6 papers
Offline Estimation of Controlled Markov Chains: Minimaxity and Sample Complexity
Imon Banerjee, Harsha Honnappa, Vinayak Rao
In this work, we study a natural nonparametric estimator of the transition probability matrices of a finite controlled Markov chain. We consider an offline setting with a fixed dat…
Neural Diffusion Intensity Models for Point Process Data
Xinlong Du, Harsha Honnappa, Vinayak Rao
Cox processes model overdispersed point process data via a latent stochastic intensity, but both nonparametric estimation of the intensity model and posterior inference over intens…
Exact Gibbs sampling for stochastic differential equations with gradient drift and constant diffusion
Xinyi Pei, Minhyeok Kim, Vinayak Rao
Stochastic differential equations (SDEs) are an important class of time-series models, used to describe stochastic systems evolving in continuous time. Simulating paths from these…
Adaptive Estimation of the Transition Density of Controlled Markov Chains
Imon Banerjee, Vinayak Rao, Harsha Honnappa
Estimating the transition dynamics of controlled Markov chains is crucial in fields such as time series analysis, reinforcement learning, and system exploration. Traditional non-pa…
dapper: Data Augmentation for Private Posterior Estimation in R
Kevin Eng, Jordan A. Awan, Nianqiao Phyllis Ju +2
This paper serves as a reference and introduction to using the R package dapper. dapper encodes a sampling framework which allows exact Markov chain Monte Carlo simulation of param…
MCMC for Bayesian nonparametric mixture modeling under differential privacy
Mario Beraha, Stefano Favaro, Vinayak Rao
Estimating the probability density of a population while preserving the privacy of individuals in that population is an important and challenging problem that has received consider…