杏吧论坛

Dr Yining Chen

This course is available on the MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (Research) and MSc in Statistics (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.

An introduction to the use of numerical linear algebra and optimisation in statistical computation, followed by their applications in parametric statistical methods, including least squares, maximum likelihood, generalized linear modelling, LASSO, etc. We then present selected topics in computational methods in nonparametric statistics, including kernel density estimation and splines. If time permits, more advanced topics such as EM and simulated annealing will also be covered. Throughout the course, students will gain practical experience of implementing these computational methods in a programming language. Learning support will be provided for at least one programming language, such as C++ or Python, but the choice of language supported may vary between years, depending on judged benefits to students, whether in terms of pedagogy or  resulting skills.