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cs.AI2023
Approximate Implication for Probabilistic Graphical Models
Batya Kenig
The graphical structure of Probabilistic Graphical Models (PGMs) represents the conditional independence (CI) relations that hold in the modeled distribution. Every separator in th…
cs.AI2021
Approximate Implication with d-Separation
Batya Kenig
The graphical structure of Probabilistic Graphical Models (PGMs) encodes the conditional independence (CI) relations that hold in the modeled distribution. Graph algorithms, such a…