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An adaptive Bayesian Markov chain Monte Carlo scheme, exploiting the link between quantile regression and the skewed-Laplace distribution, is designed for estimation and inference of the quantile causal relations, simultaneously estimating and accounting for heteroscedasticity.
A mode observer is designed for estimation of the active mode, and the continuous spatial profiles are estimated by a Distributed and Decentralized Switching Kalman Filter.
Besides, experiments must often be designed for estimation of model parameters and reduction of variances of model predictions (or parameter estimates).
A continuous-discrete Distributed and Decentralized Switching Kalman Filter (DDSKF) is designed for estimation of spatial profiles in Pressure Swing Adsorption (PSA) processes.
A local approximator with a penalty function is designed for estimation of cost-to-go values over the continuous hyperstate space.
Since genetic change depends not only on genetic estimations but also on the observed phenotypic variation, estimation of non-genetic-environmental factors, must be considered when building statistical models designed for estimation of genetic parameters and genetic evaluation in goats.
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Therefore, an estimation scheme was designed for the estimation of disturbances and their integration into the model inversion procedure.
All the estimators designed for CFO estimation in the presence of I/ Qmismatch, showed robustness to I/ Qmismatch in the experiments.
An adaptive estimator based on the real-time RLS algorithm is designed for dynamic estimation of multipath channel response.
This study compared three methods designed for age estimation.
However, the original algorithm was designed for parameter estimation with non-uniformly sampled instantaneous measurements.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com