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An efficient proposal distribution for Metropolis–Hastings using a B-splines technique


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  • Shao, Wei
  • Guo, Guangbao
  • Meng, Fanyu
  • Jia, Shuqin
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    In this paper, we proposed an efficient proposal distribution in the Metropolis–Hastings algorithm using the B-spline proposal Metropolis–Hastings algorithm. This new method can be extended to high-dimensional cases, such as the B-spline proposal in Gibbs sampling and in the Hit-and-Run (BSPHR) algorithm. It improves the proposal distribution in the Metropolis–Hastings algorithm by carrying more information from the target function. The performance of BSPHR was compared with that of other Markov Chain Monte Carlo (MCMC) samplers in simulation and real data examples. Simulation results show that the new method performs significantly better than other MCMC methods.

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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 57 (2013)
    Issue (Month): 1 ()
    Pages: 465-478

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    Handle: RePEc:eee:csdana:v:57:y:2013:i:1:p:465-478

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    Keywords: Efficient proposal distribution; Metropolis–Hastings algorithm; B-splines; Gibbs sampling; Markov Chain Monte Carlo;


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    1. Giovanni Petris, . "An R Package for Dynamic Linear Models," Journal of Statistical Software, American Statistical Association, American Statistical Association, vol. 36(i12).
    2. Liang F. & Wong W.H., 2001. "Real-Parameter Evolutionary Monte Carlo With Applications to Bayesian Mixture Models," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 96, pages 653-666, June.
    3. S. Illeris & G. Akehurst, 2002. "Introduction," The Service Industries Journal, Taylor & Francis Journals, vol. 22(1), pages 1-3, January.
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    Cited by:
    1. Fabrizio Leisen & Roberto Casarin & David Luengo & Luca Martino, 2013. "Adaptive Sticky Generalized Metropolis," Working Papers 2013:19, Department of Economics, University of Venice "Ca' Foscari".


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