Adaptive Markov Chain Monte Carlo Sampling and Estimation in Mata
نویسندگان
چکیده
منابع مشابه
Adaptive Markov Chain Monte Carlo Sampling and Estimation in Mata
I describe algorithms for drawing from distributions using adaptive Markov chain Monte Carlo (MCMC) methods, introduce a Mata function for performing adaptive MCMC, amcmc(), and a suite of functions amcmc *() allowing an alternative implementation of adaptive MCMC. amcmc() and amcmc *() may be used in conjunction with models set up to work with Mata’s [M-5] moptimize( ) or [M-5] optimize( ), or...
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A major limitation towards more widespread implementation of Bayesian approaches is that obtaining the posterior distribution often requires the integration of high-dimensional functions. This can be computationally very difficult, but several approaches short of direct integration have been proposed (reviewed by Smith 1991, Evans and Swartz 1995, Tanner 1996). We focus here on Markov Chain Mon...
متن کاملMarkov Chain Monte Carlo and Gibbs Sampling
A major limitation towards more widespread implementation of Bayesian approaches is that obtaining the posterior distribution often requires the integration of high-dimensional functions. This can be computationally very difficult, but several approaches short of direct integration have been proposed (reviewed by Smith 1991, Evans and Swartz 1995, Tanner 1996). We focus here on Markov Chain Mon...
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In Adaptive Markov Chain Monte Carlo (AMCMC) simulation, classical estimators of asymptotic variances are inconsistent in general. In this work we establish that despite this inconsistency, confidence interval procedures based on these estimators remain consistent. We study two classes of confidence intervals, one based on the standard Gaussian limit theory, and the class of so-called fixed-b c...
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ژورنال
عنوان ژورنال: The Stata Journal: Promoting communications on statistics and Stata
سال: 2014
ISSN: 1536-867X,1536-8734
DOI: 10.1177/1536867x1401400309