In a traditional Gaussian graphical model, data homogeneity is routinely...
Single-cell RNA-sequencing technologies may provide valuable insights to...
Posterior computation in hierarchical Dirichlet process (HDP) mixture mo...
We consider the problem of clustering grouped data with possibly
non-exc...
Tensor regression methods have been widely used to predict a scalar resp...
Multivariate functional data arise in a wide range of applications. One
...
We propose an approach termed "qDAGx" for Bayesian covariate-dependent
q...
Causal discovery for quantitative data has been extensively studied but ...
Sequencing-based technologies provide an abundance of high-dimensional
b...
Health monitoring applications increasingly rely on machine learning
tec...
Reinforcement Learning (RL) has opened up new opportunities to solve a w...
We consider the problem of causal discovery (structure learning) from
he...
Causal discovery for purely observational, categorical data is a
long-st...
Microorganisms play a critical role in host health. The advancement of
h...
Access and adherence to antiretroviral therapy (ART) has transformed the...
High-throughput sequencing technology provides unprecedented opportuniti...
Although combination antiretroviral therapy (ART) is highly effective in...
We present a consensus Monte Carlo algorithm that scales existing Bayesi...
Adversarial training is a useful approach to promote the learning of
tra...
We propose a categorical matrix factorization method to infer latent dis...
We develop a scalable multi-step Monte Carlo algorithm for inference und...