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.

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  1. Belle, Vaishak; De Raedt, Luc: Semiring programming: a semantic framework for generalized sum product problems (2020)
  2. Cohen, William; Yang, Fan; Mazaitis, Kathryn Rivard: TensorLog: a probabilistic database implemented using deep-learning infrastructure (2020)
  3. Dylus, Sandra; Christiansen, Jan; Teegen, Finn: Implementing a library for probabilistic programming using non-strict non-determinism (2020)
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  5. Bingham, Eli; Chen, Jonathan P.; Jankowiak, Martin; Obermeyer, Fritz; Pradhan, Neeraj; Karaletsos, Theofanis; Singh, Rohit; Szerlip, Paul; Horsfall, Paul; Goodman, Noah D.: Pyro: deep universal probabilistic programming (2019)
  6. Bonilla, Edwin V.; Krauth, Karl; Dezfouli, Amir: Generic inference in latent Gaussian process models (2019)
  7. Cozman, Fabio Gagliardi; Mauá, Denis Deratani: The finite model theory of Bayesian network specifications: descriptive complexity and zero/one laws (2019)
  8. Sandra Dylus, Jan Christiansen, Finn Teegen: Implementing a Library for Probabilistic Programming using Non-strict Non-determinism (2019) arXiv
  9. Abdallah, Samer: PRISM revisited: declarative implementation of a probabilistic programming language using multi-prompt delimited control (2018)
  10. Angelopoulos, Nicos; Cussens, James: Distributional logic programming for Bayesian knowledge representation (2017)
  11. Bach, Stephen H.; Broecheler, Matthias; Huang, Bert; Getoor, Lise: Hinge-loss Markov random fields and probabilistic soft logic (2017)
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  14. Cho, Kenta; Jacobs, Bart: The EfProb library for probabilistic calculations (2017)
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  16. Culpepper, Ryan; Cobb, Andrew: Contextual equivalence for probabilistic programs with continuous random variables and scoring (2017)
  17. Dal Lago, Ugo; Grellois, Charles: Probabilistic termination by monadic affine sized typing (2017)
  18. Kucukelbir, Alp; Tran, Dustin; Ranganath, Rajesh; Gelman, Andrew; Blei, David M.: Automatic differentiation variational inference (2017)
  19. 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)
  20. Nitti, Davide; Belle, Vaishak; De Laet, Tinne; De Raedt, Luc: Planning in hybrid relational MDPs (2017)

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