Herbert Lee

 

 


 

Herbert Lee is Professor of Applied Mathematics and Statistics in the Jack Baskin School of Engineering at the University of California, Santa Cruz, where he also serves as Vice Provost for Academic Affairs and Campus Diversity Officer for Faculty. He received his B.S. in Mathematics from Yale University, and his M.S. and Ph.D. in Statistics from Carnegie Mellon University. He completed a post-doc at Duke University before joining the UC Santa Cruz faculty in 2002. He is an applied Bayesian statistician with research interests that include computer simulation experiments, inverse problems, optimization, spatial statistics, classification and clustering, and neural networks. His published research includes two books, Bayesian Nonparametrics via Neural Networks and Multiscale Modeling: A Bayesian Perspective (co-authored with Marco Ferreira) as well as a wide variety of papers.




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Nov 21st 2016

This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach.

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