# MWRC: Carl Youngblood - Simple Bayesian Networks with Ruby

Bayesian networks are excellent tools for modeling knowledge, especially in realistic situations where there is incomplete domain awareness. Given reasonably accurate causal relationships between variables, a Bayesian network can determine the most likely state of unobserved variables in a system. Bayesian networks are used in a wide variety of applications, including textual analysis, image processing, consumer credit scoring systems, and other decision support systems.

Until recently, open source Bayesian network libraries were not readily available, and those that have been released are, in this author's opinion, unnecessarily complex. The Simple Bayesian Networking Library (SBN) and its associated Ruby module (SBN4R) make it easy to harness the power of Bayesian networks in your application. This presentation will cover what Bayesian networks are and how they are used, as well as how to use SBN4R in your next Ruby application.

Carl Youngblood has been using Ruby since the publication of the Pickaxe book in 2000. He currently works as the CTO of Construction Capital Source, a construction lending firm with headquarters in Salt Lake City. This position is especially exciting for Carl, since it allows him to use Ruby all day at work. Carl received a bachelors degree in Portuguese from Brigham Young University and a masters degree in Computer Science from the University of Washington. He has been working professionally as a software engineer for ten years.