Multivariate Analysis and Random Matrices in Statistics
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Course Description
Topics on Multivariate Analysis and Random Matrices in Statistics. Random matrices arise frequently in modern statistical theory, and tools reflecting their properties are the basis of many statistical tests and estimation procedures. Random Matrix theory is both an appealing branch of pure mathematics and an important engine for understanding many phenomena that appear in dealing with modern high-dimensional data. We will emphasize (a) phenomena - the strange things that can happen in high dimensions; (b) sightings - places where these phenomena appear and help explain puzzles in modern machine learning and statistics; (c) monuments - the central objects in the mathematical theory, their names and properties; (d) applications - ways that RMT helps statisticians and applied mathematicians in modern research.
Grading Basis
ROP - Letter or Credit/No Credit
Min
3
Max
3
Course Repeatable for Degree Credit?
No
Course Component
Lecture
Enrollment Optional?
No
Does this course satisfy the University Language Requirement?
No
Programs
STATS325
is a
completion requirement
for:
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