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 present paper aims to provide a brief review on several deterministic ensemble Kalman filtering (EnKF) methods and the related practical techniques to prevent filter divergence. Since Evensen (1994), several formulations of EnKF have been proposed, and Whitaker and Hamill (2002) suggested that a deterministic method, a.k.a. an ensemble square root filter (EnSRF, Andrews 1968), is expected t...

We propose a method for post-processing an ensemble of multivariate forecasts in order to obtain a joint predictive distribution of weather. Our method utilizes existing univariate postprocessing techniques, in this case ensemble Bayesian model averaging (BMA), to obtain estimated marginal distributions. However, implementing these methods individually offers no information regarding the joint ...

Clustering ensemble performance is affected by two main factors: diversity and quality. Selection of a subset of available ensemble members based on diversity and quality often leads to a more accurate ensemble solution. However, there is not a certain relationship between diversity and quality in selection of subset of ensemble members. This paper proposes the Hierarchical Cluster Ensemble Sel...

The present paper aims to provide a brief review on several deterministic ensemble Kalman filtering (EnKF) methods and the related practical techniques to prevent filter divergence. Since Evensen (1994), several formulations of EnKF have been proposed, and Whitaker and Hamill (2002) suggested that a deterministic method, a.k.a. an ensemble square root filter (EnSRF, Andrews 1968), is expected t...