State space modeling of time series
State space modeling of time series
A model for reasoning about persistence and causation
Computational Intelligence
A Comparison of New and Old Algorithms for a Mixture EstimationProblem
Machine Learning - Special issue on the eighth annual conference on computational learning theory, (COLT '95)
Continuous time bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Update rules for parameter estimation in Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning continuous time bayesian networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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Continuous time Bayesian network is a new kind of dynamic graphical models developed in recent year, which describe structured stochastic processes with finitely many states that evolve over continuous time. The parameters for each variable in the model represent a finite state continuous time Markov process, whose transition model is a function of its parents. This paper presents an algorithm for updating parameters from an existing CTBN model with a set of data samples. It is a unified framework for online parameter estimation and batch parameter updating where a pre-accumulated set of samples is used. We analyze different conditions of the algorithm, and show its performance in experiments.