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. Chen, Yutian; Bornn, Luke; de Freitas, Nando; Eskelin, Mareija; Fang, Jing; Welling, Max: Herded Gibbs sampling (2016)
  2. Davis, Ernest; Marcus, Gary: The scope and limits of simulation in automated reasoning (2016)
  3. Narayanan, Praveen; Carette, Jacques; Romano, Wren; Shan, Chung-chieh; Zinkov, Robert: Probabilistic inference by program transformation in Hakaru (system description) (2016)
  4. Nitti, Davide; De Laet, Tinne; De Raedt, Luc: Probabilistic logic programming for hybrid relational domains (2016)
  5. Turliuc, Calin Rares; Dickens, Luke; Russo, Alessandra; Broda, Krysia: Probabilistic abductive logic programming using Dirichlet priors (2016)
  6. Vlasselaer, Jonas; van den Broeck, Guy; Kimmig, Angelika; Meert, Wannes; De Raedt, Luc: $T_\mathcalP$-compilation for inference in probabilistic logic programs (2016)
  7. De Raedt, Luc; Kimmig, Angelika: Probabilistic (logic) programming concepts (2015)
  8. Jansen, Nils; Kaminski, Benjamin Lucien; Katoen, Joost-Pieter; Olmedo, Federico; Gretz, Friedrich; McIver, Annabelle: Conditioning in probabilistic programming (2015)
  9. Michels, Steffen; Hommersom, Arjen; Lucas, Peter J.F.; Velikova, Marina: A new probabilistic constraint logic programming language based on a generalised distribution semantics (2015)
  10. Patri, Jean-François; Diard, Julien; Perrier, Pascal: Optimal speech motor control and token-to-token variability: a Bayesian modeling approach (2015)
  11. Borgström, Johannes; Gordon, Andrew D.; Greenberg, Michael; Margetson, James; Van Gael, Jurgen: Measure transformer semantics for Bayesian machine learning (2013)
  12. Gordon, Andrew D.; Aizatulin, Mihhail; Borgstrom, Johannes; Claret, Guillaume; Graepel, Thore; Nori, Aditya V.; Rajamani, Sriram K.; Russo, Claudio: A model-learner pattern for Bayesian reasoning (2013)
  13. Hutter, Marcus; Lloyd, John W.; Ng, Kee Siong; Uther, William T.B.: Probabilities on sentences in an expressive logic (2013)
  14. Freer, Cameron E.; Roy, Daniel M.: Computable de Finetti measures (2012)
  15. Gutmann, Bernd; Thon, Ingo; Kimmig, Angelika; Bruynooghe, Maurice; De Raedt, Luc: The magic of logical inference in probabilistic programming (2011)
  16. Lloyd, John W.; Ng, Kee Siong: Declarative programming for agent applications (2011) ioport
  17. Milch, Brian; Russell, Stuart: Extending Bayesian networks to the open-universe case (2010)
  18. Freer, Cameron E.; Roy, Daniel M.: Computable exchangeable sequences have computable de Finetti measures (2009)
  19. Ng, K.S.; Lloyd, J.W.; Uther, W.T.B.: Probabilistic modelling, inference and learning using logical theories (2008)