Thompson sampling (TS) is one of the most popular and earliest algorithm...
In a traditional Gaussian graphical model, data homogeneity is routinely...
Single-cell RNA-sequencing technologies may provide valuable insights to...
With the increasing amount of distributed energy resources (DERs)
integr...
Posterior computation in hierarchical Dirichlet process (HDP) mixture mo...
Gaussian graphical models typically assume a homogeneous structure acros...
We consider the problem of clustering grouped data with possibly
non-exc...
Despite impressive performance on a wide variety of tasks, deep neural
n...
Modern data science applications often involve complex relational data w...
We consider a latent space model for dynamic networks, where our objecti...
Survival models are used to analyze time-to-event data in a variety of
d...
Clustering of proteins is of interest in cancer cell biology. This artic...
It is well known that the integration among different data-sources is
re...
Gaussian graphical models (GGMs) are well-established tools for probabil...
Modern genomic studies are increasingly focused on identifying more and ...
Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to
...
In recent biomedical scientific problems, it is a fundamental issue to
i...
Estimating the marginal and joint densities of the long-term average int...