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Bayesian Networks & BayesiaLab: A Practical Introduction for Researchers

By Stefan Conrady and Lionel Jouffe
385 pages, 433 illustrations

Download your free copy today. http://www.bayesia.com/bayesialab-book

Table of Contents

1. Introduction

  • All Roads Lead to Bayesian Networks
  • A Map of Analytic Modeling

2. Bayesian Network Theory

  • A Non-Causal Bayesian Network Example
  • A Causal Network Example
  • A Dynamic Bayesian Network Example
  • Representation of the Joint Probability Distribution
  • Evidential Reasoning
  • Causal Reasoning
  • Learning Bayesian Network Parameters
  • Learning Bayesian Network Structure
  • Causal Discovery

3. BayesiaLab

  • BayesiaLab’s Methods, Features, and Functions
  • Knowledge Modeling
  • Discrete, Nonlinear and Nonparametric Modeling
  • Missing Values Processing
  • Parameter Estimation
  • Bayesian Updating
  • Machine Learning
  • Inference: Diagnosis, Prediction, and Simulation
  • Model Utilization
  • Knowledge Communication

4. Knowledge Modeling & Reasoning

  • Background & Motivation
  • Example: Where is My Bag?
  • Knowledge Modeling for
    Problem #1
  • Evidential Reasoning for
    Problem #1
  • Knowledge Modeling for
    Problem #2
  • Evidential Reasoning for
    Problem #2

5. Bayesian Networks and Data

  • Example: House Prices in Ames, Iowa
  • Data Import Wizard
  • Discretization
  • Graph Panel
  • Information-Theoretic Concepts
  • Parameter Estimation
  • Naive Bayes Network

6. Supervised Learning

  • Example: Tumor Classification
  • Data Import Wizard
  • Discretization Intervals
  • Supervised Learning
  • Model 1: Markov Blanket
  • Model 1: Performance Analysis
  • K-Folds Cross-Validation
  • Model 2: Augmented Markov Blanket
  • Cross-Validation
  • Structural Coefficient
  • Model Inference
  • Interactive Inference
  • Adaptive Questionnaire
  • WebSimulator
  • Target Interpretation Tree
  • Mapping
7. Unsupervised Learning

  • Example: Stock Market
  • Data Import
  • Data Discretization
  • Unsupervised Learning
  • Network Analysis
  • Inference with Hard Evidence
  • Inference with Probabilistic and Numerical Evidence
  • Conflicting Evidence

8. Probabilistic Structural Equation Models

  • Example: Consumer Survey
  • Workflow Overview
  • Data Import
  • Step 1: Unsupervised Learning
  • Step 2: Variable Clustering
  • Step 3: Multiple Clustering
  • Step 4: Completing the Probabilistic Structural Equation Model
  • Key Drivers Analysis
  • Multi-Quadrant Analysis
  • Product Optimization

9. Missing Values Processing

  • Types of Missingness
  • Missing Completely at Random
  • Missing at Random
  • Missing Not at Random
  • Filtered Values
  • Missing Values Processing in BayesiaLab

10. Causal Identification & Estimation

  • Motivation: Causality for Policy Assessment and Impact Analysis
  • Sources of Causal Information
  • Causal Inference by Experiment
  • Causal Inference from Observational Data and Theory
  • Identification and Estimation Process
  • Causal Identification
  • Computing the Effect Size
  • Theoretical Background
  • Potential Outcomes Framework
  • Causal Identification
  • Ignorability
  • Example: Simpson’s Paradox
  • Methods for Identification and Estimation
  • Workflow #1: Identification and Estimation with a Directed Acyclic Graph
  • Indirect Connection
  • Common Parent
  • Common Child (Collider)
  • Creating a CDAG Representing Simpson’s Paradox
  • Graphical Identification Criteria
  • Adjustment Criterion and Identification
  • Workflow #2: Effect Estimation with Bayesian Networks
  • Creating a Causal Bayesian Network
  • Path Analysis
  • Pearl’s Graph Surgery
  • Introduction to Matching
  • Jouffe’s Likelihood Matching
  • Direct Effects Analysis
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