Focused belief propagation for query-specific inference

Journal of Machine Learning Research

Volumn 9, Issue , 2010, Pages 89-96

  Chechetka, Anton ^a   Guestrin, Carlos ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE INFERENCE; BELIEF PROPAGATION; CONVERGENCE SPEEDUP; END USERS; GRAPHICAL MODEL; PROBABILISTIC GRAPHICAL MODELS; STATE OF THE ART;

ARTIFICIAL INTELLIGENCE;

GRAPHIC METHODS;

EID: 84862291043     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (21)

1. I. Beinlich, J. Suermondt, M. Chavez, and G. Cooper. The ALARM monitoring system: A case study with two probablistic inference techniques for belief networks. In ECAIM, 1988.
2. A. Choi and A. Darwiche. Focusing generalizations of belief propagation on targeted queries. In AAAI, 2008.
3. A. Choi, H. Chan, and A. Darwiche. On Bayesian network approximation by edge deletion. In UAI, 2005.
4. A. Deshpande, C. Guestrin, S. Madden, J. Hellerstein, and W. Hong. Model-driven data acquisition in sensor networks. In VLDB, 2004.
5. G. Elidan, I. Mcgraw, and D. Koller. Residual belief propagation: Informed scheduling for asynchronous message passing. In UAI, 2006.
6. J. Gonzalez, Y. Low, and C. Guestrin. Residual splash for optimally parallelizing belief propagation. In AISTATS, 2009.
7. A. Ihler. Accuracy bounds for belief propagation. In UAI, July 2007.
8. U. Kjaerulff. Approximation of Bayesian networks through edge removals. Technical report, Aalborg University, 1993.
9. D. Koller and N. Friedman. Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009.
10. R. McEliece, D. MacKay, and J. Cheng. Turbo decoding as an instance of Pearl's belief propagation algorithm. IEEE Journal on Selected Areas in Communications, 16 (2):140-152, 1998.
11. J. Mooij and H. Kappen. Suffcient conditions for convergence of the sum-product algorithm. IEEE Trans. on Inf. Theory, 12(53), 2007.
12. J. M. Mooij and H. J. Kappen. Bounds on marginal probability distributions. In NIPS, 2008.
13. J. Pearl. Probabilistic reasoning in intelligent systems: networks of plausible inference. 1988.
14. W. Pentney, A.-M. Popescu, S. Wang, H. A. Kautz, and M. Philipose. Sensor-based understanding of daily life via large-scale use of common sense. In AAAI, 2006.
15. W. Pentney, M. Philipose, J. A. Bilmes, and H. A. Kautz. Learning large scale common sense models of everyday life. In AAAI, 2007.
16. M. Richardson and P. Domingos. Markov logic networks. Machine Learning, 62(1-2), 2006.
17. D. Roth. On the hardness of approximate reasoning. Artificial Intelligence, 82(1-2), 1996.
18. R. A. van Engelen. Approximating Bayesian belief networks by arc removal. IEEE Trans. on Patt. Analysis and Mach. Intelligence, 19(8), 1997.
19. M. P. Wellman, J. S. Breese, and R. P. Goldman. From knowledge bases to decision models. Knowledge Engineering Review, 7:35-53, 1992.
20. C. Yanover and Y. Weiss. Approximate inference and protein folding. In NIPS, 2002.
21. J. Yedidia, W. Freeman, and Y. Weiss. Understanding belief propagation and its generalizations. In Exploring artificial intelligence in the new millennium. Morgan Kaufmann Publishers Inc., 2003.