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Chib, S. (2001) Markov Chain Monte Carlo Methods: Computation and Inference. In: Heckman, J.J. and Leamer, E., Eds., Handbook of Econometrics, Elsevier, Amsterdam, 3569-3649.

has been cited by the following article:

• TITLE: Comparison of the Sampling Efficiency in Spatial Autoregressive Model

  AUTHORS: Yoshihiro Ohtsuka, Kazuhiko Kakamu

  KEYWORDS: Acceptance-Rejection Metropolis-Hastings Algorithm, Griddy Gibbs Sampler, Markov Chain Monte Carlo (MCMC), Random Walk Metropolis-Hastings Algorithm, Spatial Autoregressive Model

  JOURNAL NAME: Open Journal of Statistics, Vol.5 No.1, January 22, 2015

  ABSTRACT: A random walk Metropolis-Hastings algorithm has been widely used in sampling the parameter of spatial interaction in spatial autoregressive model from a Bayesian point of view. In addition, as an alternative approach, the griddy Gibbs sampler is proposed by [1] and utilized by [2]. This paper proposes an acceptance-rejection Metropolis-Hastings algorithm as a third approach, and compares these three algorithms through Monte Carlo experiments. The experimental results show that the griddy Gibbs sampler is the most efficient algorithm among the algorithms whether the number of observations is small or not in terms of the computation time and the inefficiency factors. Moreover, it seems to work well when the size of grid is 100.