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Localtab Group


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

titleExamples of probabilistic reasoning
Image RemovedImage Added
  • Cognitive scienceScience: how How our probabilistic brain Probabilistic Brain uses priors Priors in the interpretation Interpretation of imagesImages
  • Interpreting results Results of medical testsMedical Tests

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

titleProbability Theory

  • Probabilistic axiomsAxioms
  • Perception of the Particles
  • Joint probability distribution Probability Distribution (JPD)
  • Probabilistic Expert System for Decision Support: Types of Requests
  • Leveraging independence propertiesIndependence Properties
  • Product/chain rule for compact representation Chain Rule for Compact Representation of JPD

titleBayesian Networks

  • Qualitative partPart: Directed Acyclic Graph
  • Graph terminologyTerminology
  • Graphical Properties
  • D-separationSeparation
  • Markov Blanket
  • Quantitative partPart: marginal and conditional probability distributionsMarginal and Conditional Probability Distributions
  • Exact and Approximate Inference in Bayesian networks
  • Example of probabilistic inferenceProbabilistic Inference: Alarm systemSystem

titleBuilding Bayesian Networks Manually

  • Expert-based Based Modeling via Brainstorming
  • Why Expert-based Based Modeling?
  • Value of Expert-based Based Modeling
  • Structural modelingModeling: Bottom-up Up and Top-down approachesDown Approaches
  • Parametric modelingModeling
  • Cognitive biasesBiases
  • BEKEE: Bayesia Expert Knowledge Elicitation Environment


Localtab Group

titleParameter Estimation

  • Maximum Likelihood Estimation
  • Bayesian Parameter Estimation with Dirichlet priorsPriors
  • Smooth Probability Estimation (Laplacian correctionCorrection)

titleInformation Theory

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

titleUnsupervised Structural Learning

  • Entropy Optimization
  • Minimum Description Length (MDL) Score
  • 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
    • Exclusion of a Node
    • Heuristic Search Algorithms
    • Data Perturbation (Learning, Bootstrap)
    • Choice of the Structural Coefficient
    • Console
    • Symmetric Layout
    • Analysis of the Model (Arc Force, Node Force, Pearson Coefficient)
    • Dictionary of Node Positions
    • Association of an Image in the Background

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