We propose a new model for correlated outputs of mixed type, such as continuous and binary outputs, with a particular focus on joint regression and classification, motivated by an application in constrained optimization for computer simulation modeling. Our framework is based upon multivariate stochastic processes, extending Gaussian process methodology for modeling of continuous multivariate s...

The basic Voronoi concept involves tessellating an m-dimensional space with respect to a finite set of objects by assigning all locations in the space to the closest member of the object set. This concept can be operationalised in a variety of ways by considering different methods for determining “closeness”, subsets of objects rather than individual objects as generators, moving objects, and d...

Many different algorithms can be used to optimize spatial network designs. For spatial interpolation of environmental variables in routine and emergency situations, computation time and interpolation accuracy are important criteria. The objective of this work is to compare the performance of different optimization algorithms for both criteria. Both adding to and deleting measurements from an ex...

We consider multi-period portfolio selection problems for a decision maker with a specified utility function when the variance of security returns is described by a discrete time stochastic model. The solution of these problems involves a dynamic programming formulation and backward induction. We present a simulation-based method to solve these problems adopting an approach which replaces the p...

E.G. Beek, 1991. Spatial interpolation of daily meteorological data; theoretical evaluation of available techniques. Wageningen (The Netherlands), DLO The Winand Staring Centre. Report 53.1.44 pp.; 13 Figs; 1 Table; 20 Refs. In agromcteorological crop yield models meteorological values at not observed points have to be obtained by means of interpolation techniques. In this study, interpolation ...

We introduce a method to learn a hierarchy of successively more abstract representations of complex data based on optimizing an information-theoretic objective. Intuitively, the optimization searches for a set of latent factors that best explain the correlations in the data as measured by multivariate mutual information. The method is unsupervised, requires no model assumptions, and scales line...