This is the course webpage for MTH210a: Statistical Computing from 2023-24 Semester-II.

Students: Due to limited lab space availability, I am only able to allow DC students to take this course. Please do not email me requesting to take the course. There is nothing I can do.


Course expectations:

The topics will revolve around Monte Carlo simulations and Optimization methods, along with topics that combine the use of both together - details on the course outline will be uploaded soon. You must be proficient in R (or willing to devote time to be proficient).

Knowledge of elementary concepts in probability and statistics are a must: you must be familiar with the law of large numbers, central limit theorem, expectation, variance, bias, mean squared error, and basic distributions and their properties.

Information:


References:

  • Sampling from Distributions:
    • “Simulation” by Sheldon M. Ross (Academic Press, Fourth Edition), 2006, Chaps. 1-5.
    • “Non-Uniform Random Variable Generation” by Luc Devroye. Online book
    • Bernoulli Factories: “Designing perfect simulation algorithms using local correctness” arxiv
    • Ratio-of-Uniforms method Paper, Resource 1, Resource 2 <!– - Maximum Likelihood Estimation
    • “Statistical Inference” by Casella and Berger.
    • MLE estimation notes at UChicago link
  • Regression
    • “Applied Linear Regression” by Sanford Weisberg
    • Elements of Statistical Learning by Hastie, Tibshirani, and Friedman Link
  • Optimization
    • “Convex Optimization” by Boyd and Vandenberghe Link
    • MM Algorithm notes by Kenneth Lange Link
    • MM Algorithm notes Link
  • EM Algorithm
  • Cross-validation
    • Slides Link
    • Note Link
    • Elements of Statistical Learning by Hastie, Tibshirani, and Friedman - Chapter 7 Link
  • Bootstrap
  • MCMC
  • Linchpin Accept-Reject
  • Importance Sampling