** TMA4300 Computer Intensive Statistical Methods spring 2019 ** ====== 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 | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture1/Lecture1.pdf| Slides]] | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture1/Lecture1_Queue.R| R code for Simulation of Queue]], [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture1/Lecture1_PseudoRandomNumber.R| R code for simulation of pseudo random numbers]], [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture1/Lecture1_Intro_to_R.R| Intro to R ]], [[[[https://medium.freecodecamp.org/a-brief-history-of-random-numbers-9498737f5b6c|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 | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture2/Lecture2.pdf| Slides]] | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture3/inversion.R|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 | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture3/Lecture3.pdf| Slides]] | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture3/ratio_of_unif.R|R code to illustrate ratio of uniforms method and mixtures method]],[[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture3/rejection_sampling.R|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 | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture4/Lecture4.pdf| 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 | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture5/Lecture5.pdf| Slides]] | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture5/Importance_sampling.R|R code to illustrate importance sampling]] | GL: 2.1, 2.2, | | 6 | 05.02 | More on Bayesian statistics, MCMC intro | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture6/Lecture6.pdf| Slides]] | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture6/demo_toyMC2.R| 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 | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture7/Lecture7.pdf| Slides]] | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture7/demo_MCMC_RW.R| R code to illustrate RW proposal]], [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture7/demo_mcmcRao.R| R code for the Rao example]] | GL: 6.4, 5.1, 5.2, GH: 7.1-7.2 | | 7 | 14.02 | Gibbs sampling, Convergence diagnostic | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture8/Lecture8.pdf| Slides]] | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture8/demo_linear_reg_Gibbs.R| R code to implement Gibbs sampling for linear regression]], [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture8/Vikings.R| R code to implement Gibbs sampling for the vikings example]] | GL: 6.4, 5.3, 5.4, GH: 7.2-7.3 | | 7 | 15.02 | Convergence diagnostic, Intro to INLA | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture9/Lecture9.pdf| Slides]] | | GL:5.3,5.3, GH:7.2,7.3 | | 8 | 19.02 | Integrated nested Laplace approximation (INLA) | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture10/Lecture10.pdf| Slides]] | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture10/Taylor_expansion.R| R code to illustrate the GMRF approximation]] , [[https://www.precision-analytics.ca/blog-1/inla| A gentle introduction to INLA]] | | | 8 | 21.02 | Integrated nested Laplace approximation (INLA) | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture11/R_INLA.pdf| Slides]] | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture11/inla_simple.R|Simple example of R-INLA]] , [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture11/Scotland.R|Besag model with INLA]] , [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture11/R_INLA|R code from the slides]] | | | 11 | 12.03 | Bootstrap | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture12/Lecture12.pdf| Slides]] | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture12/Bootstrap_intro.R|R code with intro to bootstrap]] | GH: 9.1, 9.2 | | 11 | 15.03 | Bootstrap | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture13/Lecture13.pdf| Slides]] | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture13/boot_example.R|R code with bootstrap example]], [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture13/demo-pairedBootstrap.R|R code Nickel Alloy example]], [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture13/lutenizing_boot.R|R code Lutenizing hormon example]] | GH: 9.3.1, 9.5.1, 9.5.2 (until 9.5.2.3 included) | | 12 | 19.03 | Bootstrap and permutation test | same slide as last time | | | | 12 | 22.03 | EM algorithm | [[http://www.math.ntnu.no/emner/TMA4300/2019v/Lecture15/Lecture15.pdf| Slides]] | | GH: 4.1, 4.2 | | | 11.04 | Summary | [[http://www.math.ntnu.no/emner/TMA4300/2019v/summary.pdf| Slides]] | | |