### NTNU, Trondheim, Norway

**For proposals of bachelor/project/master-thesis topics please see**

**here**#### Current PhD students

- Ingeborg Gullikstad Hem (08/2017 - ) Ingeborg has joined the project "Penalised Complexity-priors: A new tool to define priors and robustify Bayesian models.
- Alexander Knight (08/2015 - present, currently on 20% work load) working on multilevel models and the development of new default priors based on penalised complexity priors.

#### Completed theses

##### PhD theses

- Jingyi Guo (08/2013-08/2016),

*Bayesian Meta-analysis*. Defended on: 06.12.2016.

##### Master theses

- Maxime Conjard:

*Towards joint disease mapping using penalised complexity priors*(08/2015-07/2016)

- John Darkwah:

*Bayesian inference for disease mapping: Comparing INLA and MCMC*(08/2015-07/2016)

- James Korley Attuquaye:

*The impact of varying time scales on the quality of cancer projections based on the Bayesian age-period-cohort model*(08/2014-07/2015)

##### Bachelor theses

- Guro Aglen:

*Hidden Markov models*(01/2016-08/2016)

#### Full lectures:

- 01/2016-07/2016: TMA4300 Computer intensive statistical methods

- 08/2015-12/2015: TMA4265 Stochastic processes

- 01/2015-07/2015: TMA4300 Computer intensive statistical methods

- 08/2014-12/2014: TMA4265 Stochastic processes

- 01/2014-07/2014: TMA4300 Computer intensive statistical methods

- 08/2013-12/2013: TMA4265 Stochastic processes

### University of Zurich, Switzerland

#### Full lecture:

- 02/2012-06/2012: Bayesian inference (for M.Sc. in Biostatistics)

#### Exercise classes:

- 09/2010-12/2010: Bayesian inference (for M.Sc. in Statistics)

- 09/2009–12/2009: Biostatistics (for M.Sc. in Medical Biology)

#### Other:

- 01/2009–08/2009: Development of Java-applets to visualise statistical concepts

(see http://www.biostat.uzh.ch/static/applets/)

### INLA-courses

- 2 day course (jointly with Haakon Bakka)
**University of Zurich, Switzerland,**May 12-13, 2016. Course descriptionCourse material: See here

References for computing the continuous ranked probabiity score (CRPS): The definition is provided in the paper by Gneiting and Raftery, equation 20: Gneiting and Raftery (2007), JASA, Volume 12, Number 102 If the predictive distribution can be assumed to be normal (which often applies approximately) a simplified formula, which is given directly below equation 20, can be used. Here, only the mean and standard deviation of the predictive distribution are needed which are available from INLA.

Otherwise, the CRPS can be computed using a Monte Carlo approach based on samples (see inla.posterior sample example) from the predictive distribution see Equation 37 Sigrist et al. (2014), arXiv:1204.6118 - 3 hour course,
**Universiy of Toronto, Canada**, March 27, 2016 - 2 day course,
**Universidad Publica de Navarra, Pamplona, Spain**, December 1-2, 2015 - 3 hour pre-conference course,
**IBS channel network conference 2015**,**Nijmegen, Netherlands**, April 20, 2015Course material: see here - 1.5 day course,
**University of Oslo, Norway**, November 5-6, 201Course material: see here