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 course will be given at the Växjö campus, Linnaeus University (Rooms are yet t.b.a.), but some lectures will be streamed live online, through Zoom. There will also be computer classes to ensure a strong connection to empirical econometric modelling and these will be streamed live online, through Zoom.
We will be following the book Econometric analysis: 8th Edition. W. H. Greene closely throughout the course. The exam will consist of a number of home assignment involving theoretical matters as well as empirical analysis.
This will be a PhD course in probability theory taught by Zangin Zeebari during the first half of spring semester. The course will be online (potentially one start-up meeting if the students want to). Please send an email to Kristofer Månsson if you are interested. (kristofer.mansson@ju.se). The syllabus contains information about content and literature etc.
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 will be a PhD course in probability theory taught by Zangin Zeebari. The course wis only online. It will start in mid-November and a schedule is uploaded here. The syllabus contains information about content and litterature etc.
In an era of explosive growth in complex data, our course is your bridge to a deeper understanding of Functional Data Analysis (FDA) and Machine Learning (ML).
The main challenge today isn't data availability but rather our ability to interpret it. Join our growing community of professionals applying ML to tackle complex data challenges.
This crash course highlights key developments at the intersection of FDA and ML, with a focus on handling complex functional data. Moreover, as a student, you have the opportunity to earn 3 ECTS credits by submitting a report at the end of the workshop.
The goal of this summer course is to enhance students' understanding and skills in applied nonparametric econometrics. Initially, the course aims to provide a comprehensive foundation in nonparametric statistical methods. This is important for addressing the disconnect between economics and statistics in this field. This is a summer course that takes place between the 24th and 28th of June at Blekinge tekniska högskola.
