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.
Bayesian Statistics Without Tears : A Sampling-Resampling Perspective
Carvalho More by Hedibert F. Lopes Search this author samplinb. 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 samplinf 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. 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.
Lopes , Polson , Carvalho : Bayesian statistics with a smile: A resampling–sampling perspective
Sequentially interacting Markov chain Monte Carlo. The Annals of Statistics 38— Inference for perwpective Bayesian models using the Gibbs sampler. The Canadian Journal of Statistics 19— An improved particle filter for non-linear problems.
Particle learning and smoothing. Statistical Science 2588— Sapling learning for general mixtures. Bayesian Analysis 5— MR Digital Object Identifier: You have access to this content.
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