198 citations · 888 across the 47 of their papers we have counts for
6 papers · 1 filter
Bayesian two-step estimation in differential equation models
Prithwish Bhaumik, Subhashis Ghosal
Ordinary differential equations (ODEs) are used to model dynamic systems appearing in engineering, physics, biomedical sciences and many other fields. These equations contain unkno…
Supremum Norm Posterior Contraction and Credible Sets for Nonparametric Multivariate Regression
William Weimin Yoo, Subhashis Ghosal
In the setting of nonparametric multivariate regression with unknown error variance, we study asymptotic properties of a Bayesian method for estimating a regression function f and…
Efficient Bayesian estimation and uncertainty quantification in ordinary differential equation models
Prithwish Bhaumik, Subhashis Ghosal
Often the regression function is specified by a system of ordinary differential equations (ODEs) involving some unknown parameters. Typically analytical solution of the ODEs is not…
Bayesian estimation in differential equation models
Prithwish Bhaumik, Subhashis Ghosal
Ordinary differential equations (ODEs) are used to model dynamic systems appearing in engineering, physics, biomedical sciences and many other fields. These equations contain unkno…
Adaptive Bayesian density regression for high-dimensional data
Weining Shen, Subhashis Ghosal
Density regression provides a flexible strategy for modeling the distribution of a response variable given predictors by letting that the conditio…
Adaptive Bayesian procedures using random series priors
Weining Shen, Subhashis Ghosal
We consider a prior for nonparametric Bayesian estimation which uses finite random series with a random number of terms. The prior is constructed through distributions on the numbe…