![]() Kernel density estimation Nonkernel methods Permutation tests Missing data, marginalization, and notation Markov chains Metropolis-Hastings algorithm Introduction to the Monte Carlo method Exact simulationĪpproximate simulation Variance reduction techniques ![]() The projects may come from the optional problems assigned by the instructor or be proposed by the students themselves upon the approval of the instructor. Project: Students are required to work in groups on course projects and submit their final reports before May 1st, Friday, 10:00 am. Half of the grade counts for correctness of one selected problem. Half of the grade counts for completeness Prerequisite: STAT 411 or consent of instructor. ![]() John Wiley & Sons, Inc., 2nd edition, 2013.Ĭourse Contents: EM Optimization Methods, Simulation and Monte Carlo Integration, Markov Chain Monte Carlo, Bootstrapping, Nonparametric Density Estimation, Bivariate Smoothing Office Hours: Monday, Wednesday, Friday at 2:00 p.m. Time: Monday, Wednesday, Friday at 10:00 AM - 10:50 AM Course information for Stat 451 STAT 451 (37112, 37113) Computational Statistics Spring Semester 2020
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