Abstract Details
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Activity Number:
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61
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #309471 |
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Title:
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Posteriors and Conjugacy for General Nonparametric Bayesian Priors
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Author(s):
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Tamara Broderick*+ and Michael I. Jordan
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Companies:
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University of California Berkeley and UC Berkeley
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Keywords:
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conjugacy ;
Bayesian nonparametrics
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Abstract:
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So far in Bayesian nonparametrics, calculating posteriors and establishing conjugacy has been performed on a case-by-case basis; for instance, Ferguson (1973) proved that the Dirichlet process is conjugate to the infinite multinomial likelihood, and the work of Hjort (1990), Kim (1999), and Thibaux and Jordan (2007) established that the beta process is conjugate to the Bernoulli process. We provide more general results concerning the posterior distributions for nonparametric Bayesian priors; in particular, we do not limit to a single stochastic process analogue of a classical one-dimensional distribution.
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Authors who are presenting talks have a * after their name.
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