This is a 6HP PhD course that will be hosted at the Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University. Preliminary schedule is estimated to be week 16, 13th-15th April 2026. A few days of lectures will be at Linköping week 16, and examination will be online around week 23.
The goal of the course is to serve as an introduction to the clustering task. It is aimed for students that have an applied or engineering background, e.g., machine learning, signal processing, statistics, computer vision, and control.
At the Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, 17th-20th March 2026, we will be hosting a workshop concerning stochastic differential equations and the YUIMA R package (Simulation and Inference for SDEs and Other Stochastic Processes, https://cran.r-project.org/web/packages/yuima/index.html ). The lectures will be given by members of the YUIMA team.
This is a 7.5HP course for Ph.D. students in Statistics. It is planned as a traditional on campus blackboard lecture and exercise session course. The schedule is to be decided yet. It will be blocked into two intensive ca week long blocks in weeks 6 and 11. Please email me directly, krzysztof_dot_bartoszek_at_liu_dot_se if you are interested in participating. There is a course webpage: https://www.ida.liu.se/~krzba67/include/SP_2026.html , where updates will appear.
The aim of this course is to provide an introduction to the theory and application of sequential Monte Carlo (SMC) methods. To this end we will start by studying the use of SMC for inference in nonlinear dynamical systems. It will be shown how SMC can be used to solve challenging parameter (system identification) and state inference problems in nonlinear dynamical systems.
Submitted by Frank Miller on November 18, 2024 - 10:26
To register for the course, please send an email to Frank Miller (frank.miller@liu.se) until January 23 at the latest.
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.
This is a 7.5HP course for Ph.D. students in Statistics. It is planned as a traditional on campus blackboard lecture and exercise session course. The schedule is to be decided yet, but tentatively it will be blocked into two intensive ca week long blocks. Please email me directly, krzysztof_dot_bartoszek_at_liu_dot_se if you are interested in participating.
Submitted by Frank Miller on December 20, 2022 - 15:02
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. The course will start on March 16; course homepage (with full schedule): http://www.adoptdesign.de/frankmillereu/adcompstat2023.html.
At the Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, 21st-24th March 2023 we will be hosting a school concerning stochastic differential equations and the YUIMA R package (Simulation and Inference for SDEs and Other Stochastic Processes, https://cran.r-project.org/web/packages/yuima/index.html ) . The lectures will be given by members of the YUIMA team. Below is a nearly final program of the school. The dates are fixed.
Submitted by jolanta.pielasz... on February 6, 2021 - 09:58
Course plan
The course is about optimal experimental design – planning of experiments. The basic idea of design optimization (best estimation of unknown model parameters, information or moment matrix, etc.) and commonly used design criteria in linear models are the main parts of the course. Besides optimal designs in classical linear models, optimal designs for estimation and prediction of fixed and random effects in particular mixed models will be discussed.
At the Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, 14th-19th May 2020 we will be hosting a school concerning stochastic differential equations and the YUIMA R package (Simulation and Inference for SDEs and Other Stochastic Processes, https://cran.r-project.org/web/packages/yuima/index.html). The lectures will be given by members of the YUIMA team ( https://yuimaproject.com/ ).
