We propose a novel global-local framework that partitions coefficients into exclusive subsets with independent Dirichlet process (DP) priors on each subset to improve the scalability, while simultaneously providing enhanced flexibility compared to global clustering in the high-dimensional function.

We conduct the Bayesian inference that makes efficient local move on the space of ultrametric matrices by leveraging the bijection map between the ultrametric matrices and the tree space.

In this paper, we propose an approach termed robust Bayesian graphical regression (rBGR) to estimate graphs that vary on subject-specific information for non-normally distributed data.

In this paper, we propose a novel Bayesian probabilistic tree-based framework, which motivates a new metric of mechanistic similarity between two or more treatments.