RESEARCH

Tree-based additive noise directed acyclic graphical models for nonlinear causal discovery with interactions

Nonparametric score-based causal discovery through Bayesian density estimation

Graph-based nonparametric multivariate density estimation

Modeling microbial community coalescence via compositional directed acyclic graphical models

Causal discovery from count-based interventional data

Multi-way overlapping clustering by Bayesian tensor decomposition

A unified Bayesian framework for biclustering multi-omic data via sparse matrix factorization

Individualized causal discovery with latent trajectory embedded Bayesian networks

Functional Bayesian networks for discovering causality from multivariate functional data

Bayesian biclustering for microbial metagenomic sequencing data via multinomial matrix factorization

Causal discovery with heterogeneous observational data