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Advanced BayesiaLab Course — April 15–17, 2024
The Graduate Cincinnati, 151 Goodman Drive, Cincinnati, Ohio 45219
April 15–17, 2024
Building on the foundation laid in the Introductory BayesiaLab Course, we introduce the Advanced BayesiaLab Course for those ready to delve even deeper.
You can take your BayesiaLab certification to the next level with this immersive experience. While the introductory course provided a comprehensive overview of Bayesian network applications, our advanced curriculum dives into the nuances.
We also go extensively into topics only briefly mentioned in the introductory course. In particular, we explore the following areas in greater detail:
- Expert-Based Modeling with BEKEE
- Discretization of Continuous Variables
- Synthesis of New Variables (Manual Synthesis and Data Clustering)
- Fine-Tuning Learning Algorithms
- Network Quality Evaluation
- Target Optimization
- Parameter Sensitivity Analysis
- Function Nodes
- Influence Diagrams
- Dynamic Bayesian Networks
- Bayesian Updating
- Aggregation of Discrete States
- Missing Values Processing
- Credible/Confidence Intervals
- Evidence Analysis
- Function Optimization
- Contribution Analysis
Given that you are already familiar with all the basic concepts, we also have many more hands-on exercises in the Advanced Course than in the Introductory Course.
- 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
- 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 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
- 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, 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, business, and industry.