The conference offers a forum to present latest developments in probabilistic graphical models (PGMs). Both theoretical and applied contributions related to the following topics are welcome:
Bayesian (belief) networks, Markov random fields, chain graphs, decision networks, influence diagrams and other PGMs.
Theoretical foundations of PGMs (conditional independence, causal models, hidden variables).
Information processing in PGMs (exact and approximate inference, sensitivity analysis).
Learning and data mining with PGMs.
Statistical methodology (multivariate modeling, Bayesian model selection, MCMC simulations).
Links between PGMs and other disciplines like information theory, optimization, imprecise probabilities and decision making under uncertainty.
Computational aspects in learning and inference.
Applications of PGMs to real-world problems (e.g. genomics).
Accepted papers will be published electronically in a volume of the Proceedings of Machine Learning Research. Each submission will be reviewed. All accepted papers will be presented at the conference. At least one of the authors of an accepted paper should register and attend the conference to present the work.
Papers must be submitted electronically through EasyChair (to be opened in January 2018).
Download the author kit here. Papers should not be longer than 12 pages, references included. The papers will go through a standard reviewing process.
After the conference, a virtual special issue with a selection of extended versions of papers presented at the conference will be published in International Journal of Approximate Reasoning.