Efficient Sampling and Structure Learning of Bayesian Networks
By Jack Kuipers, Polina Suter, Giusi Moffa. 2018
86 citations recorded by the source corpus.
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. Efforts have focused 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 synthesize 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 it also offers the possibility to sample DAGs from their posterior distribution, enabling full Bayesian model averaging for much larger Bayesian networks. Supplementary materials for this article are available online.
Subfields: Artificial Intelligence, Molecular Biology, Statistics and Probability, Probabilistic & Bayesian Methods
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In Artificial Intelligence
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In Molecular Biology
- Simon Lacoste-Julien
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In Statistics and Probability
- Simon Lacoste-Julien
- Stefan Bauer
- Yoshua Bengio
- Mikko Koivisto
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In Probabilistic & Bayesian Methods
- Yoshua Bengio
- Tristan Deleu
- Stefan Bauer
- Simon Lacoste-Julien
- Chris C. Emezue
- Ant'onio G'ois
- Mikko Koivisto
- Antti Hyttinen
Citations in the corpus
- Bayesian Structure Learning with Generative Flow Networks (2022, methodology): “This was further refined by either modifying the underlying space of the Markov chain (Kuipers and Moffa, 2017; Niinimäki et al., 2016), or its local moves (Mansinghka et al., 2006; Eaton and Murphy, 2007a; Kuipers et al., 2021).”
- Towards Scalable Bayesian Learning of Causal DAGs (2020, methodology): “Table 1: Space and time requirements with n nodes and K candidate parents per node Task Space Time Previous work [19]”