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.
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
Utility Nodes
Decision Nodes
Expected Utility
Automatic Policy Optimization
Example: Oil Wildcatter
Exercises
Motivation
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
Unrolled Networks
Compact Networks
Hyperparameters
Conditional Dependencies
Exercise: Bayesian Updating for Equine Anti-Doping
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
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
Manual Synthesis
Binarization
Clustering
K-Means
Bayesian Clustering
Hierarchical Bayesian Clustering
Exercises
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
Maximum 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
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.