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BayesiaLab 5.2: Analytics, Data Mining, Modeling & Simulation
BayesiaLab is a powerful desktop application (Windows/Mac/Unix) for knowledge discovery, data mining, analytics, predictive modeling and simulation - all based on the paradigm of Bayesian networks. Bayesian networks have become a very powerful tool for deep understanding of very complex, high-dimensional problem domains, ranging from bioinformatics to marketing science.
BayesiaLab is the world’s only comprehensive software package for learning, editing and analyzing Bayesian networks. It provides perhaps the easiest way to practically apply artificial intelligence tools, thus transforming and, more importantly, massively accelerating research workflows.
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New White Paper: Introduction to Bayesian Networks and BayesiaLab
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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.
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