Since the advent of evolution strategies many attempts have been done to improve the mutation operator [7]. The first enhancement could be seen in the introduction of a flexible step size. As a logical consequence later n step sizes, one per variable, were used. Highest flexibility is achieved with the correlated mutation but also quadratic growth of the number of strategy parameters [8]. A new...

In order to reduce the risk of complete failure, research managers often adopt a parallel strategy by simultaneously funding several R&D activities and several research teams within each activity. The parallel strategy requires the allocation of an available budget to a number of R&D activities, the determination of the number of research teams within each activity and the amount of funding the...

The Besley-Coate model of representative democracy has the problem of multiple equilibria (Besley-Coate, 1997). We show that requiring the Besley-Coate political equilibria to be iteratively undominated at the voting stage re nes the set of (pure strategy) political equilibrium outcomes only for those cases where at least four candidates stand for election. This note complements the results of ...

A high-level dialogue system involves symbolic reasoning and deep understanding of user input, and its object is to handle complex dialogue phenomena such as inference and negotiation. However, it is difficult for high-level dialogue techniques to be extended toward a practical dialogue system that must generate effective responses in a real environment because the high-level dialogue technique...

We introduce an efficient MCMC sampling scheme to perform Bayesian inference in the M/G/1 queueing model given only observations of interdeparture times. Our MCMC scheme uses a combination of Gibbs sampling and simple Metropolis updates together with three novel “shift” and “scale” updates. We show that our novel updates improve the speed of sampling considerably, by factors of about 60 to abou...

Large-scale Gaussian process inference has long faced practical challenges due to time and space complexity that is superlinear in dataset size. While sparse variational Gaussian process models are capable of learning from large-scale data, standard strategies for sparsifying the model can prevent the approximation of complex functions. In this work, we propose a novel variational Gaussian proc...

This paper studies set estimation and inference of models characterized by conditional moment inequalities. A consistent set estimator and a con…dence set are provided in this paper. Previous studies on set estimation and inference with conditional moment inequalities typically use only a …nite set of moment inequalities implied by the conditional moment inequalities. We consider an alternative...

Bayesian network is a compact representation for probabilistic models and inference. They have been used successfully for multisensor fusion and situation assessment. It is well known that, in general, the inference algorithms to compute the exact posterior probability of the target state are either computationally infeasible for dense networks or impossible for mixed discretecontinuous network...