Monte Carlo Methods MAP001169
Fall 2017

Components: 20 hours of lectures (10 lectures), 20 hours of discussion sessions (10 labs),

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:
- PRIMARY - Monte-Carlo methods and stochastic processes , by Emmanuel Gobet, CRC Press, 2016
- SUPPORTING - Computer Intensive Statistical Methods , Lecture notes by Martin Skold, Lund University, 2005.

Instructors:

Krzysztof Podgorski , Department of Statistics, Lund University e-mail: Krzysztof.Podgorski@stat.lu.se,
On leave in the Department of Mathematics at Wroclaw University of Science and Technology
Consulting hours: 10:00-12:00 Tuesday, Room 2.15 in the Math Building or by appointment
Krzysztof Bogdan , Department of Mathematics at Wroclaw University of Science and Technology e-mail: Krzysztof.Bogdan@pwr.edu.pl,

Weakly Timetable

Schedule of classes

Week #	Day	Time and Location	Lecture/Discussion #	Material	Handouts/Reading material
40	Tuesday, 03/10/2017	15:15-16:55 4.04 C-11	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
40	Wednesday, 04/10/2017	13:15-15:00 P.01 C-11	Lecture 1 (KP): Simulating from random models	Effective random simulation
41	Tuesday, 10/10/2017	15:15-16:55 4.04 C-11	Lab 2 (KB):	Assignment 2	Assignment 2 , Tablica 5 , Tablica 6 , Tablica 7 , Tablica 8 Tablica 9 , Tablica 10
41	Wednesday, 11/10/2017	13:15-15:00 P.01 C-11	Lecture 2 (KP): Rejection algorithm and conditional methods	Rejection algorithm and conditional methods
42	Tuesday, 17/10/2017	15:15-16:55 4.04 C-11	Lab 3 (KB):	Assignment 3	Assignment 3 ,
42	Wednesday, 18/10/2017	13:15-15:00 P.01 C-11	Lecture 3 (KP): Monte Carlo integration	MC-Integration	Assignment 3 ,
43	Tuesday, 24/10/2017	15:15-16:55 4.04 C-11	Lab 4 (KB):
43	Wednesday, 25/10/2017	13:15-15:00 P.01 C-11	Lecture 4 (KP): Importance sampling	Reduction of variance
44	Monday 30/10/2017	13:15-15:00 P.01 C-11	Lecture 5 (KP): Monte Carlo Markov Chain -- Introduction	MCMC
44	Tuesday, 31/10/2017	15:15-16:55	Rector's Hours
45	Tuesday, 07/11/2017	15:15-16:55 4.04 C-11	Lab 6 (KB):
45	Wednesday, 08/11/2017	13:15-15:00 P.01 C-11	Lecture 6 (KP): Metropolis-Hastings algorithm and Gibbs sampler	Metropolis-Hastings algorithm, Gibbs sampler
46	Tuesday, 14/11/2017	15:15-16:55 4.04 C-11	Lab 7 (KB):
46	Wednesday, 15/11/2017	13:15-15:00 P.01 C-11	Lecture 7 (KP): Independence and random walk proposals
47	Tuesday, 21/11/2017	15:15-16:55 4.04 C-11	Lab 8 (KB):
47	Wednesday, 22/11/2017	13:15-15:00 P.01 C-11	Lecture 8 (KP): Review of Monte Carlo integration	Complete notes for the first part of the course
48	Tuesday, 28/11/2017	15:15-16:55 4.04 C-11	Lab 9 (KB):
48	Wednesday, 29/11/2017	13:15-15:00 P.01 C-11	Test I (KP): Labs 1-8
49	Tuesday, 05/12/2017	15:15-16:55 4.04 C-11	Group A (KB) Lab :
49	Wednesday, 06/12/2017	13:15-15:00 P.01 C-11	Lecture 9 (KP): Statistical inference	Fundamentals of statistical inference
50	Tuesday, 12/12/2017	15:15-16:55 4.04 C-11	Lab (KB):		,
50	Wednesday, 13/12/2017	13:15-15:00 P.01 C-11	Lecture 10 (KP): Bootstrap method	Bootstrap method	,
51	Tuesday, 19/12/2017	15:15-16:55 4.04 C-11	Group A (KB) Lab :
51	Wednesday, 20/12/2017	13:15-15:00 P.01 C-11	Lecture 11 (KB): Bayesian inference
2	Tuesday, 09/01/2017	15:15-16:55 4.04 C-11	Lab (KB):		,
2	Wednesday, 10/01/2017	13:15-15:00 P.01 C-11	Lecture 12 (KB): Bayesian computation		,
3	Tuesday, 16/01/2017	15:15-16:55 4.04 C-11	Group A (KB) Lab :
3	Wednesday, 17/01/2017	13:15-15:00 P.01 C-11	Lecture 13 (KB): Review of Monte Carlo methods for statistical inference
4	Tuesday, 23/01/2017	15:15-16:55 4.04 C-11	Lab (KB):
4	Wednesday, 24/01/2017	13:15-15:00 P.01 C-11	Test II (KB): Labs 6-14

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)

The components to the total score: 2 in-class tests (1 hour each) --60% (30% each), active participation in lab sessions (answering questions, presenting solutions, written reports on selected solutions) --10%, raports from labs --30%

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)

Monte Carlo Methods MAP001169 Fall 2017

Weakly Timetable

Schedule of classes

Monte Carlo Methods MAP001169
Fall 2017