Recent studies have examined the effectiveness of using probabilistic models to guide the sample generation process for searching high dimensional spaces. Although the simplest models, which do not account for parameter interdependencies, often perform well on many problems, they may perform poorly when used on problems that have a high degree of interdependence between parameters. More complex...

The theory of sortability of partition property was introduced to prove the existence of an optimal partition satisfying the property for optimal partition problems over single-parameter space, and then extended to multi-dimensional parameter spaces. For each partition property of interest, almost all levels of sortabilities were obtained; however, the part-specific-sortabilities are hard to be...

In this paper we give examples which show that the convolution theorem (Boll, [1], Hajek, [2]) cannot be extended to infinite-dimensional shift experiments. This answers a question posed by van der Vaart, [9], and which has been considered also by LeCam, [5].

We use Earle’s generalization of Montel’s theorem to obtain some results on holomorphic motions over infinite dimensional parameter spaces. We also study some properties of group-equivariant extensions of holomorphic motions.

Our ongoing research in performance theory integrates methods for complex instrument parameter spaces and models for musical gestures. The latter are modelled as parametric curves residing in high-dimensional symbolic and physical gesture spaces. This article shortly exposes basic concepts of those spaces and the construction of symbolic gesture curves. It then discusses the problem of fitting ...

A method is presented to exploit adaptive integration algorithms using importance sampling, like VEGAS, for the task of scanning theoretical predictions depending on a multi-dimensional parameter space. Usually, a parameter scan is performed with emphasis on certain features of a theoretical prediction. Adaptive integration algorithms are wellsuited to perform this task very efficiently. Predic...