Preprint

  1. On the Utility of Equal Batch Sizes for Inference in Stochastic Gradient Descent Singh, Rahul, Shukla, Abhinek, and Vats, Dootika Preprint [arXiv]
  2. Multivariate strong invariance principles in Markov chain Monte Carlo Banerjee, Arka, and Vats, Dootika Preprint [arXiv]
  3. Solving the Poisson equation using coupled Markov chains Douc, Randal, Jacob, Pierre E., Lee, Anthony, and Vats, Dootika Preprint [arXiv]
  4. Estimating Monte Carlo variance from multiple Markov chains Gupta, Kushagra, and Vats, Dootika Preprint [arXiv] [Code]
  5. Understanding Linchpin Variables in Markov Chain Monte Carlo Vats, Dootika, Acosta, Felipe, Huber, Mark L., and Jones, Galin L. Preprint [arXiv]

2022

  1. A principled stopping rule for importance sampling Agarwal, Medha, Vats, Dootika, and Elvira, Víctor Electronic Journal of Statisticsto appear 2022 [arXiv] [HTML] [Video]
  2. Dimension-free mixing for high-dimensional Bayesian variable selection Zhou, Quan, Yang, Jun, Vats, Dootika, Roberts, Gareth O, and Rosenthal, Jeffrey S. Journal of the Royal Statistical Society: Series Bto appear 2022 [arXiv] [HTML]
  3. Optimal scaling of MCMC beyond Metropolis Agrawal, Sanket, Vats, Dootika, Łatuszyński, Krzysztof, and Roberts, Gareth O Advances in Applied Probability to appear 2022 [arXiv] [HTML] [Code]
  4. Globally-centered autocovariances in MCMC Agarwal, Medha, and Vats, Dootika Journal of Computational and Graphical Statistics 2022 [arXiv] [HTML] [Code]
  5. Lugsail lag windows for estimating time-average covariance matrices Vats, Dootika, and Flegal, James M Biometrika 2022 [arXiv] [HTML] [Video] [Code]
  6. Efficient Bernoulli factory MCMC for intractable posteriors Vats, Dootika, Gonçalves, Flávio B, Łatuszyński, Krzysztof, and Roberts, Gareth O Biometrika 2022 [arXiv] [HTML] [Video] [Code]
  7. Batch size selection for variance estimators in MCMC Liu, Ying, Vats, Dootika, and Flegal, James M Methodology and Computing in Applied Probability 2022 [arXiv] [HTML]

2021

  1. Invited Discussion: "Rank-Normalization, Folding, and Localization: An Improved R-hat for Assessing Convergence of MCMC by Vehtari et. al." Vats, Dootika, and Jones, Galin Bayesian Analysis 2021 [HTML]
  2. Monte Carlo simulation: Are we there yet? Vats, Dootika, Flegal, James M, and Jones, Galin L Wiley StatsRef: Statistics Reference Online 2021 [HTML]
  3. Revisiting the Gelman-Rubin diagnostic Vats, Dootika, and Knudson, Christina Statistical Science 2021 [arXiv] [HTML] [Code]

2020

  1. Assessing and Visualizing Simultaneous Simulation Error Robertson, Nathan, Flegal, James M, Vats, Dootika, and Jones, Galin L Journal of Computational and Graphical Statistics 2020 [arXiv] [HTML]
  2. Analyzing Markov chain Monte Carlo output Vats, Dootika, Robertson, Nathan, Flegal, James M, and Jones, Galin L WIREs Computational Statistics 2020 [arXiv] [HTML]
  3. Comment: "Unbiased Markov chain Monte Carlo with couplings" by Jacob et. al. Vats, Dootika, and Jones, Galin L Journal of the Royal Statistical Society, Series B 2020 [HTML]

2019

  1. Multivariate output analysis for Markov chain Monte Carlo Vats, Dootika, Flegal, James M, and Jones, Galin L Biometrika 2019 [arXiv] [HTML]

2018

  1. Strong Consistency of Multivariate Spectral Variance Estimators in Markov chain Monte Carlo Vats, Dootika, Flegal, James M, and Jones, Galin L Bernoulli 2018 [arXiv] [HTML]

2017

  1. Geometric ergodicity of Gibbs samplers in Bayesian penalized regression models Vats, Dootika Electronic Journal of Statistics 2017 [arXiv] [HTML]

Book Chapter

  1. Monte Carlo simulation: Are we there yet? Vats, Dootika, Flegal, James M, and Jones, Galin L Handbook of Computational Statistics and Data Science Book Chapter

Tech report

  1. Bayesian equation selection on sparse data for discovery of stochastic dynamical systems Gupta, Kushagra, Vats, Dootika, and Chatterjee, Snigdhansu Tech report [arXiv] [Code]