Noah D. Goodman
Supplemental Material
- N. Ackerman, C. Freer, and D. Roy. Noncomputable conditional distributions. In Logic in Computer Science (LICS), 2011 26th Annual IEEE Symposium on, pages 107--116. IEEE, 2011. Google Scholar
Digital Library
- M. Frank and N. Goodman. Predicting pragmatic reasoning in language games. Science, 336 (6084): 998--998, 2012.Google ScholarCross Ref
- C. E. Freer and D. M. Roy. Computable de Finetti measures. Annals of Pure and Applied Logic, 163 (5): 530--546, 2012. 10.1016/j.apal.2011.06.011.% URL% http://www.sciencedirect.com/science/article/pii/S0168007211000868.Google ScholarCross Ref
- T. Gerstenberg and N. D. Goodman. Ping pong in Church: Productive use of concepts in human probabilistic inference. In Proceedings of the 34th annual conference of the cognitive science society, 2012.Google Scholar
- N. Goodman and A. Stuhlmüller. Knowledge and implicature: Modeling language understanding as social cognition. Topics in Cognitive Science, 2013.Google ScholarCross Ref
- N. Goodman, V. Mansinghka, D. Roy, K. Bonawitz, and J. Tenenbaum. Church: A language for generative models. In phIn UAI, 2008.Google Scholar
- C. Jones and G. Plotkin. A probabilistic powerdomain of evaluations. In Logic in Computer Science (LICS), 1989 4th Annual IEEE Symposium on, pages 186--195, Jun 1989. 10.1109/LICS.1989.39173. Google ScholarDigital Library
- A. Kimmig, B. Demoen, L. De Raedt, V. S. Costa, and R. Rocha. On the implementation of the probabilistic logic programming language ProbLog. Theory and Practice of Logic Programming, 11 (2--3): 235--262, 2011. Google ScholarDigital Library
- O. Kiselyov and C. Shan. Embedded probabilistic programming. In Domain-Specific Languages, pages 360--384, 2009. Google ScholarDigital Library
- D. Koller and N. Friedman. Probabilistic graphical models: principles and techniques. MIT press, 2009. Google ScholarDigital Library
- A. McCallum, K. Schultz, and S. Singh. Factorie: Probabilistic programming via imperatively defined factor graphs. In Neural Information Processing Systems Conference (NIPS), 2009.Google Scholar
- B. Milch, B. Marthi, S. Russell, D. Sontag, D. L. Ong, and A. Kolobov. BLOG: Probabilistic models with unknown objects. In International Joint Conference on Artificial Intelligence (IJCAI), pages 1352--1359, 2005. Google ScholarDigital Library
- A. Pfeffer. IBAL: A probabilistic rational programming language. In International Joint Conference on Artificial Intelligence (IJCAI), pages 733--740. Morgan Kaufmann Publ., 2001. Google ScholarDigital Library
- A. Pfeffer. Figaro: An object-oriented probabilistic programming language. Charles River Analytics Technical Report, 2009.Google Scholar
- D. Poole. The independent choice logic and beyond. Probabilistic inductive logic programming, pages 222--243, 2008. Google ScholarDigital Library
- M. Richardson and P. Domingos. Markov logic networks. Machine Learning, 62: 107--136, 2006. Google ScholarDigital Library
- T. Sato and Y. Kameya. PRISM: A symbolic-statistical modeling language. In International Joint Conference on Artificial Intelligence (IJCAI), 1997. Google ScholarDigital Library
- A. Stuhlmüller and N. Goodman. A dynamic programming algorithm for inference in recursive probabilistic programs. arXiv preprint arXiv:1206.3555, 2012.Google Scholar
- D. Wingate, N. Goodman, A. Stuhlmueller, and J. Siskind. Nonstandard interpretations of probabilistic programs for efficient inference. In Advances in Neural Information Processing Systems 23, 2011.Google ScholarDigital Library
- D. Wingate, A. Stuhlmueller, and N. Goodman. Lightweight implementations of probabilistic programming languages via transformational compilation. In pProceedings of the 14th international conference on Artificial Intelligence and Statistics, page 131, 2011.Google Scholar
- L. Yang, P. Hanrahan, and N. Goodman. Incrementalizing mcmc on probabilistic programs through tracing and slicing. Under review.Google Scholar
- Y. Yeh, L. Yang, M. Watson, N. Goodman, and P. Hanrahan. Synthesizing open worlds with constraints using locally annealed reversible jump mcmc. ACM Transactions on Graphics (TOG), 31 (4): 56, 2012. Google ScholarDigital Library
Index Terms
- The principles and practice of probabilistic programming
Recommendations
The principles and practice of probabilistic programming
POPL '13: Proceedings of the 40th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languagesProving expected sensitivity of probabilistic programs with randomized variable-dependent termination time
The notion of program sensitivity (aka Lipschitz continuity) specifies that changes in the program input result in proportional changes to the program output. For probabilistic programs the notion is naturally extended to expected sensitivity. A ...
Probabilistic (logic) programming concepts
A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own ...
Comments