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Clara Pereira, Ana Silva, Cláudia Ferreira, Jorge de Brito, Inês Flores-Colen and José D. Silvestre
In the field of building inspection and diagnosis, uncertainty is common and surveyors are aware of it, although it is not easily measured. This research proposes a model to quantify uncertainty based on the inspection of rendered façades. A Bayesian net...
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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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Marta Galvani, Chiara Bardelli, Silvia Figini and Pietro Muliere
Bootstrap resampling techniques, introduced by Efron and Rubin, can be presented in a general Bayesian framework, approximating the statistical distribution of a statistical functional ϕ(F)" role="presentation">??(??)?(F)
?
(
F
)
, where F is a...
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Bijen Khagi, Kun Ho Lee, Kyu Yeong Choi, Jang Jae Lee, Goo-Rak Kwon and Hee-Deok Yang
This paper presents an efficient computer-aided diagnosis (CAD) approach for the automatic detection of Alzheimer?s disease in patients? T1 MRI scans using the voxel-based morphometry (VBM) analysis of the region of interest (ROI) in the brain. The idea ...
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Jun Wang, Zhongmin Liang, Xiaolei Jiang, Binquan Li and Li Chen
Real-time correction models provide the possibility to reduce uncertainties in flood prediction. However, most traditional techniques cannot accurately capture many sources of uncertainty and provide a quantitative evaluation. To account for a wide varie...
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