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Unifying logic and probability

Published:25 June 2015Publication History
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Abstract

Open-universe probability models show merit in unifying efforts.

References

  1. Ackerman, N., Freer, C., Roy, D. On the computability of conditional probability. arXiv 1005.3014, 2013.Google ScholarGoogle Scholar
  2. Arora, N.S., Russell, S., Sudderth, E. NET-VISA: Network processing vertically integrated seismic analysis. Bull. Seism. Soc. Am. 103 (2013).Google ScholarGoogle Scholar
  3. Bacchus, F. Representing and Reasoning with Probabilistic Knowledge. MIT Press, 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Breese, J.S. Construction of belief and decision networks. Comput. Intell. 8 (1992) 624--647.Google ScholarGoogle ScholarCross RefCross Ref
  5. Claret, G., Rajamani, S.K., Nori, A.V., Gordon, A.D., Borgström, J. Bayesian inference using data flow analysis. In FSE-13 (2013). Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Dalvi, N.N., Ré, C., Suciu, D. Probabilistic databases. CACM 52, 7 (2009), 86--94. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Fischer, B., Schumann, J. AutoBayes: A system for generating data analysis programs from statistical models. J. Funct. Program 13 (2003). Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Gaifman, H. Concerning measures in first order calculi. Israel J. Math. 2 (1964), 1--18.Google ScholarGoogle ScholarCross RefCross Ref
  9. Gaifman, H. Concerning measures on Boolean algebras. Pacific J. Math. 14 (1964), 61--73.Google ScholarGoogle ScholarCross RefCross Ref
  10. Gilks, W.R., Thomas, A., Spiegelhalter, D.J. A language and program for complex Bayesian modelling. The Statistician 43 (1994), 169--178.Google ScholarGoogle ScholarCross RefCross Ref
  11. Goodman, N.D., Mansinghka, V.K., Roy, D., Bonawitz, K., Tenenbaum, J.B. Church: A language for generative models. In UAI-08 (2008).Google ScholarGoogle Scholar
  12. Hailperin, T. Probability logic. Notre Dame J. Formal Logic 25, 3 (1984), 198--212.Google ScholarGoogle ScholarCross RefCross Ref
  13. Halpern, J.Y. An analysis of first-order logics of probability. AIJ 46, 3 (1990), 311--350. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Howson, C. Probability and logic. J. Appl. Logic 1, 3--4 (2003), 151--165. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Hur, C.-K., Nori, A.V., Rajamani, S.K., Samuel, S. Slicing probabilistic programs. In PLDI-14 (2014). Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Jain, D., Kirchlechner, B., Beetz, M. Extending Markov logic to model probability distributions in relational domains. In KI-07 (2007). Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Koller, D., McAllester, D.A., Pfeffer, A. Effective Bayesian inference for stochastic programs. In AAAI-97 (1997). Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Li, L., Wu, Y., Russell, S. SWIFT: Compiled inference for probabilistic programs. Tech. Report EECS-2015-12, UC Berkeley, 2015.Google ScholarGoogle Scholar
  19. McCallum, A., Schultz, K., Singh, S. FACTORIE: Probabilistic programming via imperatively defined factor graphs. In NIPS 22 (2010).Google ScholarGoogle Scholar
  20. Milch, B. Probabilistic models with unknown objects. PhD thesis, UC Berkeley, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Milch, B., Marthi, B., Sontag, D., Russell, S.J., Ong, D., Kolobov, A. BLOG: Probabilistic models with unknown objects. In IJCAI-05 (2005). Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Milch, B., Russell, S.J. General-purpose MCMC inference over relational structures. In UAI-06 (2006).Google ScholarGoogle Scholar
  23. Nilsson, N.J. Probabilistic logic. AIJ 28 (1986), 71--87. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Paskin, M. Maximum entropy probabilistic logic. Tech. Report UCB/CSD-01-1161, UC Berkeley, 2002. Google ScholarGoogle Scholar
  25. Pasula, H., Marthi, B., Milch, B., Russell, S.J., Shpitser, I. Identity uncertainty and citation matching. In NIPS 15 (2003).Google ScholarGoogle Scholar
  26. Pasula, H., Russell, S.J. Approximate inference for first-order probabilistic languages. In IJCAI-01 (2001). Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Pearl, J. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Pfeffer, A. IBAL: A probabilistic rational programming language. In IJCAI-01 (2001). Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Poole, D. First-order probabilistic inference. In IJCAI-03 (2003). Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Richardson, M., Domingos, P. Markov logic networks. Machine Learning 62, 1--2 (2006), 107--136. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Russell, S.J. Expressive probability models in science. In Discovery Science (Tokyo, 1999). Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Van den Broeck, G. Lifted Inference and Learning in Statistical Relational Models. PhD thesis, Katholieke Universiteit Leuven, 2013.Google ScholarGoogle Scholar

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      • Published in

        cover image Communications of the ACM
        Communications of the ACM  Volume 58, Issue 7
        July 2015
        102 pages
        ISSN:0001-0782
        EISSN:1557-7317
        DOI:10.1145/2797100
        • Editor:
        • Moshe Y. Vardi
        Issue’s Table of Contents

        Copyright © 2015 ACM

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        Publication History

        • Published: 25 June 2015

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