Graduate course covering computational methods in statistics, including optimization, simulation, Monte Carlo integration, EM algorithms, MCMC, and resampling methods.
Introduction
Optimization EM Algorithm Numerical Integration Simulation of Random Variables Monte Carlo Integration MCMC Methods Bootstrap Methods
Source .tex files are available upon request.
Selected solution files and RMarkdown source files are available upon request.
Midterm Exam (Sample - has both in-class and
take-home components)
Final Exam
(Sample - has both in-class and take-home components)
Practice Problems
(Final Exam)These materials are provided for practice and illustration purposes only. They are not intended to reflect the content, format, or difficulty of future examinations.
Includes scripts for optimization, EM algorithms, Monte Carlo integration, MCMC methods, bootstrap procedures, and simulation experiments.
The R scripts provided here were developed for instructional purposes and reflect working implementations used in this course. While they ran correctly in the instructor’s environment, no guarantee is made regarding correctness, efficiency, or suitability for other settings. Users are encouraged to adapt and validate the code for their own purposes. If you notice any errors, inconsistencies, or opportunities for improvement, I would greatly appreciate being contacted so that corrections can be made.
Materials are provided for educational use. While care has been taken in their preparation, errors or omissions may occur. If you notice any errors, inconsistencies, or ambiguities in the lecture notes, homework problems, or sample exams, I would appreciate being contacted so that corrections can be made. Please contact me before reuse or adaptation.