Bayesian Modeling and Causal Inference

BNMCMC: a software for learning and visualizing Bayesian networks using MCMC methods

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

Neural Computations Underlying Causal Structure Learning

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

Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks

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.

Predicting Causal Relationships from Biological Data: Applying Automated Casual Discovery on Mass Cytometry Data of Human Immune Cells

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.

Exploiting General Independence Criteria For Network Inference

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

A Mixture Copula Bayesian Network Model for Multimodal Genomic Data

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.