2 citations · 2 across the 3 of their papers we have counts for
8 papers
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…
Variational Rejection Particle Filtering
Rahul Sharma, Soumya Banerjee, Dootika Vats +1
We present a variational inference (VI) framework that unifies and leverages sequential Monte-Carlo (particle filtering) with \emph{approximate} rejection sampling to construct a f…
Bayesian equation selection on sparse data for discovery of stochastic dynamical systems
Kushagra Gupta, Dootika Vats, Snigdhansu Chatterjee
Often the underlying system of differential equations driving a stochastic dynamical system is assumed to be known, with inference conditioned on this assumption. We present a Baye…
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…