Monte Carlo Methods MAP001169

Course description:
This course covers fundamentals of the theory of stochastic processes.
Classes will be provided in two forms: lectures, discussion sessions, where the assignments will be discussed.
Textbooks:
Instructors:
Week #  Day  Time and Location  Lecture/Discussion #  Material  Handouts/Reading material 

40  Tuesday, 03/10/2017  15:1516:55 4.04 C11  Lab 1 (KB): Simulation  Assignment 1  Basic methods of simulation from random distributions  Assignment 1  Basic methods of simulation from random distributions , Tablica 1 , Tablica 2 , Tablica 3 , Tablica 4 
Wednesday, 04/10/2017  13:1515:00 P.01 C11  Lecture 1 (KP): Simulating from random models  Effective random simulation  
41  Tuesday, 10/10/2017  15:1516:55 4.04 C11  Lab 2 (KB):  Assignment 2  Assignment 2 , Tablica 5 , Tablica 6 , Tablica 7 , Tablica 8 Tablica 9 , Tablica 10 
Wednesday, 11/10/2017  13:1515:00 P.01 C11  Lecture 2 (KP): Rejection algorithm and conditional methods  Rejection algorithm and conditional methods  
42  Tuesday, 17/10/2017  15:1516:55 4.04 C11  Lab 3 (KB):  Assignment 3  Assignment 3 , 
Wednesday, 18/10/2017  13:1515:00 P.01 C11  Lecture 3 (KP): Monte Carlo integration  MCIntegration  
43  Tuesday, 24/10/2017  15:1516:55 4.04 C11  Lab 4 (KB):  
Wednesday, 25/10/2017  13:1515:00 P.01 C11  Lecture 4 (KP): Importance sampling  Reduction of variance  
44  Monday 30/10/2017  13:1515:00 P.01 C11  Lecture 5 (KP): Monte Carlo Markov Chain  Introduction  MCMC  
Tuesday, 31/10/2017  15:1516:55  Rector's Hours  
45  Tuesday, 07/11/2017  15:1516:55 4.04 C11  Lab 6 (KB):  
Wednesday, 08/11/2017  13:1515:00 P.01 C11  Lecture 6 (KP): MetropolisHastings algorithm and Gibbs sampler  MetropolisHastings algorithm, Gibbs sampler  
46  Tuesday, 14/11/2017  15:1516:55 4.04 C11  Lab 7 (KB):  
Wednesday, 15/11/2017  13:1515:00 P.01 C11  Lecture 7 (KP): Independence and random walk proposals  
47  Tuesday, 21/11/2017  15:1516:55 4.04 C11  Lab 8 (KB):  
Wednesday, 22/11/2017  13:1515:00 P.01 C11  Lecture 8 (KP): Review of Monte Carlo integration  Complete notes for the first part of the course  
48  Tuesday, 28/11/2017  15:1516:55 4.04 C11  Lab 9 (KB):  
Wednesday, 29/11/2017  13:1515:00 P.01 C11  Test I (KP): Labs 18  
49  Tuesday, 05/12/2017  15:1516:55 4.04 C11  Group A (KB) Lab :  
Wednesday, 06/12/2017  13:1515:00 P.01 C11  Lecture 9 (KP): Statistical inference  Fundamentals of statistical inference  
50  Tuesday, 12/12/2017  15:1516:55 4.04 C11  Lab (KB):  ,  
Wednesday, 13/12/2017  13:1515:00 P.01 C11  Lecture 10 (KP): Bootstrap method  Bootstrap method  
51  Tuesday, 19/12/2017  15:1516:55 4.04 C11  Group A (KB) Lab :  
Wednesday, 20/12/2017  13:1515:00 P.01 C11  Lecture 11 (KB): Bayesian inference  
2  Tuesday, 09/01/2017  15:1516:55 4.04 C11  Lab (KB):  ,  
Wednesday, 10/01/2017  13:1515:00 P.01 C11  Lecture 12 (KB): Bayesian computation  
3  Tuesday, 16/01/2017  15:1516:55 4.04 C11  Group A (KB) Lab :  
Wednesday, 17/01/2017  13:1515:00 P.01 C11  Lecture 13 (KB): Review of Monte Carlo methods for statistical inference  
4  Tuesday, 23/01/2017  15:1516:55 4.04 C11  Lab (KB):  
Wednesday, 24/01/2017  13:1515:00 P.01 C11  Test II (KB): Labs 614 
Grading: The work throughout the discussion session will be compounded into the final grade as follows:
Percentage  Grade 

49  0  F (2.0) 
59  50  C (3.0) 
69  60  C+ (3.5) 
79  70  B (4.0) 
89  80  B+ (4.5) 
100  90  A (5.0) 