Continuous time Markov networks

Tal El-Hay*, Nir Friedman, Daphne Koller, Raz Kupferman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations


A central task in many applications is reasoning about processes that change over continuous time. Recently, Nodelman et al. introduced continuous time Bayesian networks (CTBNs), a structured representation for representing Continuous Time Markov Processes over a structured state space. In this paper, we introduce continuous time Markov networks (CTMNs), an alternative representation language that represents a different type of continuous-time dynamics, particularly appropriate for modeling biological and chemical systems. In this language, the dynamics of the process is described as an interplay between two forces: the tendency of each entity to change its state, which we model using a continuous-time proposal process that suggests possible local changes to the state of the system at different rates; and a global fitness or energy function of the entire system, governing the probability that a proposed change is accepted, which we capture by a Markov network that encodes the fitness of different states. We show that the fitness distribution is also the stationary distribution of the Markov process, so that this representation provides a characterization of a temporal process whose stationary distribution has a compact graphical representation. We describe the semantics of the representation, its basic properties, and how it compares to CTBNs. We also provide an algorithm for learning such models from data, and demonstrate its potential benefit over other learning approaches.

Original languageAmerican English
Title of host publicationProceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006
Number of pages10
StatePublished - 2006
Event22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006 - Cambridge, MA, United States
Duration: 13 Jul 200616 Jul 2006

Publication series

NameProceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006


Conference22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006
Country/TerritoryUnited States
CityCambridge, MA


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