Abstract
The use of Markov chain Monte Carlo (MCMC) methods for estimating hierarchical models involves complex data structures and is often described as a revolutionary development. An intermediate-level treatment of Bayesian hierarchical models and their applications, Applied Bayesian Hierarchical Methods demonstrates the advantages of a Bayesian approach
Keywords
Bayesian probabilityComputer scienceArtificial intelligence
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Publication Info
- Year
- 2010
- Type
- book
- Citations
- 188
- Access
- Closed
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Cite This
Peter Congdon
(2010).
Applied Bayesian Hierarchical Methods.
.
https://doi.org/10.1201/9781584887218
Identifiers
- DOI
- 10.1201/9781584887218