Introduction to Bayesian Belief Networks and their Applications

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Speaker: Lionel Jouffie, Bayesia Labs
Event Details
Bayesian Belief networks have emerged as a new form of probabilistic knowledge representation and probabilistic inference engine through the seminal works of UCLA Professor Judea Pearl. Over the last 25 years the properties of Bayesian networks have been fully validated in the world of academia and they are now becoming powerful and practical tools for “deep understanding” of very complex, high-dimensional problem domains. Their computational efficiency and inherently visual structure make Bayesian Belief networks very attractive for Expert Knowledge Modeling, Data mining, and Causal Analysis.This tutorial will provide an introduction to the wide-ranging applications of Bayesian Belief networks. Participants do not need to have any prior familiarity with Bayesian Belief networks. We will start the seminar by illustrating the conceptual foundations using several textbook examples. This will include an overview of unsupervised learning (knowledge discovery), supervised learning (dependent variable characterization), data clustering (segmentation), variable clustering (to find hidden concepts), and Probabilistic Structural Equation Models (mainly applied for drivers analysis).Bayesia will provide all participants with an unrestricted 30-day license of BayesiaLab 5.0 Professional Edition, so they can participate in exercises on their own laptops.

Speaker Bio
Dr. Lionel Jouffe, cofounder and CEO of France-based Bayesia S.A.S.Lionel Jouffe received the Ph.D. degree in Computer Science from the Université of Rennes I, Rennes, France, in 1997. After one year dedicated to the industrialization of the results of his Ph.D. research (Fuzzy Inference System learning by Reinforcement methods – automatic pig house atmosphere controller), he received the Inov’Space Award and the medal of the town of Rennes.He joined the ESIEA as a Professor/Researcher in 1998 and began his research on Bayesian network learning from data. Lionel then co-founded Bayesia in 2001, a company specialized in Bayesian networks technology. He and his team have been developing BayesiaLab since 1999 and it has emerged as the leading software package for knowledge discovery, data mining and knowledge modeling using Bayesian networks. BayesiaLab enjoys broad acceptance in academic communities as well as in business and industry. The relevance of Bayesian networks, especially in the context of consumer research, is highlighted by Bayesia’s strategic partnership with Procter & Gamble, who has deployed BayesiaLab globally since 2007