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Approximate inference of marginals using the IBIA framework
Shivani Bathla, Vinita Vasudevan
Exact inference of marginals in probabilistic graphical models (PGM) is known to be intractable, necessitating the use of approximate methods. Most of the existing variational tech…
IBIA: An Incremental Build-Infer-Approximate Framework for Approximate Inference of Partition Function
Shivani Bathla, Vinita Vasudevan
Exact computation of the partition function is known to be intractable, necessitating approximate inference techniques. Existing methods for approximate inference are slow to conve…
MPE inference using an Incremental Build-Infer-Approximate Paradigm
Shivani Bathla, Vinita Vasudevan
Exact inference of the most probable explanation (MPE) in Bayesian networks is known to be NP-complete. In this paper, we propose an algorithm for approximate MPE inference that is…
IBIA: Bayesian Inference via Incremental Build-Infer-Approximate operations on Clique Trees
Shivani Bathla, Vinita Vasudevan
Exact inference in Bayesian networks is intractable and has an exponential dependence on the size of the largest clique in the corresponding clique tree (CT), necessitating approxi…