Note on the Sampling Distribution for the Metropolis-Hastings Algorithm
نویسندگان
چکیده
The Metropolis-Hastings algorithm has been important in the recent development of Bayes methods. This algorithm generates random draws from a target distribution utilizing a sampling (or proposal) distribution. This article compares the properties of three sampling distributions—the independence chain, the random walk chain, and the Taylored chain suggested by Geweke and Tanizaki (Geweke, J., Tanizaki, H. (1999). On Markov Chain Monte-Carlo methods for nonlinear and non-Gaussian state-space models. Communications in Statistics, Simulation and Computation 28(4):867–894, Geweke, J., Tanizaki, H. (2001). Bayesian estimation of state-space model *Correspondence: Hisashi Tanizaki, Graduate School of Economics, Kobe University, Rokkodaicho 2-1, Nadaku, Kobe 657-8501, Japan; E-mail: [email protected].
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