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

Matthias Held () and Marcel Omachel ()
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Matthias Held: Faculty of Finance, WHU - Otto Beisheim School of Management
Marcel Omachel: Faculty of Finance, WHU - Otto Beisheim School of Management

No 140010, FEMM Working Papers from Otto-von-Guericke University Magdeburg, Faculty of Economics and Management

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 (search for similar items in EconPapers)
JEL-codes: C11 C15 (search for similar items in EconPapers)
Pages: 9 pages
Date: 2014-10
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:mag:wpaper:140010

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