Church: a language for generative models

Noah D. Goodman, Vikash K. Mansinghka, Daniel Roy, Keith Bonawitz, Joshua B. Tenenbaum
Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:220-229, 2008.

Abstract

Formal languages for probabilistic modeling enable re-use, modularity, and descriptive clarity, and can foster generic inference techniques. 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR6-goodman08a, title = {Church: a language for generative models}, author = {Goodman, Noah D. and Mansinghka, Vikash K. and Roy, Daniel and Bonawitz, Keith and Tenenbaum, Joshua B.}, booktitle = {Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence}, pages = {220--229}, year = {2008}, editor = {McAllester, David A. and Myllymäki, Petri}, volume = {R6}, series = {Proceedings of Machine Learning Research}, month = {09--12 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r6/main/assets/goodman08a/goodman08a.pdf}, url = {https://proceedings.mlr.press/r6/goodman08a.html}, abstract = {Formal languages for probabilistic modeling enable re-use, modularity, and descriptive clarity, and can foster generic inference techniques. 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.}, note = {Reissued by PMLR on 09 October 2024.} }
Endnote
%0 Conference Paper %T Church: a language for generative models %A Noah D. Goodman %A Vikash K. Mansinghka %A Daniel Roy %A Keith Bonawitz %A Joshua B. Tenenbaum %B Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2008 %E David A. McAllester %E Petri Myllymäki %F pmlr-vR6-goodman08a %I PMLR %P 220--229 %U https://proceedings.mlr.press/r6/goodman08a.html %V R6 %X Formal languages for probabilistic modeling enable re-use, modularity, and descriptive clarity, and can foster generic inference techniques. 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. %Z Reissued by PMLR on 09 October 2024.
APA
Goodman, N.D., Mansinghka, V.K., Roy, D., Bonawitz, K. & Tenenbaum, J.B.. (2008). Church: a language for generative models. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R6:220-229 Available from https://proceedings.mlr.press/r6/goodman08a.html. Reissued by PMLR on 09 October 2024.

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