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.

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.

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;

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

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.

We are happy to invite to the course on Longitudinal Data Analysis (3hp). The course is funded by GRAPES  but all interested in Longitudinal Data Analysis can participate. The course will present material on Repeated Measurements, Mixed Linear Models, Generalized Linear Models, Model Validation and Data Analysis. All together the course comprises 12h lectures and 12h computer classes.

GRAPES will refund travel and accomodation costs for PhD students at Swedish universities.

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.

This masters course introduces basic concepts, ideas and methods of Bayesian inference. It is given in the late fall at Stockholm University as part of the masters programme and in the late spring at Linköping University (then under the name Bayesian Learning) as part of the master's course in the programme 'Statistics and Machine Learning'. The focus is 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.