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A personalized measure for thermal comfort has been applied for use in combination with smart controls for building automation. Using data from a field study, we first show the superiority of personalized measures for thermal comfort compared to standard n ...
We present a class of models that, via a simple construction, enables exact, incremental, non-parametric, polynomial-time, Bayesian inference of conditional measures. The approach relies upon creating a sequence of covers on the conditioning variable and m ...
The binary data are often used in the medical field or in social sciences to study an effect between two groups. It can be for example the effect of a treatment between a treated and a control group. The outcome will be the number of successes and the tota ...
We run a comparative study of ecohydrological models of streamflow probability distributions (pdfs), p(Q), derived by Botter et al. (2007a, 2009), against field data gathered in different hydrological contexts. Streamflows measured in several catchments ac ...
We consider a confidence parametrization of binary information sources in terms of appropriate likelihood ratios. This parametrization is used for Bayesian belief updates and for the equivalent comparison of binary experiments. In contrast to the standard ...
Although efficient processing of probabilistic databases is a well-established field, a wide range of applications are still unable to benefit from these techniques due to the lack of means for creating probabilistic databases. In fact, it is a challenging ...
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The main objective of the thesis is the study and the determination of an optimal navigation strategy, for a sailboat in an America's Cup race, by methods of stochastic optimization. Such methods are appropriate because the sailing team expects the wind to ...
We consider a two-type contact process on Z in which both types have equal finite range and supercritical infection rate. We show that a given type becomes extinct with probability 1 if and only if, in the initial configuration, it is confined to a finite ...
This paper presents an approach for model identifiability that builds upon recent research into measurement data interpretation. The objective of this approach is to determine probabilistically to what degree the number of models able to explain a measured ...
Class posterior distributions can be used to classify or as intermediate features, which can be further exploited in different classifiers (e.g., Gaussian Mixture Models, GMM) towards improving speech recognition performance. In this paper we examine the p ...