Probabilistic Dependency Graphs
From MaRDI portal
Publication:6356527
arXiv2012.10800MaRDI QIDQ6356527
Author name not available (Why is that?)
Publication date: 19 December 2020
Abstract: We introduce Probabilistic Dependency Graphs (PDGs), a new class of directed graphical models. PDGs can capture inconsistent beliefs in a natural way and are more modular than Bayesian Networks (BNs), in that they make it easier to incorporate new information and restructure the representation. We show by example how PDGs are an especially natural modeling tool. We provide three semantics for PDGs, each of which can be derived from a scoring function (on joint distributions over the variables in the network) that can be viewed as representing a distribution's incompatibility with the PDG. For the PDG corresponding to a BN, this function is uniquely minimized by the distribution the BN represents, showing that PDG semantics extend BN semantics. We show further that factor graphs and their exponential families can also be faithfully represented as PDGs, while there are significant barriers to modeling a PDG with a factor graph.
Has companion code repository: https://github.com/orichardson/pdg
This page was built for publication: Probabilistic Dependency Graphs
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6356527)
