References
- Baabak Ahmadi, Kristian Kersting, and Scott Sanner. Multi-evidence lifted message passing, with application to pagerank and the kalman filter. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAl), pp. 1152 - 1158, 2011.
- Fahiem Bacchus. Representing and reasoning with probabilistic knowledge: a logical approach to probabilities. MIT Press, Cambridge, MA, USA, 1990.
- Peter Carbonetto, Jacek Kisynski, Nando de Freitas, and David Poole. Nonparametric bayesian logic. In Proceedings of the 21st Conference in Uncertainty in Artificial Intelligence (UAl), pp. 85 - 93, 2005.
- Jaesik Choi and Eyal Amir, Lifted Relational Variational Inference, In Proceedings of the Twenty-Eight Conference on Uncertainty in Artificial Intelligence (UAl), pp. 196-206, 2012.
- Jaesik Choi, Eyal Amir, and David Hill. Lifted inference for relational continuous models. In Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAl), pp. 126 - 134, 2010.
- Jaesik Choi, Rodrigo de Salvo Braz, and Hung H. Bui. Efficient methods for lifted inference with aggregate factors. In Proceedings of the Twenty-Fifth AAAl Conference on Artificial Intelligence (AAAI), pp. 1030-1036, 2011.
- Jaesik Choi, Abner Guzman-Rivera, and Eyal Amir. Lifted relational kalman filtering. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI), pp. 2092 - 2099, 2011.
- B. de Finetti. Funzione caratteristica di unfenomeno aleatorio. Mathematice e Naturale, 1931.
- Rodrigo de Salvo Braz, Eyal Amir, and Dan Roth. Lifted first-order probabilistic inference. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAl), pp. 1319 - 1325, 2005.
- P. Diaconis and D. Freedman. Finite exchangeable sequences. Annals of Probability, 8(1): 115 - 130, 1980. https://doi.org/10.1214/aop/1176994828
- Nir Friedman, Lise Getoor, Daphne Koller, and Avi Pfeffer. Learning probabilistic relational models. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAl), pp. 1300-1309, 1999.
- Lise Getoor and Ben Taskar, Introduction to Statistical Relational Learning, MlT Press, Cambridge, MA, USA, 2007.
- Joseph Y. Halpern. An analysis of first-order logics of probability. Artificial Intelligence, 46(3):311-350, 1990. https://doi.org/10.1016/0004-3702(90)90019-V
- E. Hewitt and L.J. Savage. Symmetric measures on cartesian products. Trans. Amer. Math. Soc., 80:470-501, 1955. https://doi.org/10.1090/S0002-9947-1955-0076206-8
- Michael I. Jordan, Zoubin Ghahramani, and Tommi Jaakkola, and Lawrence K. Saul. An introduction to variational methods for graphical models. Machine Learning, 37(2): 183-233, 1999. https://doi.org/10.1023/A:1007665907178
- Kristian Kersting and Luc De Raedt, Bayesian Logic Programs, Technical report, Albert-Ludwigs University at Freiburg, 2001.
- Andrew McCallum, Karl Schultz, and Sameer Singh. FACTORIE: Probabilistic Programming via lmperatively Defined Factor Graphs. In Advances on Neural. Information Processing Systems (NIPS), 2009.
- Brian Milch, Bhaskara Marthi, Stuart J. Russell, David Sontag, Daniel L. Ong, and Andrey Kolobov. BLOG: Probabilistic models with unknown objects. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAl), pp. 1352-1359, 2005.
- Brian Milch, Luke S. Zettlemoyer, Kristian Kersting, Michael Haimes, and Leslie Pack Kaelbling. Lifted probabilistic inference with counting formulas. In Proceedings of the Twenty-Third AAAl Conference on Artificial Intelligence (AAAl), pp. 1062-1068, 2008.
- Galileo Namata, Stanley Kok, and Lise Getoor. Collective Graph Identification. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2011
- Raymond Ng and V. S. Subrahmanian. Probabilistic logic programming. Information and Computation, 101(2): 150-201, 1992. https://doi.org/10.1016/0890-5401(92)90061-J
- Avi Pfeffer, Daphne Koller, Brian Milch, and Ken T. Takusagawa. Spook: A system for probabilistic object-oriented knowledge representation. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI), pp. 541-550, 1999.
- David Poole. First-order probabilistic inference. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAl), pp. 985-991, 2003.
- Matthew Richardson and Pedro Domingos. Markov logic networks. Machine Learning, 62(1-2): 107-136, 2006. https://doi.org/10.1007/s10994-006-5833-1
- Parag Singla and Pedro Domingos. Lifted firstorder belief propagation. In Proceedings of the Twenty-Third AAAl Conference on Artificial Intelligence (AAAI), pp. 1094-1099, 2008.