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Probabilistic Logic and Relational Models

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References

  • Blockeel H, De Raedt L (1998) Top-down induction of first-order logical decision trees. Artif Intell 101(1–2):285–297

    Article  MathSciNet  MATH  Google Scholar 

  • Breese JS (1992) Construction of belief and decision networks. Comput Intell 8(4):624–647

    Article  Google Scholar 

  • Breese JS, Goldman RP, Wellman MP (1994) Introduction to the special section on knowledge-based construction of probabilistic decision models. IEEE Trans Syst Man Cybern 24(11):1577–1579

    Google Scholar 

  • Chavira M, Darwiche A (2008) On probabilistic inference by weighted model counting. Artif Intell 172:772–799

    Article  MathSciNet  MATH  Google Scholar 

  • de Salvo Braz R, Amir E, Roth D (2005) Lifted first-order probabilistic inference. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI-05), pp 1319–1325

    Google Scholar 

  • Fierens D, den Broeck GV, Thon I, Gutmann B, De Raedt L (2011) Inference in probabilistic logic programs using weighted CNF’s. In: Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011). AUAI Press, Corvallis

    Google Scholar 

  • Friedman N, Getoor L, Koller D, Pfeffer A (1999) Learning probabilistic relational models. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI-99)

    Google Scholar 

  • Gilks WR, Thomas A, Spiegelhalter DJ (1994) A language and program for complex bayesian modelling. Statistician 43(1):169–177

    Article  Google Scholar 

  • Gogate V, Domingos P (2011) Probabilistic theorem proving. In: Proceedings of the 27th Conference of Uncertainty in Artificial Intelligence (UAI-11). AUAI Press, Corvallis

    Google Scholar 

  • Goodman ND, Mansinghka VK, Roy D, Bonawitz K, Tenenbaum JB (2008) Church: a language for generative models. In: Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI-08). AUAI Press, Corvallis

    Google Scholar 

  • Haddawy P (1994) Generating Bayesian networks from probability logic knowledge bases. In: Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence (UAI-94). Morgan Kaufmann, San Francisco, pp 262–269

    Google Scholar 

  • Halpern J (1990) An analysis of first-order logics of probability. Artif Intell 46:311–350

    Article  MathSciNet  MATH  Google Scholar 

  • Heckerman D, Meek C, Koller D (2007) Probabilistic entity-relationship models, PRMs, and plate models. In: Getoor L, Taskar B (eds) Introduction to statistical relational learning. MIT Press, Cambridge, MA

    Google Scholar 

  • Jaeger M (1997) Relational bayesian networks. In: Geiger D, Shenoy PP (eds) Proceedings of the 13th Conference of Uncertainty in Artificial Intelligence (UAI-97). Morgan Kaufmann, San Francisco, pp 266–273

    Google Scholar 

  • Jaeger M (2000) On the complexity of inference about probabilistic relational models. Artif Intell 117:297–308

    Article  MathSciNet  MATH  Google Scholar 

  • Kersting K, De Raedt L (2001) Towards combining inductive logic programming and bayesian networks. In: Proceedings of the Eleventh International Conference on Inductive Logic Programming (ILP-2001). Springer, Berlin, Heidelberg, pp 118–131

    Chapter  MATH  Google Scholar 

  • Kimmig A, Demoen B, De Raedt L, Santos Costa V, Rocha R (2011) On the implementation of the probabilistic logic programming language problog. Theory Pract Logic Program 11(2–3):235–262

    Article  MathSciNet  MATH  Google Scholar 

  • Laskey KB (2008) Mebn: a language for first-order bayesian knowledge bases. Artif Intell 172(2–3):140–178. https://doi.org/10.1016/j.artint.2007.09.006

    Article  MathSciNet  MATH  Google Scholar 

  • Laskey KB, Mahoney SM (1997) Network fragments: representing knowledge for constructing probabilistic models. In: Proceedings of the 13th Annual Conference on Uncertainty in Artificial Intelligence (UAI–97). Morgan Kaufmann Publishers, San Francisco, pp 334–341

    Google Scholar 

  • Milch B, Marthi B, Russell S, Sontag D, Ong D, Kolobov A (2005) Blog: probabilistic logic with unknown objects. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI-05), pp 1352–1359

    Google Scholar 

  • Milch B, Zettlemoyer LS, Kersting K, Haimes M, Kaelbling LP (2008) Lifted probabilistic inference with counting formulas. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI-08). AAAI Press, Menlo Park

    Google Scholar 

  • Muggleton S (1996) Stochastic logic programs. In: De Raedt L (ed) Advances in Inductive Logic Programming. IOS Press, Washington, DC, pp 254–264

    Google Scholar 

  • Neville J, Jensen D, Friedland L, Hay M (2003) Learning relational probability trees. In: Proceedings of The 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-03). ACM, New York

    Google Scholar 

  • Ng KS, Lloyd JW, Uther WTB (2008) Probabilistic modelling, inference and learning using logical theories. Ann Math Artif Intell 54(1–3):159–205

    Article  MathSciNet  MATH  Google Scholar 

  • Ngo L, Haddawy P (1997) Answering queries from context-sensitive probabilistic knowledge bases. Theor Comput Sci 171:147–177

    Article  MathSciNet  MATH  Google Scholar 

  • Nilsson N (1986) Probabilistic logic. Artif Intell 28:71–88

    Article  MathSciNet  MATH  Google Scholar 

  • Pfeffer A (2001) IBAL: a probabilistic rational programming language. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI-01)

    Google Scholar 

  • Poole D (1993) Probabilistic horn abduction and Bayesian networks. Artif Intell 64:81–129

    Article  MATH  Google Scholar 

  • Poole D (2003) First-order probabilistic inference. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI-03)

    Google Scholar 

  • Poole D (2008) The independent choice logic and beyond. In: De Raedt L, Frasconi P, Kersting K, Muggleton S (eds) Probabilistic inductive logic programming, lecture notes in artificial intelligence, vol 4911. Springer, Berlin, pp 222–243

    Chapter  Google Scholar 

  • Richardson M, Domingos P (2006) Markov logic networks. Mach Learn 62(1–2):107–136

    Article  Google Scholar 

  • Robins G, Pattison P, Kalish Y, Lusher D (2007) An introduction to exponential random graph (p*) models for social networks. Soc Networks 29(2):173–191

    Article  Google Scholar 

  • Sato T (1995) A statistical learning method for logic programs with distribution semantics. In: Proceedings of the 12th International Conference on Logic Programming (ICLP’95). MIT Press, Cambridge, pp 715–729

    Google Scholar 

  • Van den Broeck G, Taghipour N, Meert W, Davis J, De Raedt L (2011) Lifted probabilistic inference by first-order knowledge compilation. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI-11)

    Google Scholar 

Recommended Reading

  • De Raedt L (2008) Logical and relational learning. Springer, Berlin

    Book  MATH  Google Scholar 

  • De Raedt L, Frasconi P, Kersting K, Muggleton S (eds) (2008) Probabilistic inductive logic programming, lecture notes in artificial intelligence, vol 4911. Springer, Berlin

    Google Scholar 

  • Getoor L, Taskar B (eds) (2007) Introduction to statistical relational learning. MIT Press, Cambridge, MA

    MATH  Google Scholar 

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Correspondence to Manfred Jaeger .

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Jaeger, M. (2018). Probabilistic Logic and Relational Models. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7131-2_157

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