| Instructors | Geert Molenberghs (Coordination), Francis Tuerlinckx, Katrijn van Deun & Tom Wilderjans |
| geert.molenberghs@kuleuven.be | |
| Venue | KU University of Leuven |
| Mandatory/elective | Elective |
| ECTS | 2 |
| Abstract | Numerical problems are frequently encountered by statisticians. Prominently, the estimation of the parameters of a statistical model requires the solution of an optimization problem. In a few simple cases, closed-form solutions exist but for many probability models the optimal parameter estimates have to be determined by means of an iterative algorithm. The goal of this course is threefold. First, we want to offer the readers an overview of some frequently used optimization algorithms in (applied) statistics. Second, we want to provide a framework for understanding the connections among several optimization algorithms as well as between optimization and aspects of statistical inference. Third, although very common, optimization is not the only numerical problem and therefore some important related topics such as numerical differentiation and integration will be covered. |
| Examination | Participation |
| Registration | IOPS members: secretariaat.iops@rug.nl Affiliated members/others: secretariaat.iops@rug.nl + KU Leuven Choose option ‘Non profit/social sector’ when registering |
| Costs | IOPS members: free of charge Affiliated members/others: €500 |
IOPS
