Computational Statistics

Course description

Content

• Maximum-likelihood and numerical optimization
• The EM-algorithm
• Stochastic optimization algorithms
• Simulation algorithms and Monte Carlo methods
• Nonparametric density estimation
• Bivariate smoothing
• Numerical linear algebra in statistics. Sparse and structured matrices
• Practical implementation of statistical computations and algorithms
• R/C++ and RStudio statistical software development

Recommended academic qualifications

StatMet and MStat (alternatively MatStat from previous years) or similar knowledge of statistics and some experience with R usage. Linear algebra, multivariate distributions, likelihood and least squares methods are essential prerequisites. It is a good idea to have a working knowledge of conditional distributions as treated in Statistics A.

Academic qualifications equivalent to a BSc degree is recommended.

This course requires a certain statistical maturity at the level of MSc students in statistics. It is not an introduction to R for statistical data analysis.