Continuous-time belief propagation

ICML 2010 - Proceedings, 27th International Conference on Machine Learning

Volumn , Issue , 2010, Pages 343-350

  El Hay, Tal ^a   Cohn, Ido ^a   Friedman, Nir ^a   Kupferman, Raz ^a  

^a HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

BELIEF PROPAGATION; CLOSE APPROXIMATION; CONNECTED NETWORKS; CONTINUOUS - TIME STOCHASTIC PROCESS; CONTINUOUS TIME; INDIVIDUAL COMPONENTS; INHOMOGENEOUS MARKOV PROCESS; MULTICOMPONENTS; NATURAL EXTENSION; REPRESENTATION LANGUAGES; THEORETICAL FOUNDATIONS; VARIATIONAL APPROXIMATION;

CONTINUOUS TIME SYSTEMS; INFERENCE ENGINES; LEARNING SYSTEMS; MARKOV PROCESSES; ORDINARY DIFFERENTIAL EQUATIONS; STOCHASTIC SYSTEMS;

BAYESIAN NETWORKS;

EID: 77956555218     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Chung, K.L. Markov chains with stationary transition probabilities. 1960.
2. Cohn, I., El-Hay, T., Friedman, N. and Kupferman, R. Mean field variational approximation for continuous-time Bayesian networks. UAI, 2009.
3. El-Hay, T., Friedman, N. and Kupferman, R. Gibbs sampling in factorized continuous-time markov processes. UAI, 2008.
4. Fan, Y. and Shelton, C. R. Learning continuous-time social network dynamics. UAI, 2009.
5. Fan, Y. and Shelton, CR. Sampling for approximate inference in continuous time Bayesian networks. In AI & Math, 2008.
6. Felsenstein, J. Inferring Phytogenies. Sinauer, 2004.
7. Heskes, T. and Zoeter, O. Expectation propagation for approximate inference in dynamic Bayesian networks. UAI, 2002.
8. Jordan, M.I., Ghahramani, Z., Jaakkola, T.S. and Saul, L. K. An introduction to variational approximations methods for graphical models. In Learning in Graphics Models. 1998.
9. Koller, D. and Friedman, N. Probabilistic Graphical Models: Principles and Techniques. 2009.
10. Minka, T. P. Expectation propagation for approximate Bayesian inference. UAI, 2001.
11. Nodelman, U., Shelton, C. R. and Koller, D. Continuous time Bayesian networks. UAI, 2002.
12. Nodelman, U., Shelton, C.R. and Koller, D. Expectation propagation for continuous time Bayesian networks. UAI, 2005.
13. Opper, M. and Sanguinetti, G. Variational inference for Markov jump processes. NIPS, 2007.
14. Saria, S., Nodelman, U. and Koller, D. Reasoning at the right time granularity. UAI, 2007.
15. Simma, A., Goldszmidt, M., MacCormick, M., Barham, M., Black, M., Isaacs, M. and Mortier, R. CT-NOR: Representing and reasoning about events in continuous time. UAI, 2008.
16. Wainwright, M. J. and Jordan, M. Graphical models, exponential families, and variational inference. Found. Trends Mach. Learn., 1:1-305, 2008.
17. Xu, J. and Shelton, C. R. Continuous time Bayesian networks for host level network intrusion detection. ECML/PKDD, 2008.
18. Yedidia, J.S., Freeman, W.T. and Weiss, Y. Constructing free-energy approximations and generalized belief propagation algorithms. IEEE Trans. Info. Theory, 51:2282-2312, 2005.