97 citations · 234 across the 26 of their papers we have counts for
7 papers · 1 filter
An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions
Qi Wang, Vinayak Rao, Yee Whye Teh
Stochastic differential equations (SDEs) or diffusions are continuous-valued continuous-time stochastic processes widely used in the applied and mathematical sciences. Simulating p…
Flexible Mixture Modeling on Constrained Spaces
Putu Ayu Sudyanti, Vinayak Rao
This paper addresses challenges in flexibly modeling multimodal data that lie on constrained spaces. Such data are commonly found in spatial applications, such as climatology and c…
Group-Representative Functional Network Estimation from Multi-Subject fMRI Data via MRF-based Image Segmentation
Aditi Iyer, Bingjing Tang, Vinayak Rao +1
We propose a novel two-phase approach to functional network estimation of multi-subject functional Magnetic Resonance Imaging (fMRI) data, which applies model-based image segmentat…
Efficient parameter sampling for Markov jump processes
Boqian Zhang, Vinayak Rao
Markov jump processes (MJPs) are continuous-time stochastic processes widely used in a variety of applied disciplines. Inference for MJPs typically proceeds via Markov chain Monte…
Data augmentation for models based on rejection sampling
Vinayak Rao, Lizhen Lin, David Dunson
We present a data augmentation scheme to perform Markov chain Monte Carlo inference for models where data generation involves a rejection sampling algorithm. Our idea, which seems…
Bayesian nonparametric inference on the Stiefel manifold
Lizhen Lin, Vinayak Rao, David B. Dunson
The Stiefel manifold is the space of all orthonormal matrices, with the hypersphere and the space of all orthogonal matrices constituting special cases…
Fast MCMC sampling for Markov jump processes and extensions
Vinayak Rao, Yee Whye Teh
Markov jump processes (or continuous-time Markov chains) are a simple and important class of continuous-time dynamical systems. In this paper, we tackle the problem of simulating f…