Module Descriptor School of Computer Science and Statistics
|Module Name||MODERN STATISTICAL METHODS II|
|Module Short Title||Simulation methods|
|Contact Hours||Lecture hours: 33|
|Module Personnel||Dr. Bernardo Nipoti|
Students will have ability: to devise suitable simulation methods for generating random numbers from a given probability distribution to use the sampled random numbers in order to estimate quantities of interest or evaluate integrals to assess the quality of the generated sample via diagnostic tools
The course will introduce simulation methods with particular focus on Monte Carlo methods based on Markov Chains. Theoretical justification of basic concepts will be provided together with examples where such methods are applied to problems arising with statistical practice.
Specific topics addressed in this module include:
|Recommended Reading List|
Rubinstein and Kroese, “Simulation and the Monte Carlo method”, 2nd edition, Wiley 2008.
Devroye, “Non-uniform random variate generation”, Springer 1986. (the book is freely available on the author’s webpage)
Robert & Casella, “Monte Carlo statistical methods”, Springer 2004.
Additional material might be provided during the course.
|Module Prerequisites||Familiarity with basic concepts in probability and statistics.|
Exam (100%), 2 hours of time
|Academic Year of Data||2016/17|