📘E-Book: Bayesian Networks & BayesiaLab — A Practical Introduction for Researchers

By Stefan Conrady and Dr. Lionel Jouffe

The Original Book

We released our first book on Bayesian networks and BayesiaLab at the 3rd Annual BayesiaLab Conference in Fairfax, Virginia, in October of 2015. Among BayesiaLab users, it soon became known simply as "the book" and served as their principal reference. For students of Bayesian networks, it emerged as a very popular textbook, a kind of Bayesian Networks 101.

Beyond the hardcopy, which remains available on Amazon, we have been offering our book as a free PDF, which has been downloaded over 30,000 times since its launch.

However, with the rapid development of new features in BayesiaLab, it's been impossible to keep the book up to date with current screenshots, etc. The BayesiaLab user interface has also undergone a major redesign, and even the BayesiaLab-specific terminology has evolved since 2015. While working intermittently on drafts of a long-promised second edition, we turned our book into a "living document" that can be updated in sync with the software and the user manual.

The New Online Edition

The book's new version is now embedded on our website. As a result, all the original cross-references in the original book have been translated into hyperlinks that can instantly take you to datasets, networks, videos, and other related learning resources.

Despite the ease by which you can jump from the book to the manual and tutorials, you can still follow the linear structure of a traditional book. Countless readers said they enjoyed reading it cover-to-cover, just like a novel.

Table of Contents

pageOverview of BookpageChapter 1: IntroductionpageChapter 2: Bayesian Network TheorypageChapter 3: BayesiaLabpageChapter 4: Knowledge Modeling & Probabilistic ReasoningpageChapter 5: Bayesian Networks and DatapageChapter 6: Supervised LearningpageChapter 7: Unsupervised LearningpageChapter 8: Probabilistic Structural Equation Models for Key Driver AnalysispageChapter 9: Missing Values ProcessingpageChapter 10: Causal Effect Identification and EstimationpageChapter 11: Causality and OptimizationpageChapter 12: Attribution, Contribution, and Counterfactuals

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