Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks

@article{Nodelman2005ExpectationMA,
  title={Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks},
  author={Uri Nodelman and Christian R. Shelton and Daphne Koller},
  journal={CoRR},
  year={2005},
  volume={abs/1207.1402}
}
Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is a function of its parents. We address the problem of learning the parameters and structure of a CTBN from partially observed data. We show how to apply expectation maximization… CONTINUE READING
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