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Applied Bayesian Statistics

Closed
Organizing institution
Utrecht University - Faculty of Social and Behavioural Sciences
Course code
S64
Course fee (no housing)
€ 700.00
Level
PhD
This 5-day course zooms in on the key concepts of Bayesian Statistics and advanced techniques for data-analysis. Topics that are covered include: Bayes’ theorem, Gibbs sampling, the Metropolis-Hastings algorithm, the Bayes factor, the evaluation of informative hypotheses, Bayesian methods for linear regression, moderation, and mediation, and data-analysis in OpenBUGS or R and JAGS.
The course is aimed at researchers who not only work with statistical tools, but are also interested in the development and evaluation of statistical tools.

Bayesian statistics offer flexible techniques for researchers who cannot properly analyze their data using methods based on classical statistics. This course will provide a sound basis in Bayesian statistics for those who want to:

• understand what Bayesian statistics is about;
• use Bayesian statistics to build and evaluate statistical models;
• get hands-on experience with Bayesian statistics in OpenBUGS or R and JAGS.

This 5-day course zooms in on the key concepts of Bayesian Statistics and advanced techniques for data-analysis. Topics that are covered include: Bayes’ theorem, Gibbs sampling, the Metropolis-Hastings algorithm, the Bayes factor, the evaluation of informative hypotheses, Bayesian methods for linear regression, moderation, and mediation, and data-analysis in OpenBUGS or R and JAGS.

The course is aimed at researchers who not only work with statistical tools, but are also interested in the development and evaluation of statistical tools. Among these are psychometricians, sociometricians, epidemiologists and statisticians. The only requirement is familiarity with the following concepts: the likelihood function, the p-value, analysis of variance, and multiple regression.

Note: Participants need to bring a laptop computer to the course, with OpenBugs [http://www.openbugs.net], or alternatively with R ((https://www.r-project.org/) installed and JAGS installed (https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/). This latter option is recommended especially for Mac users.

Download the day-to-day programme (PDF)

Course director

dr. Milica Miočević

Lecturers

Prof. dr. Herbert Hoijtink, dr. Ellen Hamaker, dr. Milica Miočević

Target audience

The course is aimed at researchers who not only work with statistical tools, but are also interested in the development and evaluation of statistical tools. Among these are psychometricians, sociometricians, epidemiologists and statisticians. The only requirement is familiarity with the following concepts: the likelihood function, the p-value, analysis of variance, and multiple regression.

The maximum number of participants for this course is 40. About half of these places are reserved specifically for UU MSc Epidemiology students and IOPS students, the other half for other participants.

Course aim

Introduce the main concepts of Bayesian statistics to researchers who want to use Bayesian techniques in practice.

To provide a sound basis in Bayesian statistics for those who
• Want to understand what Bayesian statistics is about
• Want to use Bayesian statistics to build and evaluate statistical models
• Want to get hands on experience with Bayesian statistics in OpenBUGS or R and JAGS.

Study load

Five days (09.00 – 17.00 hrs.)

Students who want to obtain a grade and credit points (Epidemiology Master Students 1.5 EC, IOPS PhD students 2 EC) for this course need to attend the grading session on Friday. Certificates of attendance are available upon request.

Fee

Course + course materials € 700.00

Tuition fee for PhD candidates from the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by the Graduate School of Social and Behavioural Sciences.

Scholarships

Utrecht Summer School does not offer scholarships for this course.

More information

Irene de Bruijne   ms.graduate.program@uu.nl

Registration

Application deadline: 15 April 2018