pyAgrum is a scientific C++ and Python library dedicated to Bayesian networks (BN) and other Probabilistic Graphical Models. Based on the C++ aGrUM library, it provides a high-level interface to the C++ part of aGrUM allowing to create, manage and perform efficient computations with Bayesian networks and others probabilistic graphical models : Markov networks (MN), influence diagrams (ID) and LIMIDs, credal networks (CN), dynamic BN (dBN), probabilistic relational models (PRM).
The aGrUM/pyAgrum user day will take place on Friday 18 March 2022.
Agenda
Morning
10h00-11h00 : Pierre-Henri Wuillemin, pyAgrum: Introduction, introspection and illustration
11h10-11h40 : Jeremy Chichportich, Optimal Quantized Belief Propagation
11h40-12h10 : Clara Charon, Classification with pyAgrum for prediction in nursing homes
12h10-12h40 : Mahdi Hadj Ali, A Quantitative Explanation for pyAgrum Classifier: Shapley values
Afternoon
14h00-15h00 : Christophe Gonzales , Fast & Furious
15h10-15h40 : Marvin Lasserre, Coupling aGrUM/pyAgrum with external libraries : an application to continuous non-parametric Bayesian Networks
15h40-16h10 : Mélanie Munch, A process reverse engineering approach using Expert Knowledge and Probabilistic Relational Models
16h10-16h40 : Santiago Cortijo, Simpson's Paradox analyzed through Causal Reasoning
16h40-17h10 : Ketemwabi Yves Shamavu, Beyond Black-box Models in Sensitive Environments
17h10-18h00 : Questions and Answers