Continuous time Bayesian network reasoning and learning engine. 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.
Keywords for this software
References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Delcroix, Véronique; Grislin-Le Strugeon, Emmanuelle; Puisieux, François: A knowledge based system for the management of a time stamped uncertain observation set with application on preserving mobility (2021)
- Liu, Manxia; Hommersom, Arjen; van der Heijden, Maarten; Lucas, Peter J. F.: Hybrid time Bayesian networks (2017)
- Sturlaugson, Liessman; Sheppard, John W.: Uncertain and negative evidence in continuous time Bayesian networks (2016)
- Rao, Vinayak; Teh, Yee Whye: Fast MCMC sampling for Markov jump processes and extensions (2013)
- Fan, Yu; Xu, Jing; Shelton, Christian R.: Importance sampling for continuous time Bayesian networks (2010)
- Shelton, Christian R.; Fan, Yu; Lam, William; Lee, Joon; Xu, Jing: Continuous time Bayesian network reasoning and learning engine (2010)
Further publications can be found at: http://rlair.cs.ucr.edu/papers/