Bayesian statistics with a smile: A resampling-sampling perspective

Hedibert F. Lopes, Nicholas G. Polson, Carlos M. Carvalho

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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 languageEnglish (US)
Pages (from-to)358-371
Number of pages14
JournalBrazilian Journal of Probability and Statistics
Issue number4
StatePublished - Nov 2012
Externally publishedYes


  • Bayesian lasso
  • Gibbs sampling
  • Hierarchical models
  • MCMC

ASJC Scopus subject areas

  • Statistics and Probability


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