Bayesian Statistics without Tears: A Sampling–Resampling Perspective

Abstract

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. Here we offer a straightforward sampling-resampling perspective on Bayesian inference, which has both pedagogic appeal and suggests easily implemented calculation strategies. Key Words: Bayesian inferenceExploratory data analysisGraphical methodsInfluencePosterior distributionPredictionPrior distributionRandom variate generationSampling-resampling techniquesSensitivity analysisWeighted bootstrap