Efficient Sampling and Structure Learning of Bayesian Networks

Kuipers, Jack and Suter, Polina and Moffa, Giusi. (2021) Efficient Sampling and Structure Learning of Bayesian Networks.

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Official URL: https://edoc.unibas.ch/84871/

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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. Efforts have focussed on two fronts: constraint-based methods that perform conditional independence tests to exclude edges and score and search approaches which explore the DAG space with greedy or MCMC schemes. Here we synthesise these two fields in a novel hybrid method which reduces the complexity of MCMC approaches to that of a constraint-based method. Individual steps in the MCMC scheme only require simple table lookups so that very long chains can be efficiently obtained. Furthermore, the scheme includes an iterative procedure to correct for errors from the conditional independence tests. The algorithm offers markedly superior performance to alternatives, particularly because DAGs can also be sampled from the posterior distribution, enabling full Bayesian model averaging for much larger Bayesian networks.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Mathematik > Statistik (Moffa)
UniBasel Contributors:Moffa, Giusi
Item Type:Working Paper
Number of Pages:40
Note:Publication type according to Uni Basel Research Database: Discussion paper / Internet publication
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Last Modified:01 Nov 2021 10:38
Deposited On:01 Nov 2021 10:38

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