Optimal Experimental Design

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.

Course Data
University: 
Linköping
Offered: 
2021:1
Level: 
PhD
Credits (ECTS): 
3
Type of schedule: 
Travel friendly schedule

Optimisation algorithms in Statistics II, 3.5 credits

Based on the topics discussed in first part of the course, we continue with deepening the theoretical basis for stochastic optimisation algorithms. Specifically, we discuss theory around Stochastic Gradient Ascent (including momentum and adaptive step sizes), Simulated Annealing, and Particle Swarm Optimisation. Theoretical results on convergence and speed will be discussed.

See http://gauss.stat.su.se/phd/oasi/optimisation2.html for more information.

Course Data
University: 
Stockholm
Offered: 
2021:1
Level: 
PhD
Credits (ECTS): 
4
Type of schedule: 
Travel friendly schedule

Causal inference

This course will be run on-line via zoom, except for the final exam which is onsite (but local examination might be arranged for PhD students at other universities).

Course Data
University: 
Umeå
Offered: 
2020:2
Level: 
Master
PhD
Credits (ECTS): 
8
Type of schedule: 
Travel friendly schedule

Introduction to Structural Equation Models (7.5 ECTS)

Upon completion of the course, the doctoral student will be able to: explain the basic theoretical foundation and practical use of Structural Equation Models (SEM), apply SEM on real problems, interpret and present the results, estimate structural equation models with maximum likelihood and least squares and be able to evaluate the results.

Course Data
University: 
Dalarna
Offered: 
2020:2
Level: 
PhD
Credits (ECTS): 
8
Type of schedule: 
Schedule to be decided

Statistical Inference I (7.5 hp)

A Ph.D. course in modern statistical inference theory for students in statistics, mathematical statistics, and related areas. Two onsite meetings (Lund and Stockholm) and three online meetings. The details about the course can be found at https://krys.neocities.org/Teaching/StatInf/PhD_stat_inf.html.

Course Data
University: 
Lund
Offered: 
2020:2
Level: 
PhD
Credits (ECTS): 
8
Type of schedule: 
Travel friendly schedule

Optimisation algorithms in Statistics I, 4 credits

We will discuss algorithms to compute minima or maxima which are frequently needed in statistics and machine learning. Topics of lectures on four occasions: gradient based algorithms, stochastic gradient based algorithms, gradient free algorithms (e.g. particle swarm optimisation), handling of restrictions during optimisation. Course homepage: http://www.adoptdesign.de/optimisation1.html

Course Data
University: 
Stockholm
Offered: 
2020:2
Level: 
PhD
Credits (ECTS): 
4
Type of schedule: 
Travel friendly schedule

Dependence modelling using vine copulas: theory and applications

All lectures are online via Zoom. The lecturer is Prof. Dr. Claudia Czado (https://www.professoren.tum.de/en/czado-claudia/) who also wrote the course book. Info regarding the course is available in the attached course plan. The course will take place in the last week of august (detailed schedule is attached). Any questions contact Kristofer Månsson (kristofer.mansson@ju.se).

 

Course Data
University: 
Jönköping
Offered: 
2020:2
Level: 
PhD
Credits (ECTS): 
5
Type of schedule: 
Travel friendly schedule

Large sample theory

Due to the outbreak of the coronavirus all lectures will be online via Zoom.The lecturer is Dr. Abdul Aziz Ali. Info regarding the course is available in the attached course plan. The course will start in the midle of may. Any questions contact Kristofer Månsson (kristofer.mansson@ju.se).

Course Data
University: 
Jönköping
Offered: 
2020:1
Level: 
PhD
Credits (ECTS): 
8
Type of schedule: 
Travel friendly schedule

Stochastic differential equations with R school

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/ ).

Course Data
University: 
Linköping
Offered: 
2020:1
Level: 
Master
PhD
Credits (ECTS): 
0
Type of schedule: 
Travel friendly schedule

Advanced Bayesian Learning, 7.5 credits

This is an advanced course in Bayesian statistics for PhD students in statistics, computer science, the engineering sciences and other related fields.
The course is divided into 3-5 contemporary topics in Bayesian analysis, and the choice of topics can vary from year to year depending on the research frontier.
The planned topics for the current year are (preliminary and subject to change):

1. Gaussian Processes with Applications
2. Bayesian Nonparametrics
3. (Stochastic) Variational Inference
4. Bayesian Model Inference

Course Data
University: 
Stockholm
Offered: 
2020:1
Level: 
PhD
Credits (ECTS): 
8
Type of schedule: 
Travel friendly schedule

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