You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Optimum Monte-Carlo Sampling Using Markov Chains
P. H. Peskun
Vol. 60, No. 3 (Dec., 1973), pp. 607-612
Stable URL: http://www.jstor.org/stable/2335011
Page Count: 6
You can always find the topics here!Topics: Sampling methods, Markov chains, Matrices, Eigenvalues, Simulations, Mathematical optima, Estimation bias, Sample size, Preliminary estimates, Estimate reliability
Were these topics helpful?See somethings inaccurate? Let us know!
Select the topics that are inaccurate.
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
The sampling method proposed by Metropolis et al. (1953) requires the simulation of a Markov chain with a specified π as its stationary distribution. Hastings (1970) outlined a general procedure for constructing and simulating such a Markov chain. The matrix P of transition probabilities is constructed using a defined symmetric function sij and an arbitrary transition matrix Q. Here, for a given Q, the relative merits of the two simple choices for sij suggested by Hastings (1970) are discussed. The optimum choice for sij is shown to be one of these. For the other choice, those Q are given which are known to make the sampling method based on P asymptotically less precise than independent sampling.
Biometrika © 1973 Biometrika Trust