Spatial analyses often focus on spatial smoothing using the geostatistical technique known as kriging.  Theoretical results regarding large sample convergence rates of kriging predictors remain elusive.  By casting kriging as a variational problem, we develop an equivalent kernel approximation technique that can also lead to computational feasibility for large data problems.

Workshop on Computational Space-Time 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.

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.

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.

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.

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.