Lecture log

Below you find an overview of what we have discussed in each lecture. This overview will be updated after each lecture.

Week	Date	Topics	Slides	Extra Material	Reading
2	08.01	Organisation of the course. Intro to R. Simulation from discrete RV	Slides	R code for Simulation of Queue, R code for simulation of pseudo random numbers, Intro to R , Some history about random number generators]]	GL: 1.1-1.2-1.3.1, GH: 1 (repetition)
2	10.01	Bivariate techniques (Box-Muller algorithm), ratio-of-uniforms method	Slides	R code to illustrate inversion sampling	GL: 1.3.2, GH: 6-6.2.2
2	11.01	Methods based on mixtures, multivariate Normal, Rejection sampling	Slides	R code to illustrate ratio of uniforms method and mixtures method,R code to illustrate rejection sampling	GL: 1. 4, 1.5.1, GH:6.2.2, 6.2.3
3	15.01	Finish rejection sampling, adaptive rejection sampling, Monte Carlo integration, importance sampling	Slides		GL: 1.5 (all), GH: 6.2.3 (all), 6.3.1, 6.4.1
3	17.01	Finished inportance sampling, Intro to bayesian statistics	Slides	R code to illustrate importance sampling	GL: 2.1, 2.2,
6	05.02	More on Bayesian statistics, MCMC intro	Slides	R code to implement toy MCMC example	GL: 2.3-2.4, 6.1-6.2, GH:7.1
7	12.02	Metropolis-Hasting algorithm and Gibbs sampling	Slides		GL: 6.4, 5.1, 5.2, GH: 7.1-7.2
7	14.02		Slides