MATH 470 Computational Statistics
The objective of this course is for students to develop a fundamental understanding of writing computer programs that implement simulation-based statistical computations. Topics covered in this course will be selected from the following: generation of random numbers, Monte Carlo simulation, bootstrap methods, linear quadratic estimation (Kalman filter), stochastic gradient descent, Markov Chain Monte Carlo methods, probability density function estimation, linear and nonlinear smoothers. Other topics in computational statistics may be explored at the discretion of the instructor.