Download Citation on ResearchGate | Bayesian Statistics Without Tears: A Sampling-Resampling Perspective | Even to the initiated, statistical calculations. Here we offer a straightforward samplingresampling perspective on Bayesian inference, which has both pedagogic appeal and suggests easily implemented. Bayesian statistics without tears: A sampling-resampling perspective (The American statistician) [A. F. M Smith] on *FREE* shipping on qualifying.
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LopesNicholas G. Polsonand Carlos M. Carvalho More by Hedibert F.
Bayesian Statistics Without Tears : A Sampling-Resampling Perspective
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 perspetive sequential nature of Bayes theorem. Predictive inferences are a direct byproduct of our analysis as are marginal likelihoods for model assessment.
Bayesian Statistics Without Tears : A Sampling-Resampling Perspective – Semantic Scholar
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.
Permanent link to this document https: Zentralblatt MATH identifier Bayesian statistics with a smile: More by Hedibert F. Lopes Search this author in: Google Scholar Project Euclid.
Lopes , Polson , Carvalho : Bayesian statistics with a smile: A resampling–sampling perspective
More by Nicholas G. Polson Search this author in: More by Carlos M. Carvalho Search this author in: 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. Dates First available in Project Euclid: Download Email Please enter a valid email address. Sequentially interacting Markov chain Monte Carlo. The Annals of Statistics 38 statitsics, — Inference for nonconjugate Bayesian models using the Gibbs sampler. The Canadian Gayesian of Statistics 19— An improved particle filter for non-linear problems.
Particle learning and smoothing. Statistical Science 2588— Particle learning for general mixtures. Bayesian Analysis perspeective— MR Digital Object Identifier: You have access to this content.
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