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SAMSI SPRING 2013 Course: Computational and Inferential Methods for High Dimensions and Massive Datasets

Principal Instructors: Various

Course Day and Time: Course will be held at SAMSI (driving directions) in RTP on Tuesdays, 4:30-7:00 p.m. in Room 150.

Schedule: First class Tuesday, January 8, 2013; last class day, Tuesday, April 16, 2013

This course focuses on fundamental methodological questions of statistics, mathematics and computer science posed by massive datasets, with applications to astronomy, high energy physics, and the environment.

Topics will include:

Data: storage and transfer of massive datasets, missing and noisy data, complex data structures
Computing: efficient computational algorithms, simulation

Visualization: data visualization to enhance human understanding
Statistical Inference: problems and opportunities in high dimensional data; false discovery rates; regularization, Bayes and empirical Bayes; parametric, semi-parametric and non- parametric modeling; leveraging algorithms and computer resources

Registration for this course is being processed through your respective university:
Duke: STA 790.01
NCSU: MA 810.002 and ST 810.010
UNC:STOR 892.1

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Semiparametric models

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Karp (1991) - Randomized algorithms

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Classification references

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Lopiano - Non-negative matrix decomposition

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Big Data and Climate Science

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Class schedule - subject to change

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BayesVsFrequentistFDR

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Classical Multiple Testing procecures

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Bayesian FDR

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Sun's paper on confidence bands