publications

an up-to-date list is also available on Google Scholar

Preprints

2024

  1. Exact MCMC for Intractable Proposals
    Dwija Kakkad, and Dootika Vats
    2024
    arXiv Code
  2. Efficient Multivariate Initial Sequence Estimators for MCMC
    Arka Banerjee, and Dootika Vats
    2024
    arXiv Code

2023

  1. On the Utility of Equal Batch Sizes for Inference in Stochastic Gradient Descent
    Rahul Singh, Abhinek Shukla, and Dootika Vats
    2023
    arXiv Code
  2. Solving the Poisson equation using coupled Markov chains
    Randal Douc, Pierre E. Jacob, Anthony Lee, and Dootika Vats
    2023
    arXiv

2020

  1. Estimating Monte Carlo variance from multiple Markov chains
    Kushagra Gupta, and Dootika Vats
    2020
    arXiv Code

Journal articles

2024

  1. Multivariate strong invariance principles in Markov chain Monte Carlo
    Arka Banerjee, and Dootika Vats
    Electronic Journal of Statistics, 2024
    arXiv HTML

2023

  1. Optimal scaling of MCMC beyond Metropolis
    Sanket Agrawal, Dootika Vats, Krzysztof Latuszynski, and Gareth O Roberts
    Advances in Applied Probability, 2023
    arXiv HTML Code

2022

  1. A principled stopping rule for importance sampling
    Medha Agarwal, Dootika Vats , and Víctor Elvira
    Electronic Journal of Statistics, 2022
    arXiv HTML Video
  2. Dimension-free mixing for high-dimensional Bayesian variable selection
    Quan Zhou, Jun Yang, Dootika Vats, Gareth O Roberts, and Jeffrey S. Rosenthal
    Journal of the Royal Statistical Society: Series B, 2022
    arXiv HTML
  3. Globally-centered autocovariances in MCMC
    Medha Agarwal, and Dootika Vats
    Journal of Computational and Graphical Statistics, 2022
    arXiv HTML Code
  4. Lugsail lag windows for estimating time-average covariance matrices
    Dootika Vats, and James M Flegal
    Biometrika, 2022
    arXiv HTML Video Code
  5. Efficient Bernoulli factory MCMC for intractable posteriors
    Dootika Vats, Flavio B Goncalves, Krzysztof Latuszynski, and Gareth O Roberts
    Biometrika, 2022
    arXiv HTML Video Code
  6. Batch size selection for variance estimators in MCMC
    Ying Liu, Dootika Vats, and James M Flegal
    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."
    Dootika Vats, and Galin Jones
    Bayesian Analysis, 2021
    HTML
  2. Monte Carlo simulation: Are we there yet?
    Dootika Vats, James M Flegal, and Galin L Jones
    Wiley StatsRef: Statistics Reference Online, 2021
    HTML
  3. Revisiting the Gelman-Rubin diagnostic
    Dootika Vats, and Christina Knudson
    Statistical Science, 2021
    arXiv HTML Code
    170 Google Scholar citations

2020

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

2019

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

2018

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

2017

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

Book chapters

2020

  1. Monte Carlo simulation: Are we there yet?
    Dootika Vats, James M Flegal, and Galin L Jones
    2020

Technical reports

2022

  1. Understanding Linchpin Variables in Markov Chain Monte Carlo
    Dootika Vats, Felipe Acosta , Mark L. Huber, and Galin L. Jones
    2022
    arXiv

2021

  1. Bayesian equation selection on sparse data for discovery of stochastic dynamical systems
    Kushagra Gupta, Dootika Vats, and Snigdhansu Chatterjee
    2021
    arXiv Code