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Lang."],"published-print":{"date-parts":[[2020,11,13]]},"abstract":"<jats:p>Probabilistic programming languages (PPLs) are an expressive means of representing and reasoning about probabilistic models. The computational challenge of<jats:italic>probabilistic inference<\/jats:italic>remains the primary roadblock for applying PPLs in practice. Inference is fundamentally hard, so there is no one-size-fits all solution. In this work, we target scalable inference for an important class of probabilistic programs: those whose probability distributions are<jats:italic>discrete<\/jats:italic>. Discrete distributions are common in many fields, including text analysis, network verification, artificial intelligence, and graph analysis, but they prove to be challenging for existing PPLs.<\/jats:p><jats:p>We develop a domain-specific probabilistic programming language called Dice that features a new approach to exact discrete probabilistic program inference. Dice exploits program structure in order to factorize inference, enabling us to perform exact inference on probabilistic programs with hundreds of thousands of random variables. Our key technical contribution is a new reduction from discrete probabilistic programs to weighted model counting (WMC). This reduction separates the structure of the distribution from its parameters, enabling logical reasoning tools to exploit that structure for probabilistic inference. We (1) show how to compositionally reduce Dice inference to WMC, (2) prove this compilation correct with respect to a denotational semantics, (3) empirically demonstrate the performance benefits over prior approaches, and (4) analyze the types of structure that allow Dice to scale to large probabilistic programs.<\/jats:p>","DOI":"10.1145\/3428208","type":"journal-article","created":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T23:36:06Z","timestamp":1606260966000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":67,"title":["Scaling exact inference for discrete probabilistic programs"],"prefix":"10.1145","volume":"4","author":[{"given":"Steven","family":"Holtzen","sequence":"first","affiliation":[{"name":"University of California at Los Angeles, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guy","family":"Van den Broeck","sequence":"additional","affiliation":[{"name":"University of California at Los Angeles, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Todd","family":"Millstein","sequence":"additional","affiliation":[{"name":"University of California at Los Angeles, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,11,13]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/0169-2070(95)00664-8"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133904"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/0924-980x(95)00252-g"},{"key":"e_1_2_2_4_1","doi-asserted-by":"crossref","unstructured":"R Iris Bahar Erica A Frohm Charles M Gaona Gary D Hachtel Enrico Macii Abelardo Pardo and Fabio Somenzi. 1997. 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Journal of Artificial Intelligence Research 17 ( 2002 ) 229-264. A. Darwiche and P. Marquis. 2002. A Knowledge Compilation Map. Journal of Artificial Intelligence Research 17 ( 2002 ) 229-264.","DOI":"10.1613\/jair.989"},{"key":"e_1_2_2_27_1","first-page":"2462","volume-title":"Proceedings of IJCAI","volume":"7","author":"Raedt Luc De","year":"2007","unstructured":"Luc De Raedt , Angelika Kimmig , and Hannu Toivonen . 2007 . ProbLog: A Probabilistic Prolog and Its Application in Link Discovery . In Proceedings of IJCAI , Vol. 7 . 2462 - 2467 . Luc De Raedt, Angelika Kimmig, and Hannu Toivonen. 2007. ProbLog: A Probabilistic Prolog and Its Application in Link Discovery. In Proceedings of IJCAI, Vol. 7. 2462-2467."},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-63390-9_31"},{"key":"e_1_2_2_29_1","unstructured":"Joshua V Dillon Ian Langmore Dustin Tran Eugene Brevdo Srinivas Vasudevan Dave Moore Brian Patton Alex Alemi Matt Hofman and Rif A Saurous. 2017. 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SampleSearch: Importance sampling in presence of determinism. Artificial Intelligence 175 2 ( 2011 ) 694-729. V. Gogate and R. Dechter. 2011. SampleSearch: Importance sampling in presence of determinism. Artificial Intelligence 175 2 ( 2011 ) 694-729.","DOI":"10.1016\/j.artint.2010.10.009"},{"key":"e_1_2_2_38_1","volume-title":"Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence (UAI).","author":"Goodman Noah D.","unstructured":"Noah D. Goodman , Vikash K. Mansinghka , Daniel M. Roy , Keith Bonawitz , and Joshua B. Tenenbaum . 2008. Church: a language for generative models . In Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence (UAI). Noah D. Goodman, Vikash K. Mansinghka, Daniel M. Roy, Keith Bonawitz, and Joshua B. Tenenbaum. 2008. Church: a language for generative models. In Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence (UAI)."},{"key":"e_1_2_2_39_1","unstructured":"Noah D Goodman and Andreas Stuhlm\u00fcller. 2014. The design and implementation of probabilistic programming languages. Noah D Goodman and Andreas Stuhlm\u00fcller. 2014. The design and implementation of probabilistic programming languages."},{"key":"e_1_2_2_40_1","volume-title":"Automatic Reparameterisation of Probabilistic Programs. International Conference on Machine Learning (ICML) ( 2020 ).","author":"Gorinova Maria I","year":"2020","unstructured":"Maria I Gorinova , Dave Moore , and Matthew D Hofman . 2020 . Automatic Reparameterisation of Probabilistic Programs. International Conference on Machine Learning (ICML) ( 2020 ). Maria I Gorinova, Dave Moore, and Matthew D Hofman. 2020. Automatic Reparameterisation of Probabilistic Programs. International Conference on Machine Learning (ICML) ( 2020 )."},{"key":"e_1_2_2_41_1","volume-title":"Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities. arXiv preprint arXiv","author":"Gram-Hansen Bradley","year":"1804","unstructured":"Bradley Gram-Hansen , Yuan Zhou , Tobias Kohn , Tom Rainforth , Hongseok Yang , and Frank Wood . 2018. Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities. arXiv preprint arXiv : 1804 . 03523 ( 2018 ). Bradley Gram-Hansen, Yuan Zhou, Tobias Kohn, Tom Rainforth, Hongseok Yang, and Frank Wood. 2018. Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities. arXiv preprint arXiv: 1804. 03523 ( 2018 )."},{"key":"e_1_2_2_42_1","article-title":"The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo","volume":"15","author":"Hofman Matthew D","year":"2014","unstructured":"Matthew D Hofman and Andrew Gelman . 2014 . The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo . Journal of Machine Learning Research 15 , 1 ( 2014 ), 1593-1623. Matthew D Hofman and Andrew Gelman. 2014. The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research 15, 1 ( 2014 ), 1593-1623.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_2_43_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning (ICML).","author":"Holtzen Steven","year":"2018","unstructured":"Steven Holtzen , Guy Van den Broeck , and Todd Millstein . 2018 . Sound abstraction and decomposition of probabilistic programs . In Proceedings of the 35th International Conference on Machine Learning (ICML). Steven Holtzen, Guy Van den Broeck, and Todd Millstein. 2018. Sound abstraction and decomposition of probabilistic programs. In Proceedings of the 35th International Conference on Machine Learning (ICML)."},{"key":"e_1_2_2_44_1","volume-title":"Guy Van den Broeck, and Todd Millstein","author":"Holtzen Steven","year":"2020","unstructured":"Steven Holtzen , Guy Van den Broeck, and Todd Millstein . 2020 . Scaling Exact Inference for Discrete Probabilistic Programs . arXiv:arXiv: 2005.09089 Steven Holtzen, Guy Van den Broeck, and Todd Millstein. 2020. Scaling Exact Inference for Discrete Probabilistic Programs. arXiv:arXiv: 2005.09089"},{"key":"e_1_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-49498-1_14"},{"key":"e_1_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.4230\/LIPIcs.FSTTCS.2015.475"},{"key":"e_1_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/1592434.1592438"},{"key":"e_1_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007665907178"},{"key":"e_1_2_2_50_1","volume-title":"Paul C Van Oorschot, and Scott A Vanstone","author":"Katz Jonathan","year":"1996","unstructured":"Jonathan Katz , Alfred J Menezes , Paul C Van Oorschot, and Scott A Vanstone . 1996 . Handbook of applied cryptography. CRC press . Jonathan Katz, Alfred J Menezes, Paul C Van Oorschot, and Scott A Vanstone. 1996. Handbook of applied cryptography. CRC press."},{"key":"e_1_2_2_51_1","unstructured":"D. Koller and N. Friedman. 2009. Probabilistic graphical models: principles and techniques. MIT press. D. Koller and N. Friedman. 2009. Probabilistic graphical models: principles and techniques. MIT press."},{"key":"e_1_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1201\/b10391"},{"key":"e_1_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/SFCS.1979.38"},{"key":"e_1_2_2_54_1","unstructured":"Alp Kucukelbir Rajesh Ranganath Andrew Gelman and David Blei. 2015. Automatic variational inference in Stan. In Advances in neural information processing systems. 568-576. Alp Kucukelbir Rajesh Ranganath Andrew Gelman and David Blei. 2015. Automatic variational inference in Stan. In Advances in neural information processing systems. 568-576."},{"key":"e_1_2_2_55_1","article-title":"Automatic diferentiation variational inference","volume":"18","author":"Kucukelbir Alp","year":"2017","unstructured":"Alp Kucukelbir , Dustin Tran , Rajesh Ranganath , Andrew Gelman , and David M Blei . 2017 . Automatic diferentiation variational inference . The Journal of Machine Learning Research 18 , 1 ( 2017 ), 430-474. Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, and David M Blei. 2017. Automatic diferentiation variational inference. The Journal of Machine Learning Research 18, 1 ( 2017 ), 430-474.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-22110-1_47"},{"key":"e_1_2_2_57_1","volume-title":"The computational complexity of probabilistic networks","author":"Petrus Kwisthout Johan Henri","unstructured":"Johan Henri Petrus Kwisthout . 2009. The computational complexity of probabilistic networks . Utrecht University . Johan Henri Petrus Kwisthout. 2009. The computational complexity of probabilistic networks. Utrecht University."},{"key":"e_1_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.505"},{"key":"e_1_2_2_59_1","first-page":"1520","article-title":"Approximate bayesian image interpretation using generative probabilistic graphics programs","author":"Mansinghka Vikash","year":"2013","unstructured":"Vikash Mansinghka , Tejas D Kulkarni , Yura N Perov , and Josh Tenenbaum . 2013 . Approximate bayesian image interpretation using generative probabilistic graphics programs . In Advances in Neural Information Processing Systems. 1520 - 1528 . Vikash Mansinghka, Tejas D Kulkarni, Yura N Perov, and Josh Tenenbaum. 2013. Approximate bayesian image interpretation using generative probabilistic graphics programs. In Advances in Neural Information Processing Systems. 1520-1528.","journal-title":"Advances in Neural Information Processing Systems."},{"key":"e_1_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3192366.3192409"},{"key":"e_1_2_2_61_1","volume-title":"Proc. of NIPS 22 ( 2009 ), 1249-1257","author":"McCallum A","year":"2009","unstructured":"A McCallum , K Schultz , and S Singh . 2009 . Factorie: Probabilistic programming via imperatively defined factor graphs . Proc. of NIPS 22 ( 2009 ), 1249-1257 . A McCallum, K Schultz, and S Singh. 2009. Factorie: Probabilistic programming via imperatively defined factor graphs. Proc. of NIPS 22 ( 2009 ), 1249-1257."},{"key":"e_1_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-58940-9"},{"key":"e_1_2_2_63_1","unstructured":"T. Minka J.M. Winn J.P. Guiver S. Webster Y. Zaykov B. Yangel A. Spengler and J. Bronskill. 2014. Infer.NET 2.6. Microsoft Research Cambridge. http:\/\/research.microsoft.com\/infernet. T. Minka J.M. Winn J.P. Guiver S. Webster Y. Zaykov B. Yangel A. Spengler and J. Bronskill. 2014. Infer.NET 2.6. Microsoft Research Cambridge. http:\/\/research.microsoft.com\/infernet."},{"key":"e_1_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-29604-3_5"},{"key":"e_1_2_2_65_1","first-page":"2476","article-title":"R2","author":"Nori Aditya V","year":"2014","unstructured":"Aditya V Nori , Chung-Kil Hur , Sriram K Rajamani , and Selva Samuel . 2014 . R2 : An Eficient MCMC Sampler for Probabilistic Programs. In AAAI. 2476 - 2482 . Aditya V Nori, Chung-Kil Hur, Sriram K Rajamani, and Selva Samuel. 2014. R2: An Eficient MCMC Sampler for Probabilistic Programs. In AAAI. 2476-2482.","journal-title":"An Eficient MCMC Sampler for Probabilistic Programs. In AAAI."},{"key":"e_1_2_2_66_1","unstructured":"Fritz Obermeyer Eli Bingham Martin Jankowiak Neeraj Pradhan Justin Chiu Alexander Rush and Noah Goodman. 2019. Tensor variable elimination for plated factor graphs. ( 2019 ) 4871-4880. Fritz Obermeyer Eli Bingham Martin Jankowiak Neeraj Pradhan Justin Chiu Alexander Rush and Noah Goodman. 2019. Tensor variable elimination for plated factor graphs. ( 2019 ) 4871-4880."},{"key":"e_1_2_2_67_1","volume-title":"Ph. D. dissertation, Inst. Biocybern. Biomed. Eng.","author":"Onisko Agnieszka","unstructured":"Agnieszka Onisko . 2003. Probabilistic causal models in medicine: Application to diagnosis of liver disorders . In Ph. D. dissertation, Inst. Biocybern. Biomed. Eng. , Polish Academy Sci ., Warsaw, Poland. Agnieszka Onisko. 2003. Probabilistic causal models in medicine: Application to diagnosis of liver disorders. In Ph. D. dissertation, Inst. Biocybern. Biomed. Eng., Polish Academy Sci., Warsaw, Poland."},{"key":"e_1_2_2_68_1","doi-asserted-by":"crossref","unstructured":"Judea Pearl. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. Judea Pearl. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann.","DOI":"10.1016\/B978-0-08-051489-5.50008-4"},{"key":"e_1_2_2_69_1","unstructured":"Avi Pfefer. 2007a. A general importance sampling algorithm for probabilistic programs. ( 2007 ). http:\/\/nrs.harvard.edu\/urn3:HUL. InstRepos:25235125 Avi Pfefer. 2007a. A general importance sampling algorithm for probabilistic programs. ( 2007 ). http:\/\/nrs.harvard.edu\/urn3:HUL. InstRepos:25235125"},{"key":"e_1_2_2_70_1","doi-asserted-by":"crossref","unstructured":"Avi Pfefer. 2007b. The Design and Implementation of IBAL: A General-Purpose Probabilistic Language. Introduction to statistical relational learning 1993 ( 2007 ) 399. Avi Pfefer. 2007b. The Design and Implementation of IBAL: A General-Purpose Probabilistic Language. Introduction to statistical relational learning 1993 ( 2007 ) 399.","DOI":"10.7551\/mitpress\/7432.003.0016"},{"key":"e_1_2_2_71_1","volume-title":"Figaro: An object-oriented probabilistic programming language. Charles River Analytics Technical Report 137 ( 2009 ).","author":"Pfefer Avi","year":"2009","unstructured":"Avi Pfefer . 2009 . Figaro: An object-oriented probabilistic programming language. Charles River Analytics Technical Report 137 ( 2009 ). Avi Pfefer. 2009. Figaro: An object-oriented probabilistic programming language. Charles River Analytics Technical Report 137 ( 2009 )."},{"key":"e_1_2_2_72_1","volume-title":"Structured Factored Inference for Probabilistic Programming. In International Conference on Artificial Intelligence and Statistics. 1224-1232","author":"Pfefer Avi","year":"2018","unstructured":"Avi Pfefer , Brian Ruttenberg , William Kretschmer , and Alison O Connor . 2018 . Structured Factored Inference for Probabilistic Programming. In International Conference on Artificial Intelligence and Statistics. 1224-1232 . Avi Pfefer, Brian Ruttenberg, William Kretschmer, and Alison OConnor. 2018. Structured Factored Inference for Probabilistic Programming. In International Conference on Artificial Intelligence and Statistics. 1224-1232."},{"key":"e_1_2_2_73_1","doi-asserted-by":"publisher","DOI":"10.1017\/S147106841100010X"},{"key":"e_1_2_2_74_1","unstructured":"Feras Saad and Vikash Mansinghka. 2016. A Probabilistic Programming Approach To Probabilistic Data Analysis. In Advances in Neural Information Processing Systems (NIPS). Feras Saad and Vikash Mansinghka. 2016. A Probabilistic Programming Approach To Probabilistic Data Analysis. In Advances in Neural Information Processing Systems (NIPS)."},{"key":"e_1_2_2_75_1","first-page":"475","article-title":"Performing Bayesian inference by weighted model counting","volume":"5","author":"Sang Tian","year":"2005","unstructured":"Tian Sang , Paul Beame , and Henry A Kautz . 2005 . Performing Bayesian inference by weighted model counting . In AAAI , Vol. 5. 475 - 481 . Tian Sang, Paul Beame, and Henry A Kautz. 2005. Performing Bayesian inference by weighted model counting. In AAAI, Vol. 5. 475-481.","journal-title":"AAAI"},{"key":"e_1_2_2_76_1","doi-asserted-by":"publisher","DOI":"10.1145\/2499370.2462179"},{"key":"e_1_2_2_77_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.12369"},{"key":"e_1_2_2_78_1","unstructured":"Jan-Willem van de Meent Hongseok Yang Vikash Mansinghka and Frank Wood. 2015. Particle Gibbs with Ancestor Sampling for Probabilistic Programs. In AISTATS. Jan-Willem van de Meent Hongseok Yang Vikash Mansinghka and Frank Wood. 2015. Particle Gibbs with Ancestor Sampling for Probabilistic Programs. In AISTATS."},{"key":"e_1_2_2_79_1","doi-asserted-by":"publisher","DOI":"10.1561\/1900000052"},{"key":"e_1_2_2_80_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-53291-8_15"},{"key":"e_1_2_2_81_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2016.06.009"},{"key":"e_1_2_2_82_1","doi-asserted-by":"publisher","DOI":"10.1145\/3296979.3192408"},{"key":"e_1_2_2_83_1","unstructured":"David Wingate and Theophane Weber. 2013. Automated variational inference in probabilistic programming. arXiv preprint arXiv:1301.1299 ( 2013 ). David Wingate and Theophane Weber. 2013. Automated variational inference in probabilistic programming. arXiv preprint arXiv:1301.1299 ( 2013 )."},{"key":"e_1_2_2_84_1","first-page":"1024","article-title":"A new approach to probabilistic programming inference","author":"Wood Frank","year":"2014","unstructured":"Frank Wood , Jan Willem Meent , and Vikash Mansinghka . 2014 . 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