Bayesian tools for statistical analysis have made huge leaps forward in the past years, and are on their way to becoming an industry standard. On the other hand, as machine learning tools become more and more prevalent, many issues have come to light: dealing properly with uncertainty, understanding causality, the concept of "statistical significance", etc. In this talk I'll walk you through a basic A/B testing use case and demonstrate the strength challenges of Bayesian tools in dealing with these issues.
Yizhar (Izzy) Toren (M.Sc) is a Bayesian by belief, but a frequentist and ML data scientist engineer by trade. He graduated from Tel Aviv University's (TAU) interdisciplinary program with a Master's degree in Mathematics, worked in multiple industries (bio-tech, gaming, financial services, retail, etc.) and has more than 15 years of experience as a data scientist, consultant and "plain old" statistician. He is also an amateurcrossfitter and considers himself a pretty decent Paleo cook - feel free to ask him for recipes!
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