Update rules for parameter estimation in continuous time Bayesian network

Continuous time Bayesian network is a new kind of dynamic graphical models developed in recent year, which describe structured stochastic processes with finitely many states that evolve over continuous time. The parameters for each variable in the model represent a finite state continuous time Markov process, whose transition model is a function of its parents. This paper presents an algorithm for updating parameters from an existing CTBN model with a set of data samples. It is a unified framework for online parameter estimation and batch parameter updating where a pre-accumulated set of samples is used. We analyze different conditions of the algorithm, and show its performance in experiments.