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Found 6 papers, 0 papers with code
Augmenting Continuous Time Bayesian Networks with Clocks
Structured stochastic processes evolving in continuous time present a widely adopted framework to model phenomena occurring in nature and engineering.
Active Learning of Continuous-time Bayesian Networks through Interventions
We propose a novel criterion for experimental design based on a variational approximation of the expected information gain.
Continuous-Time Bayesian Networks with Clocks
Structured stochastic processes evolving in continuous time present a widely adopted framework to model phenomena occurring in nature and engineering.
A Variational Perturbative Approach to Planning in Graph-based Markov Decision Processes
We present a novel approximate solution method for multi-agent Markov decision problems on graphs, based on variational perturbation theory.
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Instead of sampling and scoring all possible structures individually, we assume the generator of the CTBN to be composed as a mixture of generators stemming from different structures.
Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Existing approximation techniques, such as sampling and low-order variational methods, either scale unfavorably in system size, or are unsatisfactory in terms of accuracy.
