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Eleni Vlachou, Aristeidis Karras, Christos Karras, Leonidas Theodorakopoulos, Constantinos Halkiopoulos and Spyros Sioutas
In this work, we present a Distributed Bayesian Inference Classifier for Large-Scale Systems, where we assess its performance and scalability on distributed environments such as PySpark. The presented classifier consistently showcases efficient inference...
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Timothy O. Hodson, Keith J. Doore, Terry A. Kenney, Thomas M. Over and Muluken B. Yeheyis
Streamflow is one of the most important variables in hydrology, but it is difficult to measure continuously. As a result, nearly all streamflow time series are estimated from rating curves that define a mathematical relationship between streamflow and so...
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Thomas Parr, Karl Friston and Peter Zeidman
Bayesian inference typically focuses upon two issues. The first is estimating the parameters of some model from data, and the second is quantifying the evidence for alternative hypotheses?formulated as alternative models. This paper focuses upon a third ...
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Xiaoou Li
This paper tackles the challenge of time series forecasting in the presence of missing data. Traditional methods often struggle with such data, which leads to inaccurate predictions. We propose a novel framework that combines the strengths of Generative ...
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Marco Scutari
Bayesian networks (BNs) are a foundational model in machine learning and causal inference. Their graphical structure can handle high-dimensional problems, divide them into a sparse collection of smaller ones, underlies Judea Pearl?s causality, and determ...
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Feilong Ding, Cheng Chi, Yu Li and Haining Huang
In passive sonars, distance and depth estimation of underwater targets is often limited by the accuracy of time delay estimations. The estimation accuracy of the existing methods of time delay estimation is limited by the uniform discrete grid (signal sa...
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Oscar D. Hurtado, Albert R. Ortiz, Daniel Gomez and Rodrigo Astroza
Simplifications and theoretical assumptions are usually incorporated into the numerical modeling of structures. However, these assumptions may reduce the accuracy of the simulation results. This problem has led to the development of model-updating techni...
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Jun-Fang Wang, Jian-Fu Lin and Yan-Long Xie
Subjected to complex loadings from the wheel?rail interaction, turnout rail is prone to crack damage. This paper aims to develop a condition evaluation method for crack-alike damage detection of in-service turnout rail. A covariance-based structural cond...
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Joseph Ndong and Ted Soubdhan
Building a sophisticated forecasting framework for solar and photovoltaic power production in geographic zones with severe meteorological conditions is very challenging. This difficulty is linked to the high variability of the global solar radiation on w...
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Nicholas Wright, Piaras Kelly, Oliver Maclaren, Ruanui Nicholson and Suresh Advani
Predict race-tracking strength in an RTM process using minimal pressure sensor measurements and position the sensors optimally throughout the preform in order to do so.
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