four 30 minute presentations from our PhD canidates!

The Department and the Faculty wish to see that when we give a course, for both Master's and PhD level candidates, then there ought to be a "visible difference" in the ways the exams are designed. One solution is to give 4 exercises to the Master students but 44 such for the PhD students, for the exam project in December. For this occasion I choose another solution, however, for our *four PhD candidates*, namely that they give us *30 minute talks*, on topics touching their own research work, and sufficiently close to our course topics, as part of the regular teaching. 

This is our schedule, so far, and soon we will have *titles and short abstracts* for all of these:

Tue Oct 29: Dennis Christensen: Applications of Bayesian linear regression in sensitivity analysis of energetic materials

Tue Nov 12: Maria Nareklishvili: Instrumental Variable Regression and its applications, Bayesian approach

Tue Nov 19: Leiv Tore Salte Rønneberg, William Denault

** Title & summary for Dennis: 
Applications of Bayesian linear regression in sensitivity analysis of energetic materials
 
Conducting experiments on the sensitivity of energetic materials is usually both expensive and time-consuming, and so sensitivity analysis is based on relatively few data points. In such applications, it is therefore paramount to account for model complexity in order to avoid over-fitting. In this talk, I shall follow the methods outlined in Chapter 3 of [1] , which illustrate how the Bayesian framework accounts for model complexity by construction. I will show how (parametric) Bayesian regression can be applied to the analysis of sensitivity of energetic materials, and discuss (with the help of the audience) how this approach may be lifted to a non-parametric setting.
1.      Bishop CM (2006) Pattern recognition and machine learning. Springer, Cambridge