|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
When Bayesians Can't Handle the Truth
If you have a question about this talk, please contact Richard Samworth.
There are elegant results on the consistency of Bayesian updating for well-specified models facing IID or Markovian data, but both completely correct models and fully observed states are vanishingly rare. In this talk, I give conditions for posterior convergence that hold when the prior excludes the truth, which may have complex dependencies. The key dynamical assumption is the convergence of time-averaged log likelihoods (Shannon-McMillan-Breiman property). The main statistical assumption is a building into the prior a form of capacity control related to the method of sieves. With these, I derive posterior and predictive convergence, and a large deviations principle for the posterior, even in infinite-dimensional hypothesis spaces; and clarify role of the prior and of model averaging as regularization devices.
This talk is part of the Statistics series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsEdwina Currie: Lies, damned lies and politicians Creative Research at Museum of Archaeology & Anthropology Cellular Medicine Seminar Series
Other talksThermostabilisation of G protein-coupled receptors to facilitate structure determination "Ireland grows up: the debate about how to celebrate the centenary of the 1916 Rising" Quantification of near field blast loading Day 1 - Corporate Finance Theory Symposium September 2015 Queues don’t matter when you can Jump them! TBA