If you use the library, please cite us. From this work, we published the paper PGM_PyLib: A Toolkit for Probabilistic Graphical Models in Python: @InProceedings{pgm-pylib, title = {PGM{\_}PyLib: A ...

By unrolling the optimizer we use for maximum a posteriori inference in these probabilistic graphical models, our method is able to learn ... which includes training and evaluation scripts, as well as ...

to provide a Python library for compiling and querying probabilistic graphical models, specifically discrete factor graphs to be extremely efficient, flexible, and easy to use to exhibit excellent ...

All past work on high-dimensional matrix graphical models assumes that independent and identically distributed (i.i.d.) observations of the matrix-variate are available. Here we allow dependent ...

Bayesian network models are probabilistic graphical models that represent a set of variables and their conditional dependencies through directed acyclic graphs (DAGs). Bayesian networks are well ...