Course Homepage for Advanced Computational Statistics (PhD Course 2023; 7.5 HEC)Summary
Statistics depends heavily on computational methods. Optimisation methods are used in statistics for example for maximum likelihood estimates, optimal experimental designs, risk minimization in decision theoretic models. In these cases, solutions of optimisation problems usually do not have a closed form but need to be computed numerically with an algorithm. Another big demand on computational methods is when statistical distributions are simulated and integrated and statistics of these distributions have to be determined in an efficient way.
This course focuses on computational methods for optimisation, simulation and integration needed in statistics. The optimisation part discusses gradient based, stochastic gradient based, and gradient free methods. Further, constrained optimisation will be a course topic. We will discuss techniques to simulate efficiently for solving statistical problems.
We will use implementation with the programming language R. Examples from machine learning and optimal design will illustrate the methods.
Most welcome to the course!
Accepted to a doctoral program in Sweden in Statistics or a related field (e.g. Mathematical Statistics, Engineering Science, Quantitative Finance, Computer Science). Knowledge about Statistical Inference (e.g. from the Master's level) and familiarity with a programming language (e.g. with R) is required.
Note that the content of the optimisation part of the course will have considerable overlap with the Ph.D. courses Optimisation algorithms in Statistics I/II which I gave in 2020/2021, i.e. the current course is not intended for the participants of the former courses.Content
The course contains fundamental principles of computational statistics. Focus is on:
On completion of the course, the student is expected to be able to:
The intended learning outcomes will be graded by several individual home assignments. The grades given: Pass or Fail.Course Literature
Lectures and some problem sessions. The teaching is conducted in English. Course participants will spend most of their study time by solving the problem sets for each topic on their own computers without supervision. The course will be held in March, April, and May 2023.
To register for the course, please send an email to me (frank.miller at liu.se) until February 20, 2023. You are also welcome for any questions related to the course.