Bayesian Networks—Artificial Intelligence for Research, Analytics, and Reasoning
Recorded on September 6, 2017 at Indiana Wesleyan University in West Chester, Ohio.
- Presentation Slides (PDF)
- Bayesian Network File for Example 1b: Where is my Bag?
- Bayesian Network File for Example 2: Breast Cancer Diagnostics
- Bayesian Network File for Example 3: S&P 500
- Bayesian Network File for Example 4: Simpson's Paradox
"Currently, Bayesian Networks have become one of the most complete, self-sustained and coherent formalisms used for knowledge acquisition, representation and application through computer systems." (Bouhamed et al., 2015)
In this workshop, we illustrate how scientists in many fields of study—rather than only computer scientists—can employ Bayesian networks as a very practical form of Artificial Intelligence for exploring complex problems. We present the remarkably simple theory behind Bayesian networks and then demonstrate how to utilize them for research and analytics tasks with the BayesiaLab software platform. More specifically, we explain BayesiaLab's supervised and unsupervised machine learning algorithms for knowledge discovery in high-dimensional domains.