Introduction to Bayesian Networks & BayesiaLab by Stefan Conrady and Dr. Lionel Jouffe.
From the webpage:
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.
The webpage gives an overview of the white paper. Or you can jump directly to the paper (PDF).
With the emphasis on machine processing, there will be people going through the motions of data processing with a black box and data dumps going into it.
And there will be people who understand the box but not the data flowing into it.
Finally there will be people using cutting edge techniques who understand the box and the data flowing into it.
Which group do you think will have the better results?