1 citations · 2 across the 6 of their papers we have counts for
6 papers
Markov Chain CLTs: Resolving Open Problems
Austin Brown, Jeffrey S. Rosenthal, Quan Zhou
Markov chain central limit theorems (CLTs) and their associated variances are very important for implementing Markov chain Monte Carlo algorithms among other applications. Häggströ…
Bayesian inference for hidden Markov models under genuine multimodality with application to ecological time series
Marco A. Gallegos-Herrada, Vianey Leos-Barajas, Jeffrey S. Rosenthal
Bayesian inference in hidden Markov models (HMMs) can be challenging due to the presence of multimodality in the likelihood function, and consequently in the joint posterior distri…
Upper and lower bounds on the subgeometric convergence of adaptive Markov chain Monte Carlo
Austin Brown, Jeffrey S. Rosenthal
We investigate lower bounds on the subgeometric convergence of adaptive Markov chain Monte Carlo under any adaptation strategy. In particular, we prove general lower bounds in tota…
Weak convergence of adaptive Markov chain Monte Carlo
Austin Brown, Jeffrey S. Rosenthal
This article develops general conditions for weak convergence of adaptive Markov chain Monte Carlo processes and is shown to imply a weak law of large numbers for bounded Lipschitz…
Estimating MCMC convergence rates using common random number simulation
Sabrina Sixta, Jeffrey S. Rosenthal, Austin Brown
This paper presents how to use common random number (CRN) simulation to evaluate Markov chain Monte Carlo (MCMC) convergence to stationarity. We provide an upper bound on the Wasse…
The Coupling/Minorization/Drift Approach to Markov Chain Convergence Rates
Yu Hang Jiang, Tong Liu, Zhiya Lou +4
This review paper provides an introduction of Markov chains and their convergence rates which is an important and interesting mathematical topic which also has important applicatio…