2008-07-09-goodman08a.md

abstract	title	year	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	note	address	container-title	volume	genre	issued	pdf	extras
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.	Church: a language for generative models	2008	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	goodman08a	0	Church: a language for generative models	220	229	220-229	220	false	Goodman, Noah D. and Mansinghka, Vikash K. and Roy, Daniel and Bonawitz, Keith and Tenenbaum, Joshua B.	given family Noah D. Goodman given family Vikash K. Mansinghka given family Daniel Roy given family Keith Bonawitz given family Joshua B. Tenenbaum	2008-07-09	Reissued by PMLR on 09 October 2024.		Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence	R6	inproceedings	date-parts 2008 7 9	https://raw.githubusercontent.com/mlresearch/r6/main/assets/goodman08a/goodman08a.pdf