Azzimonti, Laura and Corani, Giorgio and Scutari, Marco (2021) A Bayesian Hierarchical score for structure learning from related data sets. International Journal of Approximate Reasoning.
Full text not available from this repository.Abstract
Score functions for learning the structure of Bayesian networks in the literature assume that data are a homogeneous set of observations; whereas it is often the case that they comprise different related, but not homogeneous, data sets collected in different ways. In this paper we propose a new Bayesian Dirichlet score, which we call Bayesian Hierarchical Dirichlet (BHD). The proposed score is based on a hierarchical model that pools information across data sets to learn a single encompassing network structure, while taking into account the differences in their probabilistic structures. We derive a closed-form expression for BHD using a variational approximation of the marginal likelihood, we study the associated computational cost and we evaluate its performance using simulated data. We find that, when data comprise multiple related data sets, BHD outperforms the Bayesian Dirichlet equivalent uniform (BDeu) score in terms of reconstruction accuracy as measured by the Structural Hamming distance, and that it is as accurate as BDeu when data are homogeneous. This improvement is particularly clear when either the number of variables in the network or the number of observations is large. Moreover, the estimated networks are sparser and therefore more interpretable than those obtained with BDeu thanks to a lower number of false positive arcs.
| Item Type: | Scientific journal article, Newspaper article or Magazine article |
|---|---|
| Uncontrolled Keywords: | Bayesian networks, Structure learning, Hierarchical priors, Dirichlet mixtures, Network scores |
| Subjects: | Computer sciences > Artificial intelligence |
| Department/unit: | Dipartimento tecnologie innovative > Istituto Dalle Molle di studi sull'intelligenza artificiale |
| Depositing User: | Giorgio Corani |
| Date Deposited: | 10 Dec 2021 12:02 |
| Last Modified: | 10 Dec 2021 12:02 |
| URI: | http://repository.supsi.ch/id/eprint/13092 |
Actions (login required)
 |
View Item |