Teaching Assistantships and Teaching statement

  1. Statistics 204 (Fall 2008): Interdisciplinary Graduate course on Probability

    Instructor: David Aldous
    Verbal Description: A treatment of ideas and techniques most commonly found in the applications of probability: Gaussian and Poisson processes, limit theorems, large deviation principles, information, Markov chains and Markov chain Monte Carlo, martingales, Brownian motion and diffusion.

  2. Statistics 2 (Summer 2008): Undergraduate course on Introduction to Statistics

    Instructor: Brad Luen
    Verbal Description: Population and variables. Standard measures of location, spread and association. Normal approximation. Regression. Probability and sampling. Binomial distribution. Interval estimation. Some standard significance tests.

  3. Statistics 205B (Spring 2008): Main Graduate Probability course Part II

    Instructor: Elchanan Mossel
    Verbal Description: Some knowledge of real analysis and metric spaces, including compactness, Riemann integral. Knowledge of Lebesgue integral and/or elementary probability is helpful, but not essential, given otherwise strong mathematical background. Measuretheory concepts needed for probability. Expectation, distributions. Laws of lar ge numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations; martingales and theory convergence. Markov chains. Stationary processes. Also listed as Mathematics C218B.

  4. Statistics 20 (Summer 2007): Introduction to Probability and Statistics

    Instructor: Timothy A. Thornton
    Verbal Description: For students with mathematical background who wish to acquire basic concepts. Relative frequencies, discrete probability, random variables, expectation. Testing hypotheses. Estimation. Illustrations from various fields.