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### Bayesian Networks & BayesiaLabA Practical Introduction for Researchers

By Stefan Conrady and Lionel Jouffe
385 pages, 433 illustrations

Downloaded over 12,000 times since it launched! A hardcopy version is available on Amazon.

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

• 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
• 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
• 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
• 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
• 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
• 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