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A stochastic programming perspective on nonparametric Bayes

A stochastic programming perspective on nonparametric Bayes

This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 2008. We use Church, a Turing-universal language for stochastic generative processes and the probability distributions they induce, to study and extend several objects in nonparametric Bayesian statistics. We connect exchangeability and de Finetti measures with notions of purity and closures from functional programming. We exploit delayed evaluation to provide finite, machine-executable representations for various nonparametric Bayesian objects. We relate common uses of the Dirichlet process to a stochastic generalization of memoization, and use this abstraction to compactly describe and extend several nonparametric models. Finally, we briefly discuss issues of computability and inference.

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