6 citations · 26 across the 20 of their papers we have counts for
5 papers · 1 filter
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…
Dimension-free Mixing for High-dimensional Bayesian Variable Selection
Quan Zhou, Jun Yang, Dootika Vats +2
Yang et al. (2016) proved that the symmetric random walk Metropolis--Hastings algorithm for Bayesian variable selection is rapidly mixing under mild high-dimensional assumptions. W…
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…
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…