This meetup is co-hosted with Palo Alto Data Science Association.
In modeling intelligent systems for real world applications, one inevitably has to deal with uncertainty. Bayesian networks are well established as a modeling tool for expert systems in domains with uncertainty, mainly because of their powerful yet simple representation of probabilistic models as a network or graph. They are widely used in fields such as genetic research, healthcare, robotics, document classification, image processing and gaming. Working with large-scale bayesian networks is a computationally-intensive endeavor.
In this talk, Hortonworks Director of Data Science Ofer Mendelevitch will describe how to work with R and Hadoop to implement large scale Bayesian Networks for real world datasets.