THEORETICAL STATISTICS I

Prerequisites: students in a masters program must seek the director of the M.A. program in statistics' permission; students in an undergraduate program must seek the director of undergraduate studies in statistics' permission. A general introduction to mathematical statistics and statistical decision theory. Elementary decision theory, Bayes inference, Neyman-Pearson theory, hypothesis testing, most powerful unbiased tests, confidence sets. Estimation: methods, theory, and asymptotic properties. Likelihood ratio tests, multivariate distribution. Elements of general linear hypothesis, invariance, nonparametric methods, sequential analysis.

• TR 2:40 p.m.-3:55 p.m.
• Instructor: Cynthia Rush
  CULPA: 1 h-index: 12 Info
• 4 points

THEORETICAL STATISTICS III

Prerequisites: STAT G6201 and STAT G6201 This course will mainly focus on nonparametric methods in statistics. A tentavie list of topics to be covered include nonparametric density and regression function estimation -- upper bounds on the risk of kernel estimators and matching lower bounds on the minimax risk, reproducing kernel Hilbert spaces, bootstrap and resampling methods, multiple hypothesis testing, and high dimensional stastistical analysis.

• M 10:10 a.m.-noon
• Instructor: Ming Yuan
  h-index: 45 Info
• 4 points

PROBABILITY THEORY I

Prerequisites: A thorough knowledge of elementary real analysis and some previous knowledge of probability. Overview of measure and integration theory. Probability spaces and measures, random variables and distribution functions. Independence, Borel-Cantelli lemma, zero-one laws. Expectation, uniform integrability, sums of independent random variables, stopping times, Wald's equations, elementary renewal theorems. Laws of large numbers. Characteristic functions. Central limit problem; Lindeberg-Feller theorem, infinitely divisible and stable distributions. Cramer's theorem, introduction to large deviations. Law of the iterated logarithm, Brownian motion, heat equation.

• MW 2:40 p.m.-3:55 p.m.
• Instructor: Anne Van Delft
  CULPA: 2 h-index: 5 Info
• 4 points

PROBABILITY THEORY III

• T 4:10 p.m.-6 p.m.
• Instructor: Nicolas Trillos
  Info
• 4 points

Probabilistic Models and Machine Learnin

Stat ML: Theory to Practi

Probabilistic Models and Machine Learning is a PhD-level course about how to design and use probability models. We study their mathematical properties, algorithms for computing with them, and applications to real problems. We study both the foundations and modern methods in this field. Our goals are to understand probabilistic modeling, to begin research that makes contributions to this field, and to develop good practices for building and applying probabilistic models.

• MW 2:40 p.m.-3:55 p.m.
• Instructor: Genevera Allen
  Info
• 3 points

Graduate Independent Research

Independent Study with Faculty Advisor must be registered for every semester after first academic year

• Instructor: Marco A Avella Medina
  Info
• Instructor: David Blei
  Wikipedia h-index: 98 Info
• Instructor: John P Cunningham
  CULPA: 7 Info
• Instructor: Richard Davis
  CULPA: 3 h-index: 60 Info
• Instructor: Victor H de la Pena
  h-index: 20 Info
• Instructor: Bianca Dumitrascu
  Info
• Instructor: Andrew Gelman
  CULPA: 6 Wikipedia h-index: 112 Info
• Instructor: Yuqi Gu
  h-index: 6 Info
• Instructor: Ioannis Karatzas
  CULPA: 7 h-index: 58 Info
• Instructor: Samory Kpotufe
  CULPA: 2 Info
• Instructor: Jingchen Liu
  CULPA: 2 h-index: 19 Info
• Instructor: Shaw-Hwa Lo
  CULPA: 14 Info
• Instructor: Arian Maleki
  h-index: 31 Info
• Instructor: Sumit Mukherjee
  CULPA: 4 h-index: 13 Info
• Instructor: Marcel F Nutz
  CULPA: 9 Info
• Instructor: Liam M Paninski
  CULPA: 4 Info
• Instructor: Philip Protter
  h-index: 50 Info
• Instructor: Daniel Rabinowitz
  CULPA: 19 h-index: 34 Info
• Instructor: Cynthia Rush
  CULPA: 1 h-index: 12 Info
• Instructor: Bodhisattva Sen
  h-index: 24 Info
• Instructor: Michael Sobel
  CULPA: 5 Wikipedia h-index: 35 Info
• Instructor: Simon Tavare
  Wikipedia Info
• Instructor: Anne Van Delft
  CULPA: 2 h-index: 5 Info
• Instructor: Zhiliang Ying
  CULPA: 1 Wikipedia h-index: 62 Info
• Instructor: Ming Yuan
  h-index: 45 Info
• Instructor: Tian Zheng
  CULPA: 13 Wikipedia h-index: 25 Info
• Instructor: Simon Tavare
  Wikipedia Info
• Instructor: Genevera Allen
  Info
• Instructor: Christopher Harshaw
  Info
• 3 points

Theoretical Statistics

STAT ANALYSIS-NEURAL DATA

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• F 1:30 p.m.-3 p.m.
• Instructor: Liam M Paninski
  CULPA: 4 Info
• 3 points

SEM-THEORETICAL STATISTIC

Departmental colloquium in statistics.

• M 4:10 p.m.-5:25 p.m.
• 3 points

SEMINAR PROBABILITY THEORY

Departmental colloquium in probability theory.

• F 11:40 a.m.-12:55 p.m.
• Instructor: Ivan Corwin
  CULPA: 1 Wikipedia h-index: 34 Info
• 3 points

SEMINAR IN APPLIED PROB & RISK

A colloquiim in applied probability and risk.

• R 1:10 p.m.-2:25 p.m.
• 1 points

SEM IN MATHEMATICAL FINANCE

Seminar in Mathematical Financ

A colloquium on topics in mathematical finance

• R 4:10 p.m.-5:25 p.m.
• 3 points