Module MA5634: Stochastic Methods
- Credit weighting (ECTS)
- 5 credits
- Semester/term taught
First Semester 2016-17
- Module Coordinator
- Stefan Sint
- Intended Learning Outcomes
- Students who successfully complete the course should be able to:
Structure and Content
- Employ a range of techniques for generating random numbers according to different distributions.
- Describe and employ basic concepts in probability and statistical inference.
- Implement the jack knife and bootstrap resampling methods to estimate statistical errors.
- Apply variance reduction techniques to stochastic estimates of integration problems.
- Describe and use basic concepts in the theory of Markov processes as well as common simulation algorithms.
- Implement common Markov chain Monte Carlo algorithms to simulate benchmark statistical systems.
- The module covers an introduction to stochastic and statistical methods in computer simulation. After a brief revision of statistics and probability, the course will cover;
- Pseudo-random number generation, including generation according to arbitrary distributions.
- Basic ideas of Monte Carlo integration, including variance reduction techniques such as stratified sampling, antithetic variables, and importance sampling.
- Statistical inference and the statistical analysis of data, in particular the non-parametric jack knife and bootstrap estimation techniques.
- An introduction to Markov Chain Monte Carlo, and its application to problems in physics, mathematics, and chemistry.
- Assessment Detail
- 40% continuously assessed assignments. 60% written examination.