2014 Winter

Date: 2014-01-24 Time: 15:30-16:30 Location: Salle 1355, pavillon André-Aisenstadt (CRM) Abstract: Statistical model calibration of computer models is commonly done in a wide variety of scientific endeavours. In the end, this exercise amounts to solving an inverse problem and a form of regression. Gaussian process model are very convenient in this setting as non-parametric regression estimators and provide sensible inference properties. However, when the data structures are large, fitting the model becomes difficult.

Date: 2014-01-10 Time: 15:30-16:30 Location: BURN 1205 Abstract: In a seminal article in 1944, Itô introduced the stochastic integral with respect to the Brownian motion, which turned out to be one of the most fruitful ideas in mathematics in the 20th century. This lead to the development of stochastic analysis, a field which includes the study of stochastic partial differential equations (SPDEs). One of the approaches for the study of SPDEs was initiated by Walsh (1986) and relies on the concept of random-field solution for equations perturbed by a space-time white noise (or Brownian sheet).