jmlr jmlr2010 jmlr2010-28 jmlr2010-28-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Christian R. Shelton, Yu Fan, William Lam, Joon Lee, Jing Xu
Abstract: We present a continuous time Bayesian network reasoning and learning engine (CTBN-RLE). A continuous time Bayesian network (CTBN) provides a compact (factored) description of a continuoustime Markov process. This software provides libraries and programs for most of the algorithms developed for CTBNs. For learning, CTBN-RLE implements structure and parameter learning for both complete and partial data. For inference, it implements exact inference and Gibbs and importance sampling approximate inference for any type of evidence pattern. Additionally, the library supplies visualization methods for graphically displaying CTBNs or trajectories of evidence. Keywords: continuous time Bayesian networks, C++, open source software
Gunter Bolch, Stefan Greiner, Hermann de Meer, and Kishor S. Trivedi. Queueing Networks and Markov Chains. John Wiley & Sons, Inc., 1998. Gianfranco Ciardo and Andrew S. Miner. A data structure for the efficient Kronecker solution of GSPNs. In Proceedings of the 8th International Workshop on Petri Nets and Performance Models, pages 22–31, 1999. Tal El-Hay, Nir Friedman, and Raz Kupferman. Gibbs sampling in factorized continuous-time Markov processes. In Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence, pages 169–178, 2008. Yu Fan and Christian R. Shelton. Sampling for approximate inference in continuous time Bayesian networks. In Proceedings of the Tenth International Symposium on Artificial Intelligence and Mathematics, 2008. Uri Nodelman, Christian R. Shelton, and Daphne Koller. Continuous time Bayesian networks. In Proceedings of the Eighteenth International Conference on Uncertainty in Artificial Intelligence, pages 378–387, 2002. Uri Nodelman, Christian R. Shelton, and Daphne Koller. Learning continuous time Bayesian networks. In Proceedings of the Nineteenth International Conference on Uncertainty in Artificial Intelligence, pages 451–458, 2003. Uri Nodelman, Christian R. Shelton, and Daphne Koller. Expectation maximization and complex duration distributions for continuous time Bayesian networks. In Proceedings of the Twenty-First International Conference on Uncertainty in Artificial Intelligence, pages 421–430, 2005. Carl A. Petri. Kommunikation mit Automaten. PhD thesis, University of Bonn, 1962. 1140