Efficient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing.

ABSTRACT: Branching processes are a class of continuous-time Markov chains (CTMCs) with ubiquitous applications. A general difficulty in statistical inference under partially observed CTMC models arises in computing transition probabilities when the discrete state space is large or uncountable. Classical methods such as matrix exponentiation are infeasible for large or countably infinite state spaces, and sampling-based alternatives are computationally intensive, requiring integration over all possible hidden events. Recent work has successfully applied generating function techniques to computing transition probabilities for linear multi-type branching processes. While these techniques often require significantly fewer computations than matrix exponentiation, they also become prohibitive in applicat

SUBMITTER: Xu J 

PROVIDER: S-EPMC4775097 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature