This paper looks at unobserved components models and examines the implied weighting patterns for signal extraction. There are three main themes. The rst is the implications of correlated disturbances driving the components, especially those cases in which the correlation is perfect. The second is how setting up models with t distributed disturbances leads to weighting patterns which are robust ...

First we modify the basic (binary) context-tree weighting method such that the past symbols x1 D; x2 D; ; x0 are not needed by the encoder and the decoder. Then we describe how to make the context-tree depth D infinite, which results in optimal redundancy behavior for all tree sources, while the number of records in the context tree is not larger than 2T 1: Here T is the length of the source se...

Calibrated weighting methods for estimation of survey population characteristics are widely used. At the same time, model-based prediction methods for estimation of small area or domain characteristics are becoming increasingly popular. This paper explores weighting methods based on the mixed models that underpin small area estimates to see whether they can deliver equivalent small area estimat...

Extreme nonlinearity and exhibition of severe interaction effects of multivariable pH processes makes it an appropriate test bed for evaluation of advanced controllers. This paper studies different multiple model methods for Generalized Predictive Control using Independent Model approach (GPCI) with adaptive weighting matrices. New method for adaptive determination of weighting matrices, propos...

Mesh simplification has become a key ingredient for real-time graphics applications. However, practitioners have found that automatic simplification methods usually fail to produce satisfactory result when models of very low polygon count are desired. This is due to the fact that existing methods take no semantic or functional metric into account, and moreover, each error metric proposed previo...

1 Knowledge Engineering & Machine Learning Group, Technical University of Catalonia, Barcelona, email: {hnunez, miquel}@lsi.upc.es Abstract. The major hypothesis that we will be prove in this paper is that unsupervised learning techniques of feature weighting are not significantly worse than supervised methods, as is commonly believed in the machine learning community. This paper tests the powe...