A0461
Title: Efficient sampling for Bayesian networks
Authors: Jack Kuipers - ETH Zurich (Switzerland) [presenting]
Abstract: Bayesian networks are probabilistic graphical models widely employed to understand dependencies in high-dimensional data, and even to facilitate causal discovery. Learning the underlying network structure, which is encoded as a directed acyclic graph (DAG), is highly challenging, mainly due to the vast number of possible networks in combination with the acyclicity constraint, and a wide plethora of algorithms have been developed for this task. Efforts have focused on two fronts: Constraint-based methods that perform conditional independence tests to exclude edges and score, and search approaches that explore the DAG space with greedy or MCMC schemes. These two fields are synthesized in a novel hybrid method, which reduces the complexity of Bayesian MCMC approaches to that of a constraint-based method. This enables full Bayesian model averaging for much larger Bayesian networks and offers significant improvements in structure learning. The application of Bayesian network learning to patient stratification in myeloid malignancies is further discussed.
