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--- seminars:stat:160711 [2016/07/02 19:51] – qyu
+++ seminars:stat:160711 [2016/07/10 19:13] (current) – qiao
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+~~META:title =July 11, 2016~~
+<WRAP 70% center>
+^  **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 |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+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.
+</WRAP>