this paper we present a new bound for W in the multiparameter case which is inspired by the discussion in [15]. This bound expresses in a natural way how one can trade information about one parameter for information about another. The interest of this new bound depends on the precise problem one is considering:

Abstract The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group and numerous publications have discussed applications and theoretical aspects of it. This paper

Abstract. The intuition behind ensembles is that different prediciton models compensate each other’s errors if one combines them in an appropriate way. In case of large ensembles a lot of different prediction models are available. However, many of them may share similar error characteristics, which

A problem of learning a prediction rule that is approximated in a linear span of a large number of reproducing kernel Hilbert spaces is considered. The method is based on penalized empirical risk minimization with ℓ1type complexity penalty. Oracle inequalities on excess risk of such estimators

Abstract. We address how to write programs for distributed computing systems in which the network topology can change dynamically. Examples of such systems, which we call ensembles, include programmable sensor networks (where the network topology can change due to failures in the nodes or links

Numerical weather prediction (NWP) now provides major guidance in our daily weather forecast. The accuracy of NWP models has improved steadily since the first successful experiment made by Charney, Fj!rtoft and von Neuman (1950). During the past 50 years, a large number of technical papers

Abstract-It has long been realized that in pulse-code modulation (PCM), with a given ensemble of signals to handle, the quantum values should be spaced more closely in the voltage regions where the signal amplitude is more likely to fall. It has been shown by Panter and Dite that, in the limit

filters in that it uses ensemble transformation and a normalization to rapidly obtain the prediction error covariance matrix associated with a particular deployment of observational resources. This rapidity enables it to quickly assess the ability of a large number of future feasible sequences

that are at the basis of real networks, and developing models to mimic the growth of a network and reproduce its structural properties. On the other hand, many relevant questions arise when studying complex networks ’ dynamics, such as learning how a large ensemble of dynamical systems that interact through a complex

between pairs of neurons. Positive corrections imply synergy, while negative corrections indicate redundancy. Here, this analysis, previously applied to recordings from small ensembles, is developed further by considering a model of a large ensemble, in which correlations among the signal and noise