Three-Day Introductory BayesiaLab Course in Miami Beach, Florida
Spaces — 1111 Lincoln Road, Miami Beach, FL 33139 October 7–9, 2024
Since 2009, our BayesiaLab courses and events have spanned the globe. From New York to Sydney, Paris to Singapore, we've touched down in cities worldwide (take a peek at our photo gallery!).
We have a fantastic venue for our course. The classroom is in the heart of Miami Beach, just a few blocks from the glamorous South Beach.
The Introductory BayesiaLab Course is more than just a beginner's guide. It's a deep dive into applying Bayesian networks across diverse fields, from marketing science and econometrics to ecology and sociology. And we don't just stick to theory. Every conceptual lesson transitions seamlessly into hands-on practice with BayesiaLab, allowing you to apply what you've learned directly, whether in knowledge modeling, causal inference, machine learning, or more.
Over 2,000 researchers worldwide can vouch for its impact, many of whom have made Bayesian networks and BayesiaLab integral to their research. Don't just take our word for it - check out the testimonials!
Bayesian Networks: Artificial Intelligence for Decision Support under Uncertainty
Probabilistic Expert Systems
Bayesian Networks and Cognitive Science
Unstructured and Structured Particles Describing the Domain
Expert-Based Modeling and/or Machine Learning
Predictive vs. Explanatory Models, i.e., Association vs. Causation
Examples:
Medical Expert Systems
Consumer Segmentation
Drivers Analysis
Product Optimization
Examples of Probabilistic Reasoning
Cognitive Science: How our probabilistic brain uses priors for the interpretation of images
Interpreting Results of Medical Tests
Kahneman & Tversky’s Yellow Cab/White Cab Example
Probability Theory
Probabilistic Axioms
Perception of Particles
Probabilistic Expert System for Decision Support: Types of Requests
Leveraging Independence Properties
Bayesian Networks
Qualitative Part: Directed Acyclic Graph
Graph Terminology
Graphical Properties
D-Separation
Markov Blanket
Quantitative Part: Marginal and Conditional Probability Distributions
Exact and Approximate Inference in Bayesian networks
Example of Probabilistic Inference: Alarm System
Building Bayesian Networks Manually
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
Parameter Estimation
Bayesian Parameter Estimation with Dirichlet Priors
Smooth Probability Estimation (Laplacian Correction)
Information Theory
Conditional Entropy
Symmetric Relative Mutual Information
Unsupervised Structural Learning
Entropy Optimization
Structural Coefficient
Minimum Size of Data Set
Search Spaces
Search Strategies
Learning Algorithms
Maximum Weight Spanning Tree
Taboo Search
EQ
TabooEQ
SopLEQ
Taboo Order
Data Perturbation
Example: Exploring the relationships in body dimensions
Data Import (Typing, Discretization)
Definition of Classes
Excluding a Node
Heuristic Search Algorithms
Data Perturbation (Learning, Bootstrap)
Choosing the Structural Coefficient
Console
Symmetric Layout
Model Analysis: Arc Force, Node Force, and Pearson Coefficient
Dictionary of Node Positions
Adding a Background Image
Supervised Learning
Learning Algorithms
Naive
Augmented Naive
Manual Augmented Naive
Tree-Augmented Naive
Sons & Spouses
Markov Blanket
Augmented Markov Blanket
Minimal Augmented Markov Blanket
Variable Selection with the Markov Blanket
Example: Predictions Based on Body Dimensions
Data Import (Data Type, Supervised Discretization)
Heuristic Search Algorithms
Target Evaluation (In-Sample, Out-of-Sample: K-Fold, Test Set)
Smoothed Probability Estimation
Analysis of the Model (Monitors, Mapping, Target Report, Target Posterior Probabilities, Target Interpretation Tree)
Evidence Scenario File
Automatic Evidence-Setting
Adaptive Questionnaire
Batch Labeling
Semi-Supervised Learning - Variable Clustering
Algorithms
Example: S&P 500 Analysis
Variable Clustering
Changing the number of Clusters
Dynamic Dendrogram
Dynamic Mapping
Manual Modification of Clusters
Manual Creation of Clusters
Semi-Supervised Learning
Search Tool (Nodes, Arcs, Monitors, Actions)
Sticky Notes
Data Clustering
Synthesis of a Latent Variable
Expectation-Maximization Algorithm
Ordered Numerical Values
Cluster Purity
Cluster Mapping
Hypercube Cells Per State
Example: Segmentation of Men Based on Body Dimensions
Data Clustering (Equal Frequency Discretization, Meta-Clustering)
Quality Metrics (Purity, Log-Loss, Contingency Table Fit)
Posterior Mean Analysis (Mean, Delta-Means, Radar charts)
Mapping
Cluster Interpretation with Target Dynamic Profile
Cluster Interpretation with Target Optimization Tree
Projection of the Cluster on other Variables
Probabilistic Structural Equation Models
PSEM Workflow
Multiple Clustering for Creating a Factor Variable (via data Clustering) per Cluster of Manifest Variables
Unsupervised Learning for Representing the Relationships between the Factors and the Target Variable
Example: The French Market of Perfumes
Cross-Validation of the Clusters of Variables
Display of Classes
Total Effects
Direct Effects
Direct Effect Contributions
Tornado Analysis
Taboo, EQ, TabooEQ, and Arc Constraints
Multi-Quadrant Analysis
Exporting Variations
Target Optimization (Dynamic Profile)
Target Optimization (Tree)
Since then, BayesiaLab has emerged as the leading software package for knowledge discovery, data mining, and knowledge modeling using Bayesian networks. 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.