Sensitivity Analysis of Continuous Time Bayesian Networks Using Perturbation Realization

The continuous time Bayesian network (CTBN) model can be thought of as a factored Markov process. Sensitivity analysis of a Markov process is done by calculating partial derivatives of a user-defined performance function with respect to changes in the transition intensities of the Markov process. On the other hand, sensitivity analysis has yet to be applied to the CTBN model. To address this, we show how to extend the perturbation realization method for Markov process sensitivity analysis to the CTBN, which works on a sample path of the process. However, in a CTBN, the number of states is exponential in the number of nodes, making it difficult to create a sample path of the entire model that visits every state. We show how to exploit the conditional independence structure of the CTBN to build sample paths and compute performance measure derivatives independently for different subnetworks.