Recently ensemble selection for consensus clustering has emerged as a research problem in Machine Intelligence. Normally consensus clustering algorithms take into account the entire ensemble of clustering, where there is a tendency of generating a very large size ensemble before computing its consensus. One can avoid considering the entire ensemble and can judiciously select few partitions in t...

The sensitivity of numerical weather forecasts to small changes in initial conditions is estimated using ensemble samples of analysis and forecast errors. Ensemble sensitivity is defined here by linear regression of analysis errors onto a given forecast metric. We show that ensemble sensitivity is proportional to the projection of the analysis-error covariance onto the adjoint sensitivity field...

A new ensemble ocean data assimilation system, developed for the Predictive Ocean Atmosphere Model for Australia (POAMA), is described. The new system is called PEODAS, the POAMA Ensemble Ocean Data Assimilation System. PEODAS is an approximate form of an ensemble Kalman filter system. For a given assimilation cycle, a central forecast is integrated, along with a small ensemble of forecasts tha...

In this paper, it is proposed that coastal flood ensemble forecasts be partitioned into sub-ensemble forecasts using cluster analysis in order to produce representative statistics and to measure forecast uncertainty arising from the presence of clusters. After clustering the ensemble members, the ability to predict the cluster into which the observation will fall can be measured using a cluster...

A theory for estimating the probability distribution of the state of a model given a set of observations exists. This nonlinear filtering theory unifies the data assimilation and ensemble generation problem that have been key foci of prediction and predictability research for numerical weather and ocean prediction applications. A new algorithm, referred to as an ensemble adjustment Kalman filte...

The main focus of this thesis concerns the further developments in the areas of ensemble and constrained clustering. The goal of the proposed methods is to address clustering problems, in which the optimal clustering method is unknown. Additionally, by means of pairwise linkage constraints, it is possible to aggregate extra information to the clustering framework. Part I investigates the concep...

We extend the main result of the companion paper math-ph/0212063 to the case of the pfaffian ensembles.

The …nite-sample as well as the asymptotic distribution of Leung and Barron’s (2006) model averaging estimator are derived in the context of a linear regression model. An impossibility result regarding the estimation of the …nite-sample distribution of the model averaging estimator is obtained.