MATH 470 Computational Statistics
The objective of this course is for students to develop a fundamental understanding of simulation-based statistical computing methods and the necessary programming skills to implement them beyond the use of commercial software. The primary topics covered in this course will be selected from the following: generation of pseudo-random numbers, Monte Carlo simulation, bootstrapping, linear quadratic estimation (Kalman filter), stochastic gradient descent, and Markov chain Monte Carlo sampling algorithms.