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Aristeidis Karras, Christos Karras, Nikolaos Schizas, Markos Avlonitis and Spyros Sioutas
The field of automated machine learning (AutoML) has gained significant attention in recent years due to its ability to automate the process of building and optimizing machine learning models. However, the increasing amount of big data being generated ha...
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Wilson Tsakane Mongwe, Rendani Mbuvha and Tshilidzi Marwala
Markov chain Monte Carlo (MCMC) techniques are usually used to infer model parameters when closed-form inference is not feasible, with one of the simplest MCMC methods being the random walk Metropolis?Hastings (MH) algorithm. The MH algorithm suffers fro...
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Filippo Landi, Francesca Marsili, Noemi Friedman and Pietro Croce
In civil and mechanical engineering, Bayesian inverse methods may serve to calibrate the uncertain input parameters of a structural model given the measurements of the outputs. Through such a Bayesian framework, a probabilistic description of parameters ...
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Abolfazl Shojaei Barjouei and Masoud Naseri
Environmental conditions in Arctic waters pose challenges to various offshore industrial activities. In this regard, better prediction of meteorological and oceanographic conditions contributes to addressing the challenges by developing economic plans an...
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Kiyoshi Umeki,Marc David Abrams,Keisuke Toyama,Eri Nabeshima
Pág. e019
Aim of study: To develop a statistical model framework to analyze longitudinal wind-damage records while accounting for autocorrelation, and to demonstrate the usefulness of the model in understanding the regeneration process of a natural forest.Area of ...
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Edward Oughton,Peter Tyler, David Alderson
Pág. 1 - 17
The Information and Communication Technologies (ICT) infrastructure sector has dramatically expanded over the past decade as the demand for increased digital connectivity has increased from both companies and consumers. Broadband investment has been incr...
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Sandra Cristina de Oliveira, Marinho Gomes de Andrade
Pág. 339 - 347
Current research compares the Bayesian estimates obtained for the parameters of processes of ARCH family with normal and Student?s t distributions for the conditional distribution of the return series. A non-informative prior distribution was adopted and...
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Magni, P; Bellazzi, R; Nicolao, G De
Pág. 1319 - 1331
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