🎓Advanced BayesiaLab Course
Three-Day Advanced BayesiaLab Course
New course dates to be announced.
Building on the foundation laid in the Introductory BayesiaLab Course, we introduce the Advanced BayesiaLab Course for those ready to delve even deeper.
With this immersive experience, you can take your BayesiaLab certification to the next level. While the introductory course provided a comprehensive overview of Bayesian network applications, our advanced curriculum dives into the nuances.
Course Program
Modeling by Brainstorming
Expert-Based Modeling via Brainstorming
Why Expert-Based Modeling?
Value of Expert-Based Modeling
Structural Modeling: Bottom-Up and Top-Down Approaches
Parametric Modeling
BEKEE: Bayesia Expert Knowledge Elicitation Environment
Interactive
Batch
Segmentation of the Experts
Creation of Bayesian Belief Networks based on the Elicited Probabilities
Analysis of the Expert Assessments
Parameter Sensitivity Analysis
Exercise: Interactive Session for Probability Elicitation
Influence Diagrams
Utility Nodes
Decision Nodes
Expected Utility
Automatic Policy Optimization
Example: Oil Wildcatter
Exercises
Function Nodes
Motivation
Inference Functions
Formatting
Function Nodes as Parents
Exercise
About the Instructor
Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has worked in Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks.
After co-founding Bayesia in 2001, he and his team have been working full-time on the development of BayesiaLab. Since then, BayesiaLab has emerged as the leading software package for knowledge discovery, data mining, and knowledge modeling using Bayesian networks. It enjoys broad acceptance in academic communities, business, and industry.
Course Testimonials (2009-2023)
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