Gaussian Processes for Global Optimization

Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many deep…

The optimization of expensive-to-evaluate functions generally relies on metamodel-based exploration strategies. Many deterministic global optimization algorithms used in the field of computer experiments are based on Kriging (Gaussian process…

A method for the construction of approximate analytical expressions for the stationary marginal densities of general stochastic search processes is proposed. By the marginal densities, regions of the search space that with high probability contain…

Summary form only given, as follows. The simulated annealing method is a tool for finding the global minima of a performance measure function. It is accomplished by constraining the probability distribution of the process to be a Gibbs distribution…

ABSTRACT In order to confirm and refine the current classification scheme of Xanthomonas translucens and to identify novel strains from ornamental asparagus, a collection of field and reference strains was analyzed. Rep-polymerase chain reaction…

The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior distribution over functions. In this paper we investigate the use of Gaussian process priors over functions, which permit the…

Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many deep…

In this article, we extend the application of the Gaussian processes technique to classification quantitative structure-activity relationship modeling problems. We explore two approaches, an intrinsic Gaussian processes classification technique and…

We present Gaussian processes (GPs) for digital communications. GPs can be used to construct analytical nonlinear regression functions, which can be suitable for digital communications in which linear solutions under perform. GPs can be cast as…

Discriminative methods for visual object category recognition are typically non-probabilistic, predicting class labels but not directly providing an estimate of uncertainty. Gaussian Processes (GPs) provide a framework for deriving regression…