147 citations · 460 across the 31 of their papers we have counts for
8 papers · 1 filter
Dimension adaptability of Gaussian process models with variable selection and projection
Surya T. Tokdar
It is now known that an extended Gaussian process model equipped with rescaling can adapt to different smoothness levels of a function valued parameter in many nonparametric Bayesi…
Adaptive Convergence Rates of a Dirichlet Process Mixture of Multivariate Normals
Surya T. Tokdar
It is shown that a simple Dirichlet process mixture of multivariate normals offers Bayesian density estimation with adaptive posterior convergence rates. Toward this, a novel sieve…
Adaptive Bayesian multivariate density estimation with Dirichlet mixtures
Weining Shen, Surya T. Tokdar, Subhashis Ghosal
We show that rate-adaptive multivariate density estimation can be performed using Bayesian methods based on Dirichlet mixtures of normal kernels with a prior distribution on the ke…
Bayesian test of normality versus a Dirichlet process mixture alternative
Surya T. Tokdar, Ryan Martin
We propose a Bayesian test of normality for univariate or multivariate data against alternative nonparametric models characterized by Dirichlet process mixture distributions. The a…
Adaptive Gaussian Predictive Process Approximation
Surya T Tokdar
We address the issue of knots selection for Gaussian predictive process methodology. Predictive process approximation provides an effective solution to the cubic order computationa…
Efficient Gaussian Process Regression for Large Data Sets
Anjishnu Banerjee, David Dunson, Surya Tokdar
Gaussian processes (GPs) are widely used in nonparametric regression, classification and spatio-temporal modeling, motivated in part by a rich literature on theoretical properties.…