Generalized Ideal Parent (GIP): Discovering non-Gaussian Hidden Variables


Yaniv Tenzer, Gal Elidan ;
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, PMLR 51:222-230, 2016.


A formidable challenge in uncertainty modeling in general, and when learning Bayesian networks in particular, is the discovery of unknown hidden variables. Few works that tackle this task are typically limited to discrete or Gaussian domains, or to tree structures. We propose a novel general purpose approach for discovering hidden variables in flexible non-Gaussian domains using the powerful class of Gaussian copula networks. Briefly, we define the concept of a hypothetically optimal predictor of variable and show it can be used to discover useful hidden variables in the expressive framework of copula networks. Our approach leads to performance and compactness advantages over competitors in a variety of domains.

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