An Efficient Parallel Simulation Method for Posterior Inference on Paths of Markov Processes

7 Pages Posted: 21 Oct 2014

See all articles by Matthias Held

Matthias Held

WHU - Otto Beisheim School of Management

Marcel Omachel

WHU - Otto Beisheim School of Management

Date Written: October 17, 2014

Abstract

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

Held, Matthias and Omachel, Marcel, An Efficient Parallel Simulation Method for Posterior Inference on Paths of Markov Processes (October 17, 2014). Available at SSRN: https://ssrn.com/abstract=2511221 or http://dx.doi.org/10.2139/ssrn.2511221

Matthias Held (Contact Author)

WHU - Otto Beisheim School of Management ( email )

Burgplatz 2
Vallendar, 56179
Germany

Marcel Omachel

WHU - Otto Beisheim School of Management ( email )

Burgplatz 2
Vallendar, 56179
Germany

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