Call for Papers
Scope
Probabilistic Graphical Models (PGMs) are a smart marriage between graph theory and probability theory and provide a natural tool for dealing with uncertainty present in real-world problems. They represent a competitive tool that allows discovering useful knowledge from data, and its posterior exploitation by means of inference. Without any kind of doubt they are nowadays one of the favourite formalisms to build intelligent systems.
PGMs (mainly Bayesian and Markov networks) and their applications have received a considerably attention from researchers on different fields: computer science, statistics, applied mathematics, engineering and practitioners for fielded applications. This research effort has placed PGMs research into a high degree of maturity in many different tasks: inference, unsupervised learning, classification, modelling techniques, etc. However, more research is necessary to extend its applicability to really complex real-world application.
The aim of the session is to provide a forum to disseminate and discuss challenging contributions in all the aspects related with PGMs research, offering to researchers and practitioners an opportunity to identify new promising research directions in this area.
Topics
Topics include, but are not restricted to:
- Modelling techniques for Bayesian networks.
- Unsupervised learning of Bayesian networks and other PGMs.
- Classification and regression using PGMs.
- Data preprocessing for mining PGMs.
- Exact inference algorithms.
- Approximate inference algorithms.
- Complexity studies.
- Applications and cases of study.
Paper Submission
Please follow the instructions given at the corresponding section.
Organizers
- José A. Gámez: jose.gamez@uclm.es
Department of Computing Systems, University of Castilla-La Mancha, Spain. - José M. Puerta: jose.puerta@uclm.es
Department of Computing Systems, University of Castilla-La Mancha, Spain. - Antonio Salmerón: antonio.salmeron@ual.es
Department of Statistics and Applied Mathematics, University of Almería. Spain.