An Efficient Parallel Simulation Method for Posterior Inference on Paths of Markov Processes
7 Pages Posted: 21 Oct 2014
Date Written: October 17, 2014
In this note, we propose a method for efficient simulation of paths of latent Markovian state processes in a Markov Chain Monte Carlo setting. Our method harnesses available parallel computing power by breaking the sequential nature of commonly encountered state simulation routines. We offer a worked example that highlights the computational merits of our approach.
Keywords: Bayesian inference, Markov Chain Monte Carlo, Posterior path simulation
JEL Classification: C11, C15
Suggested Citation: Suggested Citation