title: Robustness considerations in Bayesian analysis.
abstract: All statistical analyses demand uncertain inputs or assumptions. This is especially true of Bayesian analyses. In addition to the usual concerns about the agreement of the data and model, a Bayesian must contemplate the effect of an uncertain prior specification. The degree to which inferences are robust to changes in the prior is of primary interest. This article discusses some robust techniques that have been suggested in the literature. One goal is to make apparent the relevance of some of these techniques to biostatistical work.
authors.affiliation: Department of Statistics, University of British Columbia, Canada.
authors.forename: P
authors.initials: P
authors.lastname: Gustafson
mesh_terms: D000319:Adrenergic beta-Antagonists / Q000627:therapeutic use; D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D008390:Markov Chains; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D009010:Monte Carlo Method; D016011:Normal Distribution; D011336:Probability
chemical_list: D000319:Adrenergic beta-Antagonists