Basic theories of statistical estimation, including optimal estimation in finite samples and asymptotically optimal estimation. A careful mathematical treatment of the primary techniques of estimation utilized by statisticians.
Classical introduction to probability theory including expectation, notions of convergence, laws of large numbers, independence, large deviations, conditional expectation, martingales and Markov chains.
See MATH 1552, 1553, 1554, 1564. Concludes the treatment of single variable calculus, and begins linear algebra; the linear basis of the multivariable theory. The first 1/3 of this course covers more advanced single variable calculus. The remaining 2/3 is an introduction to linear algebra, the theory of linear equations in several variables.