Set-Based Variational Methods in Credal Networks: the SV2U Algorithm

Jaime Shinsuke IdeFábio Gagliardi Cozman

Graphical models that represent uncertainty through sets of probability measures are often referred to as credal networks. Polynomial-time exact inference methods are available only for polytree-structured binary credal networks. In this work, we approximate potentially intractable inferences in multiconnected binary networks by tractable inferences in polytree-structures. We propose a novel set-based structural variational inference method - the SV2U algorithm. The SV2U algorithm is the first method that produces approximate inferences in large binary credal networks with theoretical solid convergence analysis and offers a promising way to handle continuous variables in credal networks.

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