Monte Carlo Simulations (L7) (865G1)
Monte Carlo Simulations (L7)
Module 865G1
Module details for 2023/24.
15 credits
FHEQ Level 7 (Masters)
Library
Main recommendation: Neil Madras, Lectures on Monte Carlo Methods, American Mathematical Society, 2002. Supplementary texts: Torsten Hothorn, Brian S. Everitt, A Handbook of Statistical Analyses Using R, Chapman and Hall/CRC, 2009. Luc Devroye, Non-Uniform Random Variate Generation, Springer, 1986. John G. Kemeny, G. Laurie Snell, Finite Markov Chains, Springer, 1976. Julia Yeomans, Statistical Mechanics of Phase Transitions, Oxford, 1992. Historical readings: Nicholas Metropolis, The beginning of the Monte Carlo method, Los Alamos Science (Special Issue dedicated to Stanislaw Ulam), 125¿130, 1987. Nicholas Metropolis, Arianna W. Rosenbluth, Marshall N. Rosenbluth, Augusta H. Teller, Edward Teller, Equation of State Calculations by Fast Computing Machines, Journal of Chemical Physics 21, 1087, 1953 Nicholas Metropolis, Stanislaw Ulam, The Monte Carlo Method, Journal of the American Statistical Association (American Statistical Association) 44, 335¿341, 1949.
Module Outline
The main aim of the module is to teach how to write Monte Carlo computer programs for the generation of random numbers, the calculation of integrals and for the analysis of systems. The module will include:
• Introduction to R
• Pseudo-random number generation
• Generation of random variates
• Variance reduction
• Markov-chain Monte Carlo and its foundations
• How to analyse Monte Carlo simulations
• Application to physics: The Ising model
• Application to statistics: Goodness-of-fit tests
Module learning outcomes
Demonstrate comprehensive knowledge of Monte Carlo methods.
Systematically design and create programs for uniform and non-uniform random deviates.
systematically investigate problems by using Markov-chain Monte Carlo simulations.
Critically and comprehensively evaluate the output of Markov-chain Monte Carlo simulations.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 8.00% | |
| Coursework components. Weighted as shown below. | ||
| Portfolio | T2 Week 11 | 100.00% |
| Dissertation (5000 words) | Semester 2 Assessment Week 1 Fri 16:00 | 92.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Spring Semester | Lecture | 2 hours | 11111111111 |
| Spring Semester | Practical | 1 hour | 11111111111 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
Dr Marianna Cerasuolo
Convenor, Assess convenor
/profiles/612334
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