Course and Class Details:
Lecture: M W F 17:00-18:00 (Venue: FB557)
Tutorial: Th 17:00-18:00 (Venue: Math Linux Lab in New Core Lab)
Tutorial Schedule: August 30; September 13; October 4, 25; November 15
1. Monte Carlo Statistical Methods by Christian Robert and George Casella
2. Statistical Computing by Debasis Kundu and Ayan Basu
Corrections/Notes : Alias, 1 , 2 ,
Introducing Monte Carlo Methods with R by Robert and Casella
Online Material (related to this book) : 1 , 2 ,
Tutorial Assignments : 1 , 2 , 3 , 4 , 5 ,
Final Assignment [Question 5 : For EM algorithm check slides 6-7]
Probability Requirement
Check results from Chapters 5 and 7 of Karr (preferred)
Basic Texts:
Introduction to Probability Theory by Paul G. Hoel, Sidney C. Port and Charles J. Stone
An Introduction to Probability and Statistics by Vijay K. Rohatgi and A. K. Md. Ehsanes Saleh
Statistical Inference by George Casella and Roger Berger
Markov Chains from Introduction to Stochastic Processes by Paul G. Hoel, Sidney C. Port and Charles J. Stone
Links:
An Introduction - 1 (article) , 2 (article) , 3 (slides) , 4 (talk) , 5 (slides)
Bayesian - 1 , 2 (Conjugate analysis) , 3 (Normal Conjugate)
Avi Wigderson -- "Randomness" and the related article
Alias Algorithm , Alias , More On Alias
Rejection Sampling (check slides 10-11)
Ratio of Uniforms , Paper by Kinderman and Monahan
Box-Muller (pp. 1-2)
Cantor distribution (also see the links here)
Slides of Dootika Vats's talk
Weighted Monte Carlo Integration
Dirichlet - 1 and 2 (spacing of uniform is discussed here)
Markov Chain - Intro , Simulate
Metropolis-Hastings Algorithm - 1 , 2 , 3 ,
Gibbs - 1 ,
EM Algorithm - 1 , 2 (slides), 3 ,