Convergence diagnostics for markov chain monte carlo
V Roy - Annual Review of Statistics and Its Application, 2020 - annualreviews.org
Markov chain Monte Carlo (MCMC) is one of the most useful approaches to scientific
computing because of its flexible construction, ease of use, and generality. Indeed, MCMC is …
computing because of its flexible construction, ease of use, and generality. Indeed, MCMC is …
Markov chain Monte Carlo in practice
Markov chain Monte Carlo (MCMC) is an essential set of tools for estimating features of
probability distributions commonly encountered in modern applications. For MCMC …
probability distributions commonly encountered in modern applications. For MCMC …
[LIVRE][B] Introducing monte carlo methods with r
CP Robert, G Casella, G Casella - 2010 - Springer
The purpose of this book is to provide a self-contained entry into Monte Carlo computational
techniques. First and foremost, it must not be confused with a programming addendum to …
techniques. First and foremost, it must not be confused with a programming addendum to …
Multivariate output analysis for Markov chain Monte Carlo
SUMMARY Markov chain Monte Carlo produces a correlated sample which may be used for
estimating expectations with respect to a target distribution. A fundamental question is: when …
estimating expectations with respect to a target distribution. A fundamental question is: when …
Revisiting the gelman–rubin diagnostic
Abstract Gelman and Rubin's (Statist. Sci. 7 (1992) 457–472) convergence diagnostic is one
of the most popular methods for terminating a Markov chain Monte Carlo (MCMC) sampler …
of the most popular methods for terminating a Markov chain Monte Carlo (MCMC) sampler …
Dimension reduction and alleviation of confounding for spatial generalized linear mixed models
Non-Gaussian spatial data are very common in many disciplines. For instance, count data
are common in disease map**, and binary data are common in ecology. When fitting …
are common in disease map**, and binary data are common in ecology. When fitting …
A short history of Markov chain Monte Carlo: Subjective recollections from incomplete data
C Robert, G Casella - 2011 - projecteuclid.org
We attempt to trace the history and development of Markov chain Monte Carlo (MCMC) from
its early inception in the late 1940s through its use today. We see how the earlier stages of …
its early inception in the late 1940s through its use today. We see how the earlier stages of …
Markov chain Monte Carlo: Can we trust the third significant figure?
Current reporting of results based on Markov chain Monte Carlo computations could be
improved. In particular, a measure of the accuracy of the resulting estimates is rarely …
improved. In particular, a measure of the accuracy of the resulting estimates is rarely …
Unbiased Markov chain Monte Carlo methods with couplings
Summary Markov chain Monte Carlo (MCMC) methods provide consistent approximations of
integrals as the number of iterations goes to∞. MCMC estimators are generally biased after …
integrals as the number of iterations goes to∞. MCMC estimators are generally biased after …
Statistical inference for model parameters in stochastic gradient descent
Statistical inference for model parameters in stochastic gradient descent Page 1 The Annals of
Statistics 2020, Vol. 48, No. 1, 251–273 https://doi.org/10.1214/18-AOS1801 © Institute of …
Statistics 2020, Vol. 48, No. 1, 251–273 https://doi.org/10.1214/18-AOS1801 © Institute of …