##Statistics Seminar##\\ Department of Mathematical Sciences ~~META:title =July 11, 2016~~ ^ **DATE:**|Monday, July 11, 2016 | ^ **TIME:**|11:00am to noon | ^ **LOCATION:**|WH 100E | ^ **SPEAKER:**|David M. Steinberg, Tel Aviv University | ^ **TITLE:**|Designing Experiments for GLM's | \\ **Abstract** Many experiments involve non-normal responses. Yet until recently not much was known about how to design efficient experiments. Standard plans appropriate for normal data continued to be used in practice. I will present the main ideas that guide modern ideas for design for GLM's. Sequential experimentation has great advantages in this setting. I will describe a format for sequential Bayesian learning and how to apply it in experimental design. I will discuss applications to two areas: sensitivity testing and active learning. In the former, the goal is to estimate a distribution when you are limited to asking binary questions. For example, you want to know an item's breaking strength but can only apply a known force and see if the item breaks. In active learning, the goal is to sample cases with known features but unknown classification in order to achieve a good classification rule. The talk will cover joint work with Hovav Dror, Chris Gotwalt, Brad Jones, Lotem Kaplan and Amit Teller.