata --
8. Using WinBUGS for model fitting --
9. Model checking --
10. Model selection --
11. Further case studies --
12. Writing Stata programs for specific Bayesian analysis--
A Standard distributions--
References--
Author index--
Subject index.
The book shows how modern analyses based on Markov chain Monte Carlo (MCMC) methods are implemented in Stata both directly and by passing Stata datasets to OpenBUGS or WinBUGS for computation, allowing Stata’s data management and graphing capability to be used with OpenBUGS/WinBUGS speed and reliability.The book emphasizes practical data analysis from the Bayesian perspective, and hence covers the selection of realistic priors, computational efficiency and speed, the assessment of convergence, the evaluation of models, and the presentation of the results. Every topic is illustrated in detail using real-life examples, mostly drawn from medical research.
The book takes great care in introducing concepts and coding tools incrementally so that there are no steep patches or discontinuities in the learning curve. The book's content helps the user see exactly what computations are done for simple standard models and shows the user how those computations are implemented. Understanding these concepts is important for users because Bayesian analysis lends itself to custom or very complex models, and users must be able to code these themselves.
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