6 citations · 26 across the 20 of their papers we have counts for
19 papers · 1 filter
SULA: Robust Langevin Sampling via Drift-Diffusion Saturation
Ritik Soni, Dootika Vats
The unadjusted Langevin algorithm (ULA) and its stochastic-gradient variant, SGLD, are workhorses of modern Bayesian machine learning, but can be fragile when faced with a target d…
Optimal Scaling of Langevin Proposals with Generalized Acceptance Rules
Ritik Soni, Dootika Vats
Langevin-based Markov chain Monte Carlo (MCMC) algorithms use gradient information to improve sampling, particularly in high dimensions. Classical optimal scaling theory for these…
Hamiltonian Monte Carlo for (Physics) Dummies
Arghya Mukherjee, Dootika Vats
Sampling-based inference has seen a surge of interest in recent years. Hamiltonian Monte Carlo (HMC) has emerged as a powerful algorithm that leverages concepts from Hamiltonian dy…
Proximal Hamiltonian Monte Carlo
Apratim Shukla, Dootika Vats, Eric C. Chi
Bayesian formulation of modern day signal processing problems has called for improved Markov chain Monte Carlo (MCMC) sampling algorithms for inference. The need for efficient samp…
MCMC Importance Sampling via Moreau-Yosida Envelopes
Apratim Shukla, Dootika Vats, Eric C. Chi
Non-differentiable priors are standard in modern parsimonious Bayesian models. Lack of differentiability, however, precludes gradient-based Markov chain Monte Carlo (MCMC) for post…
Exact MCMC for Intractable Proposals
Dwija Kakkad, Dootika Vats
Accept-reject based Markov chain Monte Carlo (MCMC) methods are the workhorse algorithm for Bayesian inference. These algorithms, like Metropolis-Hastings, require choosing a propo…
Efficient Multivariate Initial Sequence Estimators for MCMC
Arka Banerjee, Dootika Vats
Estimating Monte Carlo error is critical to valid simulation results in Markov chain Monte Carlo (MCMC) and initial sequence estimators were one of the first methods introduced for…
On the Utility of Equal Batch Sizes for Inference in Stochastic Gradient Descent
Rahul Singh, Abhinek Shukla, Dootika Vats
Stochastic gradient descent (SGD) is an estimation tool for large data employed in machine learning and statistics. Due to the Markovian nature of the SGD process, inference is a c…
Multivariate strong invariance principles in Markov chain Monte Carlo
Arka Banerjee, Dootika Vats
Strong invariance principles in Markov chain Monte Carlo are crucial to theoretically grounded output analysis. Using the wide-sense regenerative nature of the process, we obtain e…
Understanding Linchpin Variables in Markov Chain Monte Carlo
Dootika Vats, Felipe Acosta, Mark L. Huber +1
An introduction to the use of linchpin variables in Markov chain Monte Carlo (MCMC) is provided. Before the widespread adoption of MCMC methods, conditional sampling using linchpin…
Solving the Poisson equation using coupled Markov chains
Randal Douc, Pierre E. Jacob, Anthony Lee +1
This article shows how coupled Markov chains that meet exactly after a random number of iterations can be used to generate unbiased estimators of the solutions of the Poisson equat…
A principled stopping rule for importance sampling
Medha Agarwal, Dootika Vats, Víctor Elvira
Importance sampling (IS) is a Monte Carlo technique that relies on weighted samples, simulated from a proposal distribution, to estimate intractable integrals. The quality of the e…
Optimal Scaling of MCMC Beyond Metropolis
Sanket Agrawal, Dootika Vats, Krzysztof Łatuszyński +1
The problem of optimally scaling the proposal distribution in a Markov chain Monte Carlo algorithm is critical to the quality of the generated samples. Much work has gone into obta…
Globally-centered autocovariances in MCMC
Medha Agarwal, Dootika Vats
Autocovariances are a fundamental quantity of interest in Markov chain Monte Carlo (MCMC) simulations with autocorrelation function (ACF) plots being an integral visualization tool…
Efficient Bernoulli factory MCMC for intractable posteriors
Dootika Vats, Flávio Gonçalves, Krzysztof Łatuszyński +1
Accept-reject based Markov chain Monte Carlo (MCMC) algorithms have traditionally utilised acceptance probabilities that can be explicitly written as a function of the ratio of the…
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…
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…
Revisiting the Gelman-Rubin Diagnostic
Dootika Vats, Christina Knudson
Gelman and Rubin's (1992) convergence diagnostic is one of the most popular methods for terminating a Markov chain Monte Carlo (MCMC) sampler. Since the seminal paper, researchers…
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…