Church: a language for generative models

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.

• Type: Conference
• Year: 2008
• Where: UAI
• Authors: Noah Goodman, Vikash K. Mansinghka, Daniel M. Roy, Keith Bonawitz, Joshua B. Tenenbaum
• Keywords: Artificial Intelligence, Generic Inference Techniques, Modeling Enable Re-use, Stochastic Generative Processes, UAI 2008