Unsupervised local cluster-weighted bootstrap aggregating the output from multiple stochastic simulators

Bayesian Learning of unobservable output aggregating multiple weak classifiers

Abstract: In this paper we propose a bayesian classification rule in a context where the training sample is not complete. The input X is observable, but the output Y is not, and we only utilize the result of weak classifiers instead of Y . Our method is motivated by epidemiological and bioinformatics problems. We prove the consistency of our method and illustrate its efficiency using simulation...

Cluster-weighted stochastic subgrid-scale modelling

This paper presents the main results of a recent publication (Kwasniok, 2011a). A new approach for data-based stochastic parametrisation of unresolved scales and processes in numerical weather and climate prediction models is introduced. The subgrid-scale model is conditional on the state of the resolved scales, consisting of a collection of local models. A clustering algorithm in the space of ...

HIT Dependency Parsing : Bootstrap Aggregating Heterogeneous Parsers

The paper describes our system of Shared Task on Parsing the Web. We only participate in dependency parsing task. A number of methods have been developed for dependency parsing. Each of the methods adopts very different view of dependency parsing, and each view can have its strengths and limitations. Thus system combination can have great potential to further improve the performance of dependen...

Emulation of random output simulators

Local Constant and Local Bilinear Multiple - Output Quantile Regression

A new quantile regression concept, based on a directional version of Koenker and Bassett's traditional single-output one, has been introduced in [Hallin, Paindaveine andŠiman, Annals of Statistics 2010, 635-703] for multiple-output regression problems. The polyhe-dral contours provided by the empirical counterpart of that concept, however, cannot adapt to nonlinear and/or heteroskedastic depend...