Church: a language for generative models
Church: a language for generative models
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发表时间：
2008-07
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通讯作者：
Noah D. Goodman;Vikash K. Mansinghka;Daniel M. Roy;Keith Bonawitz;J. Tenenbaum
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作者：
Noah D. Goodman;Vikash K. Mansinghka;Daniel M. Roy;Keith Bonawitz;J. Tenenbaum
用于概率建模的形式语言支持重用、模块化和描述清晰度，并且可以促进通用推理技术。我们介绍了Church，一种描述随机生成过程的通用语言。Church基于lambda演算的Lisp模型，包含一个纯Lisp作为其确定性子集。Church的语义是根据评价历史和这些历史上的条件分布来定义的。Church还包含了一种新颖的语言结构，随机记忆器，它可以简单地描述许多复杂的非参数模型。我们通过几个例子来说明语言的特征，包括：一个广义贝叶斯网络，其中参数在试验上聚类，无限pcfg，通过推理规划，以及各种非参数聚类模型。最后，我们展示了如何使用蒙特卡罗技术精确和近似地在任何Church程序上实现查询。
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.