Goodman, ND and Mansinghka, VK and Roy, D and Bonawitz, K and Tenenbaum, JB (2008) Church: A language for generative models. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008. pp. 220-229.Full text not available from this repository.
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.
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Cron Job|
|Date Deposited:||15 Dec 2015 13:01|
|Last Modified:||18 Jan 2016 05:54|