activity
20072026
most citedMarkov Chain Monte Carlo: Can We Trust the Third Significant Figure?

275 citations · 287 across the 3 of their papers we have counts for

collaborators

10 papers

math.ST2026

Inference Optimal Long Run Variance Estimation with Lugsail Kernels

Rebecca P. Kurtz-Garcia, James M. Flegal

For datasets with unknown but stationary serial dependence, a robust long run variance estimator is essential to handle diverse scenarios. Spectral variance estimators are commonly…

stat.CO2024

Implementing MCMC: Multivariate estimation with confidence

James M. Flegal, Rebecca P. Kurtz-Garcia

This paper addresses the key challenge of estimating the asymptotic covariance associated with the Markov chain central limit theorem, which is essential for visualizing and termin…

stat.CO2019

Analyzing MCMC Output

Dootika Vats, Nathan Robertson, James M Flegal +1

Markov chain Monte Carlo (MCMC) is a sampling-based method for estimating features of probability distributions. MCMC methods produce a serially correlated, yet representative, sam…

stat.CO2019

Assessing and Visualizing Simultaneous Simulation Error

Nathan Robertson, James M. Flegal, Dootika Vats +1

Monte Carlo experiments produce samples in order to estimate features of a given distribution. However, simultaneous estimation of means and quantiles has received little attention…

stat.CO2018

Lugsail lag windows for estimating time-average covariance matrices

Dootika Vats, James M. Flegal

Lag windows are commonly used in time series, econometrics, steady-state simulation, and Markov chain Monte Carlo to estimate time-average covariance matrices. In the presence of p…

math.ST2018

Weighted batch means estimators in Markov chain Monte Carlo

Ying Liu, James M. Flegal

This paper proposes a family of weighted batch means variance estimators, which are computationally efficient and can be conveniently applied in practice. The focus is on Markov ch…