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

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.

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Year of publication:	2014-10
Authors:	Held, Matthias ; Omachel, Marcel
Institutions:	Fakultät für Wirtschaftswissenschaft, Otto-von-Guericke-Universität Magdeburg
Subject:	Bayesian inference \| Markov Chain Monte Carlo \| Posterior path simulation

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Extent:	application/pdf
Series:	FEMM Working Papers.
Type of publication:	Book / Working Paper
Notes:	Number 140010 9 pages
Classification:	C11 - Bayesian Analysis ; C15 - Statistical Simulation Methods; Monte Carlo Methods
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010960419