Uncertainty in Artificial Intelligence
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Church: a language for generative models
Noah Goodman, Vikash Mansinghka, Daniel Roy, Keith Bonawitz, Joshua Tenenbaum
We introduce Church, a universal language for describing stochastic generative processes. Church is based on the Lisp model of lambda calculus, containing a pure Lisp as its deterministic subset. The semantics of Church is defined in terms of evaluation histories and conditional distributions on such histories. Church also includes a novel language construct, the stochastic memoizer, which enables simple description of many complex non-parametric models. We illustrate language features through several examples, including: a generalized Bayes net in which parameters cluster over trials, infinite PCFGs, planning by inference, and various non-parametric clustering models. Finally, we show how to implement query on any Church program, exactly and approximately, using Monte Carlo techniques.
Pages: 220-229
PS Link:
PDF Link: /papers/08/p220-goodman.pdf
AUTHOR = "Noah Goodman and Vikash Mansinghka and Daniel Roy and Keith Bonawitz and Joshua Tenenbaum",
TITLE = "Church: a language for generative models",
BOOKTITLE = "Proceedings of the Twenty-Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-08)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2008",
PAGES = "220--229"

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