A model for reasoning about persistence and causation
Computational Intelligence
Queueing networks and Markov chains: modeling and performance evaluation with computer science applications
Simulation Approaches to General Probabilistic Inference on Belief Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Expectation Propagation for approximate Bayesian inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
SYSML'07 Proceedings of the 2nd USENIX workshop on Tackling computer systems problems with machine learning techniques
Continuous Time Bayesian Networks for Host Level Network Intrusion Detection
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Continuous time particle filtering
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Continuous Time Bayesian Network Reasoning and Learning Engine
The Journal of Machine Learning Research
Probabilistic inference in queueing networks
SysML'08 Proceedings of the Third conference on Tackling computer systems problems with machine learning techniques
Continuous time bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Learning continuous time bayesian networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Improving Gaussian process value function approximation in policy gradient algorithms
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Continuous time Bayesian network classifiers
Journal of Biomedical Informatics
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A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact inference in a CTBN is often intractable as the state space of the dynamic system grows exponentially with the number of variables. In this paper, we first present an approximate inference algorithm based on importance sampling. We then extend it to continuous-time particle filtering and smoothing algorithms. These three algorithms can estimate the expectation of any function of a trajectory, conditioned on any evidence set constraining the values of subsets of the variables over subsets of the time line. We present experimental results on both synthetic networks and a network learned from a real data set on people's life history events. We show the accuracy as well as the time efficiency of our algorithms, and compare them to other approximate algorithms: expectation propagation and Gibbs sampling.