Probabilistic reasoning in intelligent systems: networks of plausible inference
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Probabilistic Approximations of Signaling Pathway Dynamics
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Continuous time particle filtering
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Continuous Time Bayesian Network Reasoning and Learning Engine
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Update rules for parameter estimation in continuous time Bayesian network
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Mean field variational approximation for continuous-time Bayesian networks
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
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UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Modeling the evolution of associated data
Data & Knowledge Engineering
Importance Sampling for Continuous Time Bayesian Networks
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PutMode: prediction of uncertain trajectories in moving objects databases
Applied Intelligence
Intrusion detection using continuous time Bayesian networks
Journal of Artificial Intelligence Research
Mean Field Variational Approximation for Continuous-Time Bayesian Networks
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Probabilistic approximations of ODEs based bio-pathway dynamics
Theoretical Computer Science
Towards proactive event-driven computing
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Learning continuous time bayesian networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
A temporal policy for trusting information
Trusting Agents for Trusting Electronic Societies
Continuous time Bayesian network classifiers
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SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Simulation metamodeling in continuous time using dynamic Bayesian networks
Proceedings of the Winter Simulation Conference
International Journal of Approximate Reasoning
An n-gram topic model for time-stamped documents
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Fast MCMC sampling for Markov jump processes and extensions
The Journal of Machine Learning Research
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In this paper we present a language for finite state continuous time Bayesian networks (CTBNs), which describe structured stochastic processes that evolve over continuous time. The state of the system is decomposed into a set of local variables whose values change over time. The dynamics of the system are described by specifying the behavior of each local variable as a function of its parents in a directed (possibly cyclic) graph. The model specifies, at any given point in time, the distribution over two aspects: when a local variable changes its value and the next value it takes. These distributions are determined by the variable's current value and the current values of its parents in the graph. More formally, each variable is modelled as a finite state continuous time Markov process whose transition intensities are functions of its parents. We present a probabilistic semantics for the language in terms of the generative model a CTBN defines over sequences of events. We list types of queries one might ask of a CTBN, discuss the conceptual and computational difficulties associated with exact inference, and provide an algorithm for approximate inference which takes advantage of the structure within the process.