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Compression of general Bayesian net CPTs. (English) Zbl 1355.68266

Khoury, Richard (ed.) et al., Advances in artificial intelligence. 29th Canadian conference on artificial intelligence, Canadian AI 2016, Victoria, BC, Canada, May 31 – June 3, 2016. Proceedings. Cham: Springer (ISBN 978-3-319-34110-1/pbk; 978-3-319-34111-8/ebook). Lecture Notes in Computer Science 9673. Lecture Notes in Artificial Intelligence, 285-297 (2016).
Summary: Non-Impeding Noisy-AND (NIN-AND) Tree (NAT) models offer a highly expressive approximate representation for significantly reducing the space of Bayesian Nets (BNs). They can also significantly improve efficiency of BN inference, as shown for binary NAT models. To enable these advantages for general BNs, advancements on three technical challenges are made in this work. We overcome the limitation of well-defined Pairwise Causal Interaction (PCI) bits and present a flexible PCI pattern extraction from general target Conditional Probability Tables (CPTs). We extend parameter estimation for binary NAT models to constrained gradient descent for compressing target CPTs into multi-valued NAT models. The effectiveness of the compression is demonstrated experimentally. A novel framework is also developed for PCI pattern extraction when persistent leaky causes exist.
For the entire collection see [Zbl 1337.68012].

MSC:

68T37 Reasoning under uncertainty in the context of artificial intelligence
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