Abstract
In this paper we develop a simulation-based approach to sequential inference in Bayesian statistics. Our resampling-sampling perspective provides draws from posterior distributions of interest by exploiting the sequential nature of Bayes theorem. Predictive inferences are a direct byproduct of our analysis as are marginal likelihoods for model assessment. We illustrate our approach in a hierarchical normal-means model and in a sequential version of Bayesian lasso. This approach provides a simple yet powerful framework for the construction of alternative posterior sampling strategies for a variety of commonly used models.
Original language | English (US) |
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Pages (from-to) | 358-371 |
Number of pages | 14 |
Journal | Brazilian Journal of Probability and Statistics |
Volume | 26 |
Issue number | 4 |
DOIs | |
State | Published - Nov 2012 |
Externally published | Yes |
Keywords
- ANOVA
- Bayesian lasso
- Gibbs sampling
- Hierarchical models
- MCMC
ASJC Scopus subject areas
- Statistics and Probability