Collective Estimation of Multiple Bivariate Density Functions with Application to Angular-sampling-based Protein Structure Prediction Mehdi Moodaaliat, Assistant Professor, Marquette University Mar 10, 15:00 - 16:00 B1 statistics In this talk we develop a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs. Each log density function in the collection is modeled as a linear combination of a common set of basis functions. The shared basis functions are modeled as bivariate splines on triangulations and are estimated using data. The circular nature of angular data is taken into account by imposing appropriate smoothness constraints across boundaries of the triangles.
Bayesian Regression Trees, Nonparametric Heteroscedastic Regression Modeling and MCMC Sampling Matthew Pratola, Assistant Professor of Statistics, The Ohio State University Nov 24, 15:00 - 16:00 B1 L2 nonparametric statistics Bayesian Statistics In this talk, we introduce a new Bayesian regression tree model that allows for possible heteroscedasticity in the variance model and devise novel MCMC samplers that appear to adequately explore the posterior tree space of this model.
Uncertainty Quantification of Tsunami Models Serge Guillas, Professor of Statistics, University College London (UCL) Sep 8, 15:00 - 16:00 B1 uncertainty quantification Environmental Statistics In this talk, we first show various strategies for the efficient emulation of simulators having uncertain inputs, with applications to tsunami wave modelling. A fast surrogate of the simulator's time series of outputs is provided by the outer product emulator.
Parametric Problems, Stochastic, and Identification By Prof. Hermann Matthies (ISCTUB, Germany) Prof. Hermann Matthies, Institute of Scientific Computing TU Braunschweig, Geramany Mar 6, 15:00 - 16:00 B1 R4102 Parameter identification problems are formulated in a probabilistic language, where the randomness reflects the uncertainty about the knowledge of the true values. This setting allows conceptually easily incorporating new information, e. g. through a measurement, by connecting it to Bayes's theorem. The unknown quantity is modelled as a (may be high-dimensional) random variable. Such a description has two constituents, the measurable function and the measure.
Scalable Hierarchical Algorithms for eXtreme Computing Workshop David Keyes, Senior Associate to the President, King Abdullah University of Science and Technology Apr 28, 08:00 - Apr 30, 16:00 KAUST scientific computing The 2012 SHAX-C workshop focuses international expert attention on the prospects for the three great hierarchical algorithms of scientific computing: multigrid, fast transforms, and fast multipole methods. These methods are kernels in simulations based on formulations of partial differential equations, integral equations, and interacting particles – in short, they are scientific and engineering workhorses.
