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Distributed evolutionary Monte Carlo for Bayesian computing

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  • Hu, Bo
  • Tsui, Kam-Wah

Abstract

Sampling from a multimodal and high-dimensional target distribution posits a great challenge in Bayesian analysis. A new Markov chain Monte Carlo algorithm Distributed Evolutionary Monte Carlo (DGMC) is proposed for real-valued problems, which combines the attractive features of the distributed genetic algorithm and the Markov chain Monte Carlo. The DGMC algorithm evolves a population of Markov chains through some genetic operators to simulate the target function. Theoretical justification proves that the DGMC algorithm has the target function as its stationary distribution. The effectiveness of the DGMC algorithm is illustrated by simulating two multimodal distributions and an application to a real data example.

Suggested Citation

  • Hu, Bo & Tsui, Kam-Wah, 2010. "Distributed evolutionary Monte Carlo for Bayesian computing," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 688-697, March.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:3:p:688-697
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    References listed on IDEAS

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    1. Roberts, G. O. & Gilks, W. R., 1994. "Convergence of Adaptive Direction Sampling," Journal of Multivariate Analysis, Elsevier, vol. 49(2), pages 287-298, May.
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    Cited by:

    1. Spezia, Luigi, 2020. "Bayesian variable selection in non-homogeneous hidden Markov models through an evolutionary Monte Carlo method," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    2. Rigat, F. & Mira, A., 2012. "Parallel hierarchical sampling: A general-purpose interacting Markov chains Monte Carlo algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1450-1467.

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