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Continuous-Time Bayesian Networks with Clocks
2020
Zenodo
Structured stochastic processes evolving in continuous time present a widely adopted framework to model phenomena occurring in nature and engineering. However, such models are often chosen to satisfy the Markov property to maintain tractability. One of the more popular of such memoryless models are Continuous Time Bayesian Networks (CTBNs). In this work, we lift its restriction to exponential survival times to arbitrary distributions. Current extensions achieve this via auxiliary states, which
doi:10.5281/zenodo.3957198
fatcat:fkn6qt6klffd7coxyszf4a7vqm