Building Apriori Knowledge Into Conclusions Drawn From Simulations

Persi Diaconis, Stanford University (United States)

Simulations rule much of Bayesian(and nonBayesian) practice. If you look at what most of do with the output of a simulation, it’s surprisingly naive; use the mean +-2 s.d. .What happened to Bayes (or modern statistics)? I have found classes of problems, e.g. estimating normalizing constants by sequential importance sampling, where reasonable use of prior information helps: the importance weights can be usefully (and sometimes provably) modeled as mixtures of log normals and this helps. This is joint work with Marc Coram.

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