Measure and integration, Laws of Large Numbers, convergence of probability measures. Conditional expectation as time permits. Prerequisites: STAT 471 and STAT 512 or their equivalents.
| Section | Capacity | Class times | Login to view Instructor(s) and Location |
|---|---|---|---|
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LECTURE Q1
(89672) |
25 |
2026-01-05 - 2026-04-10 (MWF)
13:00 - 13:50
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Probability Measures, Lebesgue Measure on R, Random Variables, Probability Densities and Distributions, Lebesgue-Stieltjes integration, Independence, Convergence (almost surely, in probability, and in distribution), Product Probabilities, Key Inequalities (Markov, Chebyshev, Hölder, Jensen, and Paley-Zygmund), Characteristic functions, Law of Large Numbers, Central Limit Theorem, Concentration Inequalities, Applications to problems in analysis, geometry, and combinatorics. Prerequisite: STAT 512.
| Section | Capacity | Class times | Login to view Instructor(s) and Location |
|---|---|---|---|
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LECTURE Q1
(80363) |
15 |
2027-01-04 - 2027-04-09 (TR)
14:00 - 15:20
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