
Expectation Propagation for Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBNs) describe structured stochastic...
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Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBNs) describe structured stochastic...
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Continuous Time Bayesian Networks
In this paper we present a language for finite state continuous time Bay...
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Mean Field Variational Approximation for ContinuousTime Bayesian Networks
Continuoustime Bayesian networks is a natural structured representation...
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Cluster Variational Approximations for Structure Learning of ContinuousTime Bayesian Networks from Incomplete Data
Continuoustime Bayesian networks (CTBNs) constitute a general and power...
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Gibbs Sampling in Factorized ContinuousTime Markov Processes
A central task in many applications is reasoning about processes that ch...
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Scalable Structure Learning of ContinuousTime Bayesian Networks from Incomplete Data
Continuoustime Bayesian Networks (CTBNs) represent a compact yet powerf...
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Learning Continuous Time Bayesian Networks
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 parameters and structure of a CTBN from fully observed data. We define a conjugate prior for CTBNs, and show how it can be used both for Bayesian parameter estimation and as the basis of a Bayesian score for structure learning. Because acyclicity is not a constraint in CTBNs, we can show that the structure learning problem is significantly easier, both in theory and in practice, than structure learning for dynamic Bayesian networks (DBNs). Furthermore, as CTBNs can tailor the parameters and dependency structure to the different time granularities of the evolution of different variables, they can provide a better fit to continuoustime processes than DBNs with a fixed time granularity.
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