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Curricular information is subject to change
Knowledge and understanding of:
- Various random numbers generation methods (for both uniform and non-uniform random variables)
- Monte Carlo methods (Rejection method, Importance Sampling)
- Various Markov Chain Monte Carlo methods (Gibbs sampler, Metropolis-Hastings, diagnostic techniques)
- Advanced topics in Monte Carlo methods (Control Variates, Antithetic Variates, Simulated Annealing, Monte Carlo Hp tests)
Both theoretical and practical aspects of the different methods will be illustrated. Implementation of the methods discussed will be explored using the R programming language.
Student Effort Type | Hours |
---|---|
Specified Learning Activities | 24 |
Autonomous Student Learning | 72 |
Online Learning | 24 |
Total | 120 |
- Basic understanding of probability. For more details on the required level, see for example the content and learning outcomes of STAT20110 - Introduction to Probability;
- Familiarity with common statistical distributions e.g. Gaussian, Gamma, Beta, Exponential, etc. ;
- Basic knowledge of function optimization (mathematical optimization);
- Basic understanding of statistical inference and hypothesis testing. For more details on the required level, see for example the content and learning outcomes of STAT20100 - Inferential Statistics.
- Basic knowledge of Stochastic Processes;
- Basic knowledge of Bayesian Inference;
- Basic knowledge of R programming language.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Continuous Assessment: Online assessments | Throughout the Trimester | n/a | Standard conversion grade scale 40% | No | 40 |
Examination: End of trimester written exam | 2 hour End of Trimester Exam | No | Standard conversion grade scale 40% | No | 60 |
Resit In | Terminal Exam |
---|---|
Spring | Yes - 2 Hour |
• Group/class feedback, post-assessment
Not yet recorded.