jmlr jmlr2010 jmlr2010-39 jmlr2010-39-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elidan
Abstract: The FastInf C++ library is designed to perform memory and time efficient approximate inference in large-scale discrete undirected graphical models. The focus of the library is propagation based approximate inference methods, ranging from the basic loopy belief propagation algorithm to propagation based on convex free energies. Various message scheduling schemes that improve on the standard synchronous or asynchronous approaches are included. Also implemented are a clique tree based exact inference, Gibbs sampling, and the mean field algorithm. In addition to inference, FastInf provides parameter estimation capabilities as well as representation and learning of shared parameters. It offers a rich interface that facilitates extension of the basic classes to other inference and learning methods. Keywords: graphical models, Markov random field, loopy belief propagation, approximate inference
A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, B 39:1–39, 1977. G. Elidan, I. McGraw, and D. Koller. Residual belief propagation: Informed scheduling for asynchronous message passing. In UAI 2006. 1735 JAIMOVICH , M ESHI , M C G RAW AND E LIDAN G. Elidan, G. Heitz, and D. Koller. Learning object shape: From drawings to images. In CVPR 2006. N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In IJCAI 1999. M. Fromer and C. Yanover. A computational framework to empower probabilistic protein design. In Bioinformatics, pages 214–222, 2008. S. Geman and D. Geman. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans. on Pattern Analysis and Machine Intelligence, pages 721–741, 1984. L. Getoor, N. Friedman, D. Koller, and B. Taskar. Learning probabilistic models of relational structure. In ICML 2001. A. Jaimovich, G. Elidan, H. Margalit, and N. Friedman. Towards an integrated protein-protein interaction network. In RECOMB, 2005. M. I. Jordan, Z. Ghahramani, T. Jaakkola, and L. K. Saul. An introduction to variational approximations methods for graphical models. In M. I. Jordan, editor, Learning in Graphical Models. Kluwer, Dordrecht, Netherlands, 1998. S. L. Lauritzen and D. J. Spiegelhalter. Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, 1988. R. McEliece, D. McKay, and J. Cheng. Turbo decoding as an instance of pearl’s belief propagation algorithm. IEEE Journal on Selected Areas in Communication, 16:140–152, 1998. O. Meshi, A. Jaimovich, A. Globerzon, and N. Friedman. Convexifying the bethe free energy. In UAI 2009. K. Murphy and Y. Weiss. Loopy belief propagation for approximate inference: An empirical study. In UAI 1999. J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco, California, 1988. N. Shental, A. Zommet, T. Hertz, and Y. Weiss. Learning and inferring image segmentations with the GBP typical cut algorithm. In ICCV 2003. M. J. Wainwright, T. Jaakkola, and A. S. Willsky. Tree-based reparameterization for approximate estimation on loopy graphs. In NIPS 2002. M. J. Wainwright, T. Jaakkola, and A. S. Willsky. Exact map estimates by (hyper)tree agreement. In NIPS 2002. M. J. Wainwright, T.S. Jaakkola, and A. S. Willsky. A new class of upper bounds on the log partition function. IEEE Transactions on Information Theory, 51(7):2313–2335, 2005. W. Wiegerinck and T. Heskes. Fractional belief propagation. In NIPS 2002. J.S. Yedidia, W.T. Freeman, and Y. Weiss. Constructing free energy approximations and generalized belief propagation algorithms. IEEE Transactions on Information Theory, 51:2282–2312, 2005. 1736