Church

Church: A language for generative models. 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.


References in zbMATH (referenced in 41 articles )

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  1. Sandra Dylus, Jan Christiansen, Finn Teegen: Implementing a Library for Probabilistic Programming using Non-strict Non-determinism (2019) arXiv
  2. Abdallah, Samer: PRISM revisited: declarative implementation of a probabilistic programming language using multi-prompt delimited control (2018)
  3. Angelopoulos, Nicos; Cussens, James: Distributional logic programming for Bayesian knowledge representation (2017)
  4. Bach, Stephen H.; Broecheler, Matthias; Huang, Bert; Getoor, Lise: Hinge-loss Markov random fields and probabilistic soft logic (2017)
  5. Breuvart, Flavien; Dal Lago, Ugo; Herrou, Agathe: On higher-order probabilistic subrecursion (2017)
  6. Cho, Kenta; Jacobs, Bart: The EfProb library for probabilistic calculations (2017)
  7. Crubillé, Raphaëlle; Dal Lago, Ugo: Metric reasoning about (\lambda)-terms: the general case (2017)
  8. Culpepper, Ryan; Cobb, Andrew: Contextual equivalence for probabilistic programs with continuous random variables and scoring (2017)
  9. Dal Lago, Ugo; Grellois, Charles: Probabilistic termination by monadic affine sized typing (2017)
  10. Kucukelbir, Alp; Tran, Dustin; Ranganath, Rajesh; Gelman, Andrew; Blei, David M.: Automatic differentiation variational inference (2017)
  11. Lampropoulos, Leonidas; Gallois-Wong, Diane; Hriţcu, Cătălin; Hughes, John; Pierce, Benjamin C.; Xia, Li-yao: Beginner’s Luck: a language for property-based generators (2017)
  12. Nitti, Davide; Belle, Vaishak; De Laet, Tinne; De Raedt, Luc: Planning in hybrid relational mdps (2017)
  13. Staton, Sam: Commutative semantics for probabilistic programming (2017)
  14. Teso, Stefano; Sebastiani, Roberto; Passerini, Andrea: Structured learning modulo theories (2017)
  15. Chen, Yutian; Bornn, Luke; de Freitas, Nando; Eskelin, Mareija; Fang, Jing; Welling, Max: Herded Gibbs sampling (2016)
  16. Davis, Ernest; Marcus, Gary: The scope and limits of simulation in automated reasoning (2016)
  17. Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei: Edward: A library for probabilistic modeling, inference, and criticism (2016) arXiv
  18. Huang, Daniel; Morrisett, Greg: An application of computable distributions to the semantics of probabilistic programming languages (2016)
  19. Kiselyov, Oleg: Probabilistic programming language and its incremental evaluation (2016)
  20. Narayanan, Praveen; Carette, Jacques; Romano, Wren; Shan, Chung-chieh; Zinkov, Robert: Probabilistic inference by program transformation in Hakaru (system description) (2016)

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