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Larry Wasserman UPMC Professor of Statistics and Data Science in the Department of Statistics and Data Science and in the Machine Learning Department Carnegie Mellon University, Pittsburgh, PA 15213, USA Office: Baker Hall 132 G Email: larry@stat.cmu.edu . Phone: (412) 268-8727; Fax: (412) 268-7828
- Machine Learning
Machine Learning Department at Carnegie Mellon University....
- Stat Ml Working Group
We are a group of faculty and students in Statistics and...
- All of Statistics
by Larry Wasserman . Get the book from Springer or Amazon ....
- Data
All of Nonparametric Statistics Data. Data sets used in the...
- Test 3 *** Thursday December 5
Carnegie Mellon University
- Statistics & Data Science
I am a professor in the Department of Statistics and the...
- All of Nonparametric Statistics
Learn about the theory and practice of nonparametric...
- CHAPTER 12 BAYESIAN Inference
Statistical Machine Learning, by Han Liu and Larry...
- 36-401 Modern Regression, Fall 2017
36-401 Modern Regression. Instructor: Larry Wasserman Time:...
- Machine Learning
Larry Alan Wasserman (born 1959) is a Canadian-American statistician and a professor in the Department of Statistics & Data Science and the Machine Learning Department at Carnegie Mellon University. Biography [ edit ]
- University of Toronto (Ph.D, 1988)
- Robert Tibshirani
- 1959 (age 63–64), Windsor, Ontario, Canada
- Carnegie Mellon University
All of nonparametric statistics. L Wasserman. Springer Science & Business Media. , 2006. 2471. 2006. The selection of prior distributions by formal rules. RE Kass, L Wasserman. Journal of the American statistical Association 91 (435), 1343-1370.
Larry Wasserman is a professor at Carnegie Mellon University, where he works on nonparametric inference, causality, and applications of statistics. He has written two textbooks and received several awards and honors for his research.
Instructor: Larry Wasserman Time: MWF 1:20 - 2:10 Place: Zoom . Course description This course will cover the fundamentals of theoretical statistics. We will cover Chapters 1 -- 12 from the text plus some supplementary material. This course is excellent preparation for advanced work in statistics and machine learning. Textbook: Wasserman, L ...
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