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.
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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. Gelfand Published Mathematics, Engineering. Abstract Even to the initiated, statistical calculations based on Bayes's Theorem can be daunting because of the numerical integrations required in all but the simplest applications. Moreover, from a teaching perspective, introductions to Bayesian statistics—if they are given at all—are circumscribed by these apparent calculational difficulties.
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Weiss Mathematics Steel Economics, Computer Science Bayesian inference and the parametric bootstrap. Graphical tools for the examination of high-dimensional functions obtained as the result of Bayesian analysis W.
Kaye Computer Science References Publications referenced by this paper. The implementation of the bayesian paradigm Adrian F. Smith , Allan Skene , J. Shaw , John C. Naylor , Michael Dransfield Mathematics Progress with numerical and graphical methods for practical Bayesian statistics Adrian F.
Sampling-resampling techniques for the computation of posterior densities in normal means problems
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.
Bayesian statistics without tears: A sampling-resampling perspective