Isotonic distributional regression

Isotonic distributional regression (IDR) is a powerful non-parametric technique for the estimation of conditional distributions under order restrictions. In a nutshell, IDR learns conditional distributions that are calibrated, and simultaneously optimal relative to comprehensive classes of relevant loss functions, subject to isotonicity constraints in terms of a partial order on the covariate space. Non-parametric isotonic quantile regression and non-parametric isotonic binary regression emerge as special cases. For prediction, we propose an interpolation method that generalizes extant specifications under the pool adjacent violators algorithm. We recommend the use of IDR as a generic benchmark technique in probabilistic forecast problems, as it does not involve any parameter tuning nor implementation choices, except for the selection of a partial order on the covariate space. The method can be combined with subsample aggregation, with the benefits of smoother regression functions and gains in computational efficiency. In a simulation study, we compare methods for distributional regression in terms of the continuous ranked probability score (CRPS) and 2 estimation error, which are closely linked. In a case study on raw and post-processed quantitative precipitation forecasts from a leading numerical weather prediction system, IDR is competitive with state of the art techniques

Georeferencing Flickr photos using language models at different levels of granularity: an evidence based approach

The topic of automatically assigning geographic coordinates to Web 2.0 resources based on their tags has recently gained considerable attention. However, the coordinates that are produced by automated techniques are necessarily variable, since not all resources are described by tags that are sufficiently descriptive. Thus there is a need for adaptive techniques that assign locations to photos at the right level of granularity, or, in some cases, even refrain from making any estimations regarding location at all. To this end, we consider the idea of training language models at different levels of granularity, and combining the evidence provided by these language models using Dempster and Shafer’s theory of evidence. We provide experimental results which clearly confirm that the increased spatial awareness that is thus gained allows us to make better informed decisions, and moreover increases the overall accuracy of the individual language models

Isotonic Distributional Regression

Isotonic distributional regression (IDR) is a powerful nonparametric technique for the estimation of conditional distributions under order restrictions. In a nutshell, IDR learns conditional distributions that are calibrated, and simultaneously optimal relative to comprehensive classes of relevant loss functions, subject to isotonicity constraints in terms of a partial order on the covariate space. Nonparametric isotonic quantile regression and nonparametric isotonic binary regression emerge as special cases. For prediction, we propose an interpolation method that generalizes extant specifications under the pool adjacent violators algorithm. We recommend the use of IDR as a generic benchmark technique in probabilistic forecast problems, as it does not involve any parameter tuning nor implementation choices, except for the selection of a partial order on the covariate space. The method can be combined with subsample aggregation, with the benefits of smoother regression functions and gains in computational efficiency. In a simulation study, we compare methods for distributional regression in terms of the continuous ranked probability score (CRPS) and $L_2$ estimation error, which are closely linked. In a case study on raw and postprocessed quantitative precipitation forecasts from a leading numerical weather prediction system, IDR is competitive with state of the art techniques

4º congresso português de ‘Building Information Modelling’ vol. 2 - ptBIM

PublishedLivro de atas do Congresso ptBIM 2022, onde se promove um fórum de discussão técnico-científica em língua Portuguesa nas metodologias ‘Building Information Modelling’ (BIM), envolvendo a participação ativa das comunidades profissional e académica das áreas de Arquitetura e Engenharia. Pretende-se enfatizar os problemas e esforços de implementação na Indústria da Construção e reforçar as redes de profissionais que incorporam práticas BIM nas suas atividades. https://ptbim.org

4º congresso português de ‘Building Information Modelling’ vol. 2 - ptBIM

Livro de atas do Congresso ptBIM 2022, onde se promove um fórum de discussão técnico-científica em língua Portuguesa nas metodologias ‘Building Information Modelling’ (BIM), envolvendo a participação ativa das comunidades profissional e académica das áreas de Arquitetura e Engenharia. Pretende-se enfatizar os problemas e esforços de implementação na Indústria da Construção e reforçar as redes de profissionais que incorporam práticas BIM nas suas atividades. https://ptbim.org