Advanced Bayesian Learning

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 five contemporary topics in Bayesian analysis, and the choice of topics can vary from year to year depending on the research frontier.
The topics for the current year are (teacher in parenthesis):

1. Gaussian Processes (Mattias Villani, Statistics LiU)
2. Bayesian Networks (Jose M. Pena, Database and Information Techniques, LiU)
Course Data
University: 
Linköping
Offered: 
2014:1
Level: 
PhD
Credits (ECTS): 
6.0
Type of schedule: 
Travel friendly schedule

Longitudinal Data Analysis

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.

Course Data
University: 
Stockholm
Offered: 
2014:1
Level: 
PhD
Credits (ECTS): 
3.0
Type of schedule: 
Travel friendly schedule

Survey sampling and linear models

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.
Course Data
University: 
Linköping
Offered: 
2014:2
Level: 
PhD
Credits (ECTS): 
2.0
Type of schedule: 
Travel friendly schedule

Bayesian Learning

The course introduces basic concepts, ideas and methods of Bayesian inference. It is coordinated with a second-year master's course in the programme 'Statistics and Data Mining', and the focus with be on models and methods in the field of data mining and machine learning, but most of the course content is of general statistical interest.
Course Data
University: 
Linköping
Offered: 
2013:2
Level: 
Master
Level: 
PhD
Credits (ECTS): 
6.0
Type of schedule: 
Travel friendly schedule

The Swedish Research Student Conference in Statistics, 2013

The Swedish Research Student Conference in Statistics will be held on 18-19 April 2013 in Stockholm and is now open for registration.

Call for presentations and discussions

We welcome everyone to participate the meeting however the number of speakers and and discussants are limited due to time limitation. So the priority will give to

Causal inference in register studies

In this course we introduce causal inference with main focus on two approaches; i) the potential outcome framework/ Rubin Causal Model and ii) Causal inference with graphical models.
Identification of causal effects in the two approaches is studied. Non and semi-parametric estimators of causal effects commonly associated with the potential outcome framework will be presented. A general  introduction to graphical models is also given.

Weeks 45-03, 50% tempo http://www.usbe.umu.se/english/dept/stat/education/courses/course/?code=...



Course Data
University: 
Umeå
Offered: 
2012:2
Level: 
Master
Level: 
PhD
Credits (ECTS): 
7.5
Type of schedule: 
Regular schedule

Asymptotic Theory, Uppsala 2012

Starting November 5, I will give a course in asymptotic theory at the PhD level in Statistics. The course book is Ferguson, T.S. A Course in Large Sample Theory, Chapman and Hall 1996. Lectures are at 10.15-14 on Mondays, ending December 17. Examination is through take home exercises. For further information, see the attached schedule.

Course Data
University: 
Uppsala
Offered: 
2012:2
Level: 
PhD
Credits (ECTS): 
7.5
Type of schedule: 
Regular schedule

Spatial Statistics (Winter Conference)

Course content

Introduction to spatial data analysis, representing space, interfacing geographical information systems, space-time data, spatial statistics overview (point patterns, geostatistics, areal data).
Use of GIS and spatial statistics for radioecological modeling and mapping using Chernobyl- and Fukushima-related data, focusing on public health protection.
Course Data
University: 
Örebro
Offered: 
2012:1
Level: 
PhD
Credits (ECTS): 
3.0
Type of schedule: 
Travel friendly schedule

Analysis of High-Dimensional Data

Jönköping International Business School, February 16-17, 2012

Welcome to a workshop on high-dimensional analysis, in particular if you are a research student or are supervising some with an interest in high-dimensional problems. The main idea is that we should learn who is doing what in this very interesting area. We interpret high-dimensional analysis in a very broad sense.

Advanced Probability and Inference

Content

  • Mathematical background (limits, series, order relations and rates of convergence, continuity, sets)
  • Measure theoretic foundations of probability (probability triplets, random variables, independence, expected values, change of variable)
  • Stochastic convergence (almost sure convergence, convergence in probability, convergence in distribution, laws of large numbers, central limit theorems, non-iid stochastic variables)
  • Conditional probability and expectation
  • Statistical tests (size and critical values, power, efficiency, asy
Course Data
University: 
Örebro
Offered: 
2012:1
Level: 
PhD
Credits (ECTS): 
12.0
Type of schedule: 
Travel friendly schedule
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