Etiological Profile of Obstructive Jaundice and Acute Cholangitis: Three-year Data from a Tertiary Care Center in Eastern India.

Background And Objective: Obstructive jaundice (OJ) and acute cholangitis (AC) are common presentations of biliary obstruction. In Eastern India, data regarding the causes of OJ and AC are scarce. This study aimed to determine the etiological spectrum of OJ and AC in a tertiary center in Eastern India.

Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data.

We consider the problem of clustering grouped data with possibly non-exchangeable groups whose dependencies can be characterized by a known directed acyclic graph. To allow the sharing of clusters among the non-exchangeable groups, we propose a Bayesian nonparametric approach, termed graphical Dirichlet process, that jointly models the dependent group-specific random measures by assuming each random measure to be distributed as a Dirichlet process whose concentration parameter and base probability measure depend on those of its parent groups. The resulting joint stochastic process respects the Markov property of the directed acyclic graph that links the groups.

Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data.

In a traditional Gaussian graphical model, data homogeneity is routinely assumed with no extra variables affecting the conditional independence. In modern genomic datasets, there is an abundance of auxiliary information, which often gets under-utilized in determining the joint dependency structure. In this article, we consider a Bayesian approach to model undirected graphs underlying heterogeneous multivariate observations with additional assistance from covariates.