Bayesian Learning

The course introduces basic concepts, ideas and methods of Bayesian inference. It is coordinated with a master's course in the programme 'Statistics and Machine Learning' at LiU, and the focus with be on models and methods in the field of machine learning, but most of the course content is of general statistical interest.
The course will be in English and is given every year in the second part of the spring semester. See the course web page for more details.

Course Data
University: 
Linköping
Offered: 
2017:1
Level: 
Master
PhD
Credits (ECTS): 
6
Type of schedule: 
Regular schedule

Causal inference

 

Course Data
University: 
Umeå
Offered: 
2018:2
Level: 
Master
PhD
Credits (ECTS): 
8
Type of schedule: 
Regular schedule

Ph.D. course in Statistical Inference HT2018-VT2019

Ph.D. course in Statistical Inference (15 hp) for students in
Statistics, Mathematical Statistics, and related areas.

Course Data
University: 
Linné
Offered: 
2018:2
Level: 
PhD
Credits (ECTS): 
15
Type of schedule: 
Travel friendly schedule

Wavelets for Time Series Analysis (May 2018)

Course Code: 3FNA023
ECTS credits: 5
Level Doctoral course
Course contents
The course will cover the following topics:
- an introduction to wavelet analysis
- the Discrete Wavelet Transform (DWT)
- the Maximal Overlap DWT (MODWT)
- the wavelet variance, covariance, correlation and cross correlation

Course Data
University: 
Linné
Offered: 
Not scheduled
Level: 
PhD
Credits (ECTS): 
5
Type of schedule: 
Travel friendly schedule

Approximate Bayesian Computation

Approximate Bayesian Computation (ABC) is an increasingly popular inference paradigm in applications where traditional (Bayesian or frequentist) inference is difficult because the likelihood function is e.g. computationally costly to evaluate or unavailable in closed, tractab- le form. In the course, we will start by briefly studying traditional Bayesian inference and different kinds of simulation-based inference methods in general.

Course Data
University: 
Chalmers
Offered: 
Not scheduled
Level: 
PhD
Credits (ECTS): 
8
Type of schedule: 
Schedule to be decided

Advanced Machine Learning

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.

Course Data
University: 
Linköping
Offered: 
2017:2
Level: 
Master
PhD
Credits (ECTS): 
6
Type of schedule: 
Regular schedule

Econometric analysis 15 hp, winter/spring 2018

Course Data
University: 
Linné
Offered: 
Not scheduled
Level: 
PhD
Credits (ECTS): 
15
Type of schedule: 
Travel friendly schedule

Financial Econometrics

The course offers an introduction to financial econometrics for second-cycle studies. It
covers the main parts of the spectrum of quantitative financial economics, discusses
important results in the empirical finance literature, and provides a comprehensive
knowledge to do empirical work in financial practice.
A student who has taken the course should:
- have a solid knowledge about basic themes in financial econometrics;
- know and be able to use concepts and notation that is frequently used in financial
econometrics;

Course Data
University: 
Uppsala
Offered: 
2016:2
Level: 
Master
PhD
Credits (ECTS): 
8
Type of schedule: 
Regular schedule

Statistical Inference

There will be three 3-days gatherings each comprising 18h of lecturing. The first meeting will take place in Växjö, 12-14 October, the second meeting will be held in Stockholm, 5-7 December and the third one is organized in Uppsala, 8-10 February.

Instructors who so far have agreed to participate are Thomas Holgersson, Björn Holmquist, Hans Nyquist, Dietrich von Rosen, Rolf Sundberg and Silvelyn Zwanzig. There will be another three so each meeting will be run by three instructors. 

Content

Course Data
University: 
Linné
Offered: 
2016:2
Level: 
PhD
Credits (ECTS): 
15
Type of schedule: 
Travel friendly schedule

Advanced Survey Sampling I

Teacher and examiner: Dan Hedlin, Department of Statistics, Stockholm University. .

Topic

We focus on design-based sampling with the Horvitz-Thompson and the generalised regression estimators. (Advanced Survey Sampling II will go further into model assisted estimation). The main course book is Särndal, Swensson and Wretman (1992); for part I we limit ourselves to Chapters 1-7. Although the focus is on design-based sampling we will put this inferential framework into some wider context.

Course Data
University: 
Stockholm
Offered: 
2016:1
Level: 
PhD
Credits (ECTS): 
5
Type of schedule: 
Travel friendly schedule

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