Bayesian Modeling and Causal Inference

BNMCMC is a software tool for inferring and visualizing Bayesian networks using MCMC methods, improving upon standard samplers by avoiding local optima.

This study uses fMRI to investigate neural correlates of structure learning and associative learning, showing distinct brain regions and predictive behavioral outcomes.

This paper introduces Suppes-Bayes Causal Networks to model the accumulation of DNA changes in diseases like cancer and HIV, showing their ability to infer causal relationships from genetic data.

The paper applies causal discovery methods to mass cytometry data of human immune cells to predict causal relationships between signaling proteins and their responses to perturbations.

This study compares generalised independence criteria for network inference, finding that distance covariance performs well on biological datasets.

The paper introduces a mixture copula Bayesian network model for causal inference in non-Gaussian genomic data, showing improved performance on cancer genome atlas data.