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…
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9 results for “CTBNs”
Novel approach to learn CTBNs from data with minimal interventions.
problem Learning CTBNs from time-course data with limited resources.
method Variational approximation of expected information gain for experimental design.
result Semi-analytical expression for structure and parameter learning.
Modeling cascading behavior in complex systems using CTBNs.
problem Understanding which states trigger cascading events in complex systems.
method Continuous-time Bayesian networks (CTBNs) for modeling and identifying likely sentry states.
result Identification of likely sentry states that may lead to cascading behavior.
Researchers develop a method to learn CTBN structure using penalized maximum likelihood.
problem Learning the structure of continuous-time Bayesian networks (CTBNs) when the dependence structure is unknown.
method Penalized maximum likelihood method.
result The algorithm can accurately identify the network structure with high probability under certain conditions.
Continuous-time Bayesian Networks (CTBNs) represent a compact yet powerful framework for understanding multivariate time-series data. Given complete data, parameters and structure can be estimated efficiently in closed-form. However, if data is incomplete, the latent states of the CTBN have to be estimated by laborious…
Continuous-time Bayesian networks (CTBNs) constitute a general and powerful framework for modeling continuous-time stochastic processes on networks. This makes them particularly attractive for learning the directed structures among interacting entities. However, if the available data is incomplete, one needs to simulat…
New CTBNs with clocks allow for non-exponential survival times.
problem Modeling phenomena with non-exponential survival times in continuous time.
method Introduced node-wise clocks to construct graph-coupled semi-Markov chains, enabling non-exponential survival times without auxiliary states.
result Parameter and structure inference algorithms provided, demonstrating advantages over current CTBN extensions.
We demonstrate that a number of sociology models for social network dynamics can be viewed as continuous time Bayesian networks (CTBNs). A sampling-based approximate inference method for CTBNs can be used as the basis of an expectation-maximization procedure that achieves better accuracy in estimating the parameters of…
The thesis tackles structure learning and parameter estimation for PGMs using penalized maximum likelihood methods.
problem Recovering the true structure of probabilistic graphical models (PGMs) for decision-making and interpretation.
method Penalized maximum likelihood estimation with the LASSO penalty.
result The approach successfully recovers the true structure of PGMs, including Bayesian networks and continuous time Bayesian networks, for both complete and incomplete data.