| CODE | SOR3500 | ||||||||||||
| TITLE | Computational Methods in Statistics and Operations Research | ||||||||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||||||||
| MQF LEVEL | 6 | ||||||||||||
| ECTS CREDITS | 4 | ||||||||||||
| DEPARTMENT | Statistics and Operations Research | ||||||||||||
| DESCRIPTION | (1) Pseudorandom number generators and generating values from a distribution (2) Generating paths of a stochastic process (3) Monte Carlo integration and importance sampling (4) Variance reduction techniques (5) Markov Chain Monte Carlo Methods - Metropolis Hastings Algorithm - Gibbs Sampling - Bayesian Applications (6) Simulation-based Optimisation (7) Quasi Monte Carlo (8) Resampling Methods (9) The EM Algorithm Study-unit Aims: To give the students the computational skills required in understanding, formulating and programming algorithms necessary in many areas of statistics and operations research. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study unit, the student will be able to have a structured general overview of computational methods in use in Statistics and OR. Most of the focus will be on Monte Carlo techniques. 2. Skills: By the end of the study unit, the student will be able to implement these methods to complex problems in Statistics and Operations Research and, in the process, improve his programming skills in important software such as R and Matlab. Main Text/s and any supplementary readings: Asmussen, S and Glynn, P. (2007). Stochastic Simulation: Algorithms and Analysis. Springer. Brooks, S., Gelman, A., Jones, G.L. and Meng, X.L. (2011). Handbook of Markov Chain Monte Carlo. Chapman and Hall. Carlin, B. P. and Louis, T. A. (2000). Bayes and Empirical Bayes Methods for Data Analysis. Chapman and Hall. Gamerman, D. and Lopes, H. F. (2006). Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference. Chapman and Hall. Gentle, J.E. (2004) Random Number Generation and Monte Carlo Methods, Springer. Gilks, W. R., Richardson, S. and Spiegelhalter, D.J. (1996). Markov Chain Monte Carlo in Practice. Springer-Science+Business Media. Lemieux, C. (2009) Monte Carlo and Quasi-Monte Carlo Sampling. Springer. Ripley, B. (2006) Stochastic Simulation. Wiley-Interscience. Rubinstein, R. Y. and Kroese, D. P. (2008) Simulation and the Monte Carlo Method. Wiley. Shao, J. and Dongsheng, T. (1996) The Jackknife and Bootstrap. Springer. |
||||||||||||
| ADDITIONAL NOTES | Pre-requisite Study-units: SOR1110, SOR1222, SOR2221 and SOR2250 | ||||||||||||
| STUDY-UNIT TYPE | Lecture and Practical | ||||||||||||
| METHOD OF ASSESSMENT |
|
||||||||||||
| LECTURER/S | Derya Karagoz Monique Sciortino David Paul Suda |
||||||||||||
|
The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2025/6. It may be subject to change in subsequent years. |
|||||||||||||