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Localtab Group


  • Bayesian networks: Artificial Intelligence for Decision Support under uncertainty
  • Probabilistic Expert System
  • The Modeling World
  • Bayesian networks and Cognitive Science
  • Unstructured and structured particles/observations describing the domain
  • Expert Based Modeling and/or Machine Learning
  • Predictive (association) versus Explicative (causation) models
  • Application examples: Medical Expert Systems, Stock Market Analysis, Microarray Analysis, Consumer Segmentation, Drivers Analysis and Product Optimization

titleExamples of probabilistic reasoning

  • Cognitive science: how our probabilistic brain uses priors in the interpretation of images

  • Interpreting results of medical tests

  • Kahneman & Tversky’s Yellow Cab/White Cab example
  • The Monty Hall Problem, solving a vexing puzzle with a Bayesian network
  • Simpson’s Paradox - Observational Inference vs Causal Inference

titleProbability Theory

  • Probabilistic axioms
  • Perception of the Particles
  • Joint probability distribution (JPD)
  • Probabilistic Expert System for Decision Support: Types of Requests
  • Leveraging independence properties
  • Product/chain rule for compact representation of JPD

titleBayesian 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

titleManually 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
  • BEKEE: Bayesia Expert Knowledge Elicitation Environment


Localtab Group

titleParameter Estimation

  • Maximum Likelihood Estimation
  • Bayesian Parameter Estimation with Dirichlet priors
  • Smooth Probability Estimation (Laplacian correction)

titleInformation Theory

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  • Information is a measurable quantity: Log-Loss
  • Expected Log-Loss
  • Entropy
  • Conditional Entropy
  • Mutual Information
  • Symmetric Relative Mutual Information
  • Kullback-Leibler Divergence

titleUnsupervised Structural Learning

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  • Entropy Optimization
  • Minimum Description Length (MDL) scoreScore
  • Structural Coefficient
  • Minimum size Size of data setData Set
  • Search Spaces
  • Search Strategies
  • Learning algorithmsAlgorithms
    • 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
    • Exclusion of a nodeNode
    • Heuristic Search Algorithms
    • Data Perturbation (Learning, Bootstrap)
    • Choice of the Structural Coefficient
    • Console
    • Symmetric Layout
    • Analysis of the model Model (Arc Force, Node Force, Pearson Coefficient)
    • Dictionary of Node Positions
    • Association of an image Image in the backgroundBackground

titleSupervised 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 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


Localtab Group

titleSemi-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 (nodesNodes, arcsArcs, monitorsMonitors, actionsActions)
    • Sticky Notes

titleData Clustering

  • Synthesis of a Latent Variable
  • Expectation-Maximization Algorithm
  • Ordered Numerical Values
  • Cluster Purity
  • Cluster Mapping
  • Log-Loss and Entropy of the Data
  • Contingency Table Fit
  • 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

titleProbabilistic Structural Equation Models

  • PSEM Workflow
    • Unsupervised Structural Learning
    • Variable Clustering
    • Multiple Clustering for creating Creating a Factor variable Variable (via data Clustering) per cluster Cluster of Manifest Variables
    • Unsupervised Learning for representing Representing the relationships Relationships between the Factors and the Target variablesVariables
  • Example: The French Market of Perfumes
    • Cross-validation of the clusters Clusters of variablesVariables
    • Displayed Classes
    • Total Effects
    • Direct Effects
    • Direct Effect Contributions
    • Tornado Analysis
    • Taboo, EQ, TabooEQ, and Arc Constraints
    • Multi-Quadrants
    • Export Variations
    • Target Optimization with Dynamic Profile
    • Target Optimization with Tree