Introduction to Bayesian Networks & BayesiaLab
Stefan Conrady, Dr. Lionel Jouffe, DOI: 10.13140/2.1.4737.6965
With Professor Judea Pearl receiving the prestigious 2011 A.M. Turing Award, Bayesian networks have presumably received more public recognition than ever before. Judea Pearl’s achievement of establishing Bayesian networks as a new paradigm is fittingly summarized by Stuart Russell:
“[Judea Pearl] is credited with the invention of Bayesian networks, a mathematical formalism for defining complex probability models, as well as the principal algorithms used for inference in these models. This work not only revolutionized the field of artificial intelligence but also became an important tool for many other branches of engineering and the natural sciences. He later created a mathematical framework for causal inference that has had significant impact in the social sciences.”
While their theoretical properties made Bayesian networks immediately attractive for academic research, especially with regard to the study of causality, the arrival of practically feasible machine learning algorithms has allowed Bayesian networks to grow beyond its origin in the field of computer science. Since the first release of the BayesiaLab software package in 2001, Bayesian networks have finally become accessible to a wide range of scientists and analysts for use in many other disciplines.
In this introductory paper, we present Bayesian networks (the paradigm) and BayesiaLab (the software tool), from the perspective of the applied researcher.
In Chapter 1 we begin with the role of Bayesian networks in today’s world of analytics, juxtaposing them with traditional statistics and more recent innovations in data mining.
Once we establish how Bayesian networks fit into the proverbial big picture, we present in Chapter 2 the mathematical formalism that underpins this paradigm. While employing Bayesian networks for research has become remarkably easy with BayesiaLab, we need to emphasize importance of their theory. Only a deep understanding of this theory will allow researchers to fully appreciate the wide-ranging benefits of Bayesian networks.
Finally, in Chapter 3, we provide an overview of the BayesiaLab software platform, which leverages the Bayesian networks paradigm to far greater extent than any other tool that has ever been available in this field. We show how the theoretical properties of Bayesian networks translate into an extremely powerful and universal research tool for many fields of study, ranging from bioinformatics to marketing science and beyond.
We are grateful to Judea Pearl for allowing us to use and adapt large sections from one of his technical reports for this introductory paper.