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Probabilistic Load Forecasting of Distribution Power Systems based on Empirical Copulas
Accurate and reliable electricity load forecasts are becoming increasingly important as the share of intermittent resources in the system increases.
Unbiased likelihood-based estimation of Wright-Fisher diffusion processes
In this paper we propose an unbiased Monte Carlo maximum likelihood estimator for discretely observed Wright-Fisher diffusions.
Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines
The challenges for non-intrusive methods for Polynomial Chaos modeling lie in the computational efficiency and accuracy under a limited number of model simulations.
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