Tracking the spread of infectious disease during a pandemic has posed a ...
In this paper, we propose a policy gradient method for confounded partia...
Multivariate functional data arise in a wide range of applications. One
...
We propose a Bayesian tensor-on-tensor regression approach to predict a
...
We study the offline reinforcement learning (RL) in the face of unmeasur...
We establish the minimax risk for parameter estimation in sparse
high-di...
This paper presents a computational framework that generates ensemble
pr...
Accurate models of clinical actions and their impacts on disease progres...
Access and adherence to antiretroviral therapy (ART) has transformed the...
Although combination antiretroviral therapy (ART) is highly effective in...
We propose a one-step procedure to efficiently estimate the latent posit...
We propose a Bayesian approach, called the posterior spectral embedding,...
We develop a Bayesian nonparametric (BNP) approach to evaluate the effec...
Preventing periodontal diseases (PD) and maintaining the structure and
f...
Developing targeted therapies based on patients' baseline characteristic...
We propose a Bayesian methodology for estimating spiked covariance matri...
We develop a Bayesian approach called Bayesian projected calibration to
...
We develop a unifying framework for Bayesian nonparametric regression to...
Gaussian stochastic process (GaSP) has been widely used as a prior over
...
We propose a kernel mixture of polynomials prior for Bayesian nonparamet...
We study the problem of estimating the continuous response over time to
...