We'd like to understand how you use our websites in order to improve them. Register your interest. Implementation of Bayesian methods is complicated in many contexts by the apparent need for specialized numerical integration techniques, unfamiliar to most statistical practitioners. In fact, a shift of focus to a sampling-resampling perspective enables one to carry out Bayesian calculations without recourse to numerical integration. Such an approach is illustrated here in the familiar context of normal means inference problems, with particular focus on implementing analyses with reference priors. This is a preview of subscription content, log in to check access.
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Hedibert F. Lopes , Nicholas G. Polson , and Carlos M. Carvalho More by Hedibert F. Lopes Search this author in:. 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.
Source Braz. Zentralblatt MATH identifier Lopes, Hedibert F. Bayesian statistics with a smile: A resampling—sampling perspective. More by Hedibert F. More by Nicholas G. More by Carlos M. Abstract Article info and citation First page References Abstract In this paper we develop a simulation-based approach to sequential inference in Bayesian statistics. Article information Source Braz. Export citation. Export Cancel.
References Brockwell, A. Sequentially interacting Markov chain Monte Carlo. The Annals of Statistics 38 , — Carlin, B. Inference for nonconjugate Bayesian models using the Gibbs sampler. The Canadian Journal of Statistics 19 , — Carpenter, J. An improved particle filter for non-linear problems. Carvalho, C. Particle learning and smoothing. Statistical Science 25 , 88— Particle learning for general mixtures. Bayesian Analysis 5 , — Chen, R. Mixture Kalman filters. You have access to this content.
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Bayesian statistics without tears: A sampling-resampling perspective
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Smith and Alan E. Smith , Alan E.
Sampling-resampling techniques for the computation of posterior densities in normal means problems