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/ ).
The course covers some advanced models in machine learning.
The models are analyzed mainly from a Bayesian perspective.
The course is also a master's course on the LiU programme 'Statistics and Machine Learning'.
The course is organized into four topics:
- Graphical Models
- Hidden Markov Models
- Gaussian Process Regression and Classification
- State-space models
Each topic includes lectures, a computer lab and a follow-up seminar.
In conjunction with the LinStat 2014 a mini-course (8h) entitled 'Survey sampling and linear models' will be held by professor Stephen Haslett, Massey University, New Zealand and professor Simo Puntanen, University of Tampere, Finland, August 23-24.
The course is sponsored by GRAPES and GRAPES will refund travel and accommodation costs for PhD students at Swedish universities.