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Bayesian Networks & BayesiaLab
A Practical Introduction for Researchers

By Stefan Conrady and Lionel Jouffe
385 pages, 433 illustrations

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This practical introduction is geared towards scientists who wish to employ Bayesian networks for applied research using the BayesiaLab software platform. Through numerous examples, this book illustrates how implementing Bayesian networks involves concepts from many disciplines, including computer science, probability theory, information theory, machine learning, and statistics. Each chapter explores a real-world problem domain, exploring aspects of Bayesian networks and simultaneously introducing functions of BayesiaLab. The book can serve as a self-study guide for learners and as a reference manual for advanced practitioners.

Please also note that we are currently working on an expanded, second edition of this book. You can check out our work in progress and download individual draft chapters here.

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
    Download Ames Dataset
  • Data Import Wizard
  • Discretization
  • Graph Panel
  • Information-Theoretic Concepts
  • Parameter Estimation
  • Naive Bayes Network

6. Supervised Learning

  • Example: Tumor Classification
    Download WBCD Dataset
  • 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
    Download S&P 500 Dataset
  • 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
    Download Perfume Study Dataset
  • 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

  • Example: Simpson's Paradox
    Download Simpson's Paradox Dataset
  • Motivation: Causality for Policy Assessment and Impact Analysis
  • 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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