Probabilistic Dependency Graphs (PDGs) are a powerful class of graphical model, that can model the combination of inconsistent beliefs. PDGs generalize Bayesian Networks and Factor graphs, but are arguably more natural to use. This measure of inconsistency is itself quite useful: many information theoretically-motivated notions of loss functions and statistical distances arise naturally as the inconsistencies of the appropriate PDGs. Moreover, relationships between these PDGs give very simple intuitive proofs of otherwise fairly inscrutable results, such as variational bounds, and bounds between statistical distances.
The present repository is a python library for computing with PDGs.
For more information, see the AAAI paper, available on arXiv, and the github page corresponding to this repository.