R
Ronald C. Neath
Researcher at Columbia University
Publications - 10
Citations - 586
Ronald C. Neath is an academic researcher from Columbia University. The author has contributed to research in topics: Markov chain Monte Carlo & Monte Carlo method. The author has an hindex of 8, co-authored 10 publications receiving 544 citations. Previous affiliations of Ronald C. Neath include University of Minnesota & City University of New York.
Papers
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Journal ArticleDOI
Fixed-width output analysis for Markov chain Monte Carlo
TL;DR: In this article, the authors consider a method that stops the simulation when the width of a confidence interval based on an ergodic average is less than a user-specified value.
Book ChapterDOI
On Convergence Properties of the Monte Carlo EM Algorithm
TL;DR: It is shown that if the EM algorithm converges it converges to a stationary point of the likelihood, and that the rate of convergence is linear at best, which is an accessible but rigorous approach to the convergence properties of EM and Monte Carlo EM.
Journal ArticleDOI
Component-Wise Markov Chain Monte Carlo: Uniform and Geometric Ergodicity under Mixing and Composition
TL;DR: In this paper, the convergence properties of component-wise Markov chain Monte Carlo (MCMC) simulations have been investigated and the connections between the convergence rates of various componentwise strategies have been analyzed.
Journal ArticleDOI
Component-Wise Markov Chain Monte Carlo: Uniform and Geometric Ergodicity under Mixing and Composition
TL;DR: In this article, the convergence properties of component-wise Markov chains are studied and conditions under which some componentwise MCMC chains converge to the stationary distribution at a geometric rate.
Journal ArticleDOI
Markov chain Monte Carlo estimation of quantiles
TL;DR: In this article, quantile estimation using Markov chain Monte Carlo is considered and conditions under which the sampling distribution of the Monte Carlo error is approximately Normal are established, which enables construction of an asymptotically valid interval estimator.