Artificial Intelligence with Bayesian Networks and BayesiaLab
Training Overview
 Teaching objectives: Advanced knowledge modeling, machine learning and analysis methods with Bayesian networks and BayesiaLab
 Length: 3 days
 Required Level: Participants in the Advanced Course are required to have completed the Introductory Course.
 Teaching methods: Tutorials with practical exercises using BayesiaLab plus plenty of oneonone coaching
 Trainer: Dr. Lionel Jouffe, CEO, Bayesia SAS.
 Training materials: A printed tutorial (approx. 250 slides), plus a memory stick containing numerous exercises and white papers
 Bayesian network Software: Bayesia provides all trainees with an unrestricted 60day license of BayesiaLab Professional Edition, so they can participate in all exercises on their own laptops
 Cost: Between 2,100 and 2,500 Euros/Trainee, depending on the location of the training session. Discounts are available for groups of trainees of the same company. A special academic discount of 50% is also available for students and teachers of accredited educational institutions.
Here is a link to a Prezi presentation that describes the entire learning journey, from the short introduction to Bayesian networks to the 3rd day of the Advanced course: https://prezi.com/view/VeZ1oBtGKQtLZHwwkVhn/
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:
 ExpertBased Modeling with BEKEE
 Discretization of the Continuous Variables
 Synthesis of New Variables (Manual Synthesis and Data Clustering)
 FineTuning 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 handson exercises than during the Introductory course given that you are already familiar with all the basic concepts.
The registration is complete upon payment of the fee by Bank Transfer, or Credit Cards. Visit the BayesiaLab Store to get the prices corresponding to the type of your organization and number of seats your are interested in.
Training Program
Day 1
 Expertbased Modeling via Brainstorming
 Why Expertbased Modeling?
 Value of Expertbased Modeling
 Structural modeling: Bottomup and Topdown 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
 Experimentation of an Interactive session for probability elicitation

 Utility Nodes
 Decision Nodes
 Expected Utility
 Automatic Policy Optimization
 Example: Oil Wildcatter
 Exercices

 Motivations
 Inference Functions
 Formatting
 Function Nodes as Parents
 Exercise

 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
 Unrolled Networks
 Compact Networks
 Hyperparameters
 Conditional dependencies
 Exercise: Bayesian Updating for Equine AntiDoping

 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
 KMeans
 R2GenOpt
 R2GenOpt*
 BiVariate
 MultiVariate
 Supervised with Random Forest
 Unsupervised with Random Forest
 R2GenOpt
 LogLossGenOpt
 Exercise
 Aggregation Methods for Symbolic variables
 Manual by Expertise
 SemiAutomatic
 BiVariate with Tree
 Exercise

 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
Missing values imputation (Standard, Entropybased, Maximum Probable Explanation) Exercise Filtered/censored/skipped values
Example: Survey analysis


Day 3
 Manual Synthesis
 Binarization
 Clustering
 KMeans
 Bayesian Clustering
 Hierarchical Bayesian Clustering
 Exercises

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

 Credible/Confidence Interval Analysis
 Evidence Analysis
 Most Probable Explanation
 Joint Probability of Evidence
 LogLoss
 Information Gain
 Bayes Factor
 Performance Analysis
 Supervised
 Unsupervised
 Compression
 MultiTarget
 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

