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Three-Day Advanced Course:
Artificial Intelligence with Bayesian Networks & BayesiaLab

University of Phoenix, 203 N. LaSalle St, Classroom 1344, Chicago, IL 60601
November 5–7, 2018, 9 a.m.–5 p.m. (daily)

Please not that this course takes place at an offsite venue, not at the BayesiaLab Conference hotel.

Take your BayesiaLab certification to the next level by joining the Advanced BayesiaLab course. The Introductory course gives you a broad view of what you can do with Bayesian networks. In the Advanced course, we study in more detail topics that are only quickly touched during the Introductory course:

  • Expert-Based Modeling with BEKEE
  • Discretization of the Continuous Variables
  • Synthesis of New Variables (Manual Synthesis and Data Clustering)
  • Fine-Tuning of Learning Algorithms
  • Network Quality Evaluation
  • Target Optimization
But more importantly, we cover new topics, such as:
  • Parameter Sensitivity Analysis
  • Function Nodes
  • Influence Diagrams
  • Dynamic Bayesian Networks
  • Bayesian Updating
  • Aggregation of the Discrete States
  • Missing Values Processing
  • Credible/Confidence Intervals Analysis
  • Evidence Analysis
  • Function Optimization
  • Contribution Analysis

Note that we also have much more hands-on exercises than during the Introductory course given that you are already familiar with all the basic concepts. 

The class is limited to a maximum of 15 participants in order to allow for one-on-one coaching during the hands-on exercises with BayesiaLab. This small-group format provides a productive yet informal learning environment that facilitates a lively dialog between participants from a wide range of backgrounds.

Participants in the Advanced Course are required to have completed the Introductory Course on a previous date (see course calendar).

Day 1

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
  • Cognitive Biases
  • 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
  • Exercices

Function Nodes

  • Motivations
  • Inference Functions
  • Formatting
  • Function Nodes as Parents
  • Exercise


Temporal Dimension


  • Hidden Markov Chain
  • Unfolded Temporal Bayesian Networks
  • Dynamic Bayesian Networks
  • Temporal Simulations (Scenarios, Temporal Conditional Dependencies, Temporal Monitoring)
  • Exact and Approximate Inference
  • Unfolding Dynamic Bayesian Networks
  • Exercise: Maintenance of a Fluid Distribution System
  • Network Temporalization
  • Temporal Forecast
  • Exercise: Box & Jenkins

Day 2

Bayesian Updating

  • Unrolled Networks
  • Compact Networks
    • Hyperparameters
    • Conditional Dependencies
  • Exercise: Bayesian Updating for Equine Anti-Doping


Discretization and Aggregation


  • Impact of Discretization
  • Requirements for a Good Discretization
  • Pre and Post Discretization
  • Discretization viewed as the Creation of Latent Variables
  • Discretization Methods
    • Manual by Expertise
    • Univariate
      • Equal Frequency
      • (Normalized) Equal Distance
      • Density Approximation
      • K-Means
      • R2-GenOpt
      • R2-GenOpt*
    • Bi-Variate
      • Tree
      • Perturbed Tree
    • Multi-Variate
      • Supervised with Random Forest
      • Unsupervised with Random Forest
      • R2-GenOpt
      • LogLoss-GenOpt
  • Exercise
  • Aggregation Methods for Symbolic Variables
    • Manual by Expertise
    • Semi-Automatic
    • Bi-Variate with Tree
  • Exercise

Missing Values


  • Types of Missingness
    • Missing Completely at Random (MCAR)
    • Missing at Random (MAR)
    • Not Missing at Random (NMAR)
    • Filtered/Censored/Skipped
  • Types of Methods
    • Static
      • Filtering
      • A Priori Replacement
      • Entropy Based and Standard Static Imputation
    • Dynamic
      • Dynamic Imputation
      • Entropy Based Dynamic Imputation
      • Structural Expectation-Maximization
      • Approximate Dynamic Imputation with Static Imputation
  • Missing Values Imputation (Standard, Entropy-Based, Maximum Probable Explanation)
  • Exercise
  • Filtered/Censored/Skipped Values
  • Example: Survey Analysis

Day 3

Variable Synthesis


  • Manual Synthesis
  • Binarization
  • Clustering
  • K-Means
  • Bayesian Clustering
  • Hierarchical Bayesian Clustering
  • Exercises

Fine Tuning

  • Minimum Description Length (MDL) Score
  • Parameter Estimation with Trees
  • Structural Coefficient
  • Stratification
  • Smooth Probability Estimation
  • Exercise: CarStarts




  • Confidence/Credible Interval Analysis
  • Evidence Analysis
    • Joint Probability of Evidence
    • Log-Loss
    • Information Gain
    • Bayes Factor
    • Most Probable Explanation
    • Maximum A Posteriori
    • Most Relevant Explanation
  • Performance Analysis
    • Supervised
    • Unsupervised
      • Compression
      • Multi-Target
  • Outlier Detection
  • Path Analysis
  • Exercises


  • Genetic Algorithm
  • Objective Function
    • States/Mean
    • Function value
    • Maximization/Minimization
    • Target Value
    • Resources
    • Joint Probability/Support
  • Search Methods
    • Hard Evidence
    • Numerical Evidence
    • Direct Effects
  • Exercise: Marketing Mix Optimization




  • Direct Effects
  • Type I Contribution
  • Type II Contribution
  • Base Mean
  • Normalization
  • Stacked Curves
  • Synergies

About the Instructor

Dr. Lionel Jouffe

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 been working in the field of 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 BayesiaLab, which has since 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. 

Who should attend?

Applied researchers, statisticians, data scientists, data miners, decision scientists, biologists, ecologists, environmental scientists, epidemiologists, predictive modelers, econometricians, economists, market researchers, knowledge managers, marketing scientists, operations researchers, social scientists, students and teachers in related fields. 

What's required?

Participants are required to have completed the Introductory Course.

Terms & Conditions

  • You may cancel your registration for a full refund of the course fees up to 30 days before the start of the course. If you cancel within 30 days of the event, your course fee will not be refunded. However, you will be able to apply 100% of the paid course fees towards future BayesiaLab courses.
  • Accommodation is at the participants' own expense.

Testimonials from Earlier Courses

"I would absolutely recommend this course as a thorough and in-depth introduction to Bayesian Networks and the BayesiaLab package. The small class sizes also contributed to an enjoyable and engaging learning experience."—Brian Potter, Infotools (Introductory Course in Melbourne, November 2015).

"This is one of the best trainings I have ever had! Perfect topic that opens so many opportunities in any domain you could think of. Software is amazing and very intuitive. Presenter is extremely knowledgeable, patient and friendly."—Vladimir Agajanov, Moody's (Introductory Course in New York, January 2016).

"Overall, this training was outstanding. Lionel is a gifted teacher, and it helps that you are showcasing a first rate product. BayesiaLab is the most intuitive and easy-to-use machine-learning software available. It's a first-rate investment."—Felix Elwert, PhD, Vilas Associate Professor of Sociology, University of Wisconsin-Madison (Introductory Course in Chicago, May 2014).

“A must-take course for anyone looking to leverage advanced Bayesian network techniques in virtually any domain.”Alex Cosmas, Chief Scientist, Booz Allen Hamilton (Introductory Course in Los Angeles, June 2011).

“The BayesiaLab software is impressive in its sophistication and multi-faceted abilities as a decision support tool. I had been using it primarily as a modeling tool for deductive analysis. Taking this class opened my eyes to BayesiaLab's incredible data-mining abilities. If you are looking for something that will provide a totally new angle on business decision problems, this is it!”Michael Ryall, PhD, Professor of Strategy and Economics, Rotman Business School, University of Toronto (Introductory Course in Chicago, July 2013).

"This class can only be described as eye-opening, the tool as terrific. Some of the best instruction for the shortest period of time I’ve ever received. A seriously terrific job.” Beau Martin, President of American Choice Modeling (Introductory Course in Chicago, July 2013).