Content-Type: text/html R-LAIR: Riverside Lab for Artificial Intelligence Research

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R-LAIR: Riverside Lab for Artificial Intelligence Research

Continuous Time Bayesian Network Reasoning and Learning Engine (2010)

by Christian R. Shelton, Yu Fan, William Lam, Joon Lee, and 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 continuous-time 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.

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Christian R. Shelton, Yu Fan, William Lam, Joon Lee, and Jing Xu (2010). "Continuous Time Bayesian Network Reasoning and Learning Engine." Journal of Machine Learning Research, 11(Mar), 1137-1140. pdf   code  

Bibtex citation

@article{Sheetal10,
   author = "Christian R. Shelton and Yu Fan and William Lam and Joon Lee and Jing Xu",
   title = "Continuous Time {B}ayesian Network Reasoning and Learning Engine",
   journal = "Journal of Machine Learning Research",
   volume = 11,
   number = "Mar",
   pages = "1137--1140",
   year = 2010,
}

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