97 citations · 234 across the 26 of their papers we have counts for
7 papers · 1 filter
Detecting Jumps on a Tree: a Hierarchical Normalized Stable Process Model for Evolution of Discrete Distributions
Hanxi Sun, Heejung Shim, Vinayak Rao
This work focuses on clustering populations whose hierarchical dependency structure can be described by a tree. A particular example that is the focus of this work is a phylogeneti…
Bayesian Repulsive Mixture Modeling with Matérn Point Processes
Hanxi Sun, Boqian Zhang, Minhyeok Kim +1
Mixture models are a standard tool in statistical analyses, widely used for density modeling and model-based clustering. In this work, we propose a Bayesian mixture model with repu…
Marginally Constrained Nonparametric Bayesian Inference through Gaussian Processes
Bingjing Tang, Vinayak Rao
Nonparametric Bayesian models are used routinely as flexible and powerful models of complex data. Many times, a statistician may have additional informative beliefs about data dist…
Data Augmentation MCMC for Bayesian Inference from Privatized Data
Nianqiao Ju, Jordan A. Awan, Ruobin Gong +1
Differentially private mechanisms protect privacy by introducing additional randomness into the data. Restricting access to only the privatized data makes it challenging to perform…
Bayesian inference for Matérn repulsive processes
Vinayak Rao, Ryan P. Adams, David B. Dunson
In many applications involving point pattern data, the Poisson process assumption is unrealistic, with the data exhibiting a more regular spread. Such a repulsion between events is…
Repulsive Mixtures
Francesca Petralia, Vinayak Rao, David B. Dunson
Discrete mixture models are routinely used for density estimation and clustering. While conducting inferences on the cluster-specific parameters, current frequentist and Bayesian m…
Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks
Vinayak Rao, Yee Whye Teh
Markov jump processes and continuous time Bayesian networks are important classes of continuous time dynamical systems. In this paper, we tackle the problem of inferring unobserved…