179 citations · 363 across the 5 of their papers we have counts for
5 papers
Continuous Time Bayesian Networks
Uri Nodelman, Christian R. Shelton, Daphne Koller
In this paper we present a language for finite state continuous time Bayesian networks (CTBNs), which describe structured stochastic processes that evolve over continuous time. The…
Learning Continuous Time Bayesian Networks
Uri Nodelman, Christian R. Shelton, Daphne Koller
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…
Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks
Uri Nodelman, Christian R. Shelton, Daphne Koller
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…
Expectation Propagation for Continuous Time Bayesian Networks
Uri Nodelman, Daphne Koller, Christian R. Shelton
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…
Reasoning at the Right Time Granularity
Suchi Saria, Uri Nodelman, Daphne Koller
Most real-world dynamic systems are composed of different components that often evolve at very different rates. In traditional temporal graphical models, such as dynamic Bayesian n…