Abstract Bayesian Networks (BNs) have become increasingly popular over the last few
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …

In Bayesian structure learning, we are interested in inferring a distribution over the directed
acyclic graph (DAG) structure of Bayesian networks, from data. Defining such a distribution …

Learning a faithful directed acyclic graph (DAG) from samples of a joint distribution is a
challenging combinatorial problem, owing to the intractable search space superexponential …

Tackling the most pressing problems for humanity, such as the climate crisis and the threat
of global pandemics, requires accelerating the pace of scientific discovery. While science …

Promising results have driven a recent surge of interest in continuous optimization methods
for Bayesian network structure learning from observational data. However, there are …

Bayesian causal discovery aims to infer the posterior distribution over causal models from
observed data, quantifying epistemic uncertainty and benefiting downstream tasks …

Bayesian structure learning allows inferring Bayesian network structure from data while
reasoning about the epistemic uncertainty---a key element towards enabling active causal …

Bayesian networks have become a widely used method in the modelling of uncertain
knowledge. Owing to the difficulty domain experts have in specifying them, techniques that …

The fundamental challenge in causal induction is to infer the underlying graph structure
given observational and/or interventional data. Most existing causal induction algorithms …

Applications of Bayesian networks in systems biology are computationally demanding due
to the large number of model parameters. Conventional MCMC schemes based on proposal …