SDS 4210
Statistical Computation
Introduction to modern computational statistics. Pseudo-random number generators; inverse transform and rejection sampling. Monte Carlo approximation. Nonparametric bootstrap procedures for bias and variance estimation; bootstrap confidence intervals. Markov chain Monte Carlo methods; Gibbs and Metropolis-Hastings sampling; tuning and convergence diagnostics. Cross-validation. Time permitting, optional topics include numerical analysis in R, density estimation, permutation tests, subsampling, and graphical models. Prior knowledge of R at the level used in Math 494 is required. Acquaintance with fundamentals of programming in R is helpful.
Instructors
Reviews
2-4 hrs/week
Dr. Madrid Padilla is super energetic and teaches things that are really useful. Lots of slides and some of the homework can be tough. No exams and the final project is basically a guaranteed A
12/18/2024
So this course wasn't what I thought it would be. I'm a senior math major and just wanted to fill a requirement. Kuffner is serious but the guy really cares that you learn. At first I was annoyed but now I am so grateful that I actually understand Math 493/494 now. Grades aren't out yet, but the final was easy if you paid attention in lecture.
12/18/2016
Great course, and super nice guy, especially in office hours. He makes so many interesting comments during lecture that are not in the slides (which are awesome). It wasn't a typical class. He really expects you to understand instead of memorize. I feel like I finally understand statistics, much more deeply than before taking the course.
12/17/2016
He just reads blocks of words on his power points. These power points then go online so lecture attendance is not necessary. He does not take criticism lightly (I told him that one of his questions was misleading) and he said that since it was a 400 level course we should be able to interpret his badly written HW
11/21/2016