In this paper we present a language for finite state con tinuous time Bayesian networks (CTBNs), which de scribe structured stochastic processes that evolve over continuous time. The state of the system is decom posed into a set of local variables whose values change over time. The dynamics of the system are descnbed by specifying the behavior of each local variable as a function of its parents in a directed (possibly cyclic) graph. The model specifies, at any given point in time, the distribution over two aspects: when a local variable changes its value and the next value it takes. These distributions are determined by the variable's current value and the current values of its parents in the graph. More formally, each variable is modelled as a finite state continuous time Markov process whose transi tion intensities are functions of its parents. We present a probabilistic semantics for the language in terms of the generative model a CTBN defines over sequences of events. We list types of queries one might ask of a CTBN, discuss the conceptual and computational diffi culties associated with exact inference, and provide an algorithm for approximate inference which takes ad vantage of the structure within the process.
Continuous Time Bayesian Networks (CTBN) REF are homogeneous Markovian models of the joint trajectories of discrete finite variables, analogous to DBNs.
Continuous Time Bayesian Networks
arXiv: Artificial Intelligence
Computer Science Mathematics