The model not always estimates correctly the phase and the magnitude of intense geomagnetic storm effects on the daytime F2 layer peak electron density at different European latitudes.
The results show that the model could estimate correctly the similarity among users in the majority of cases.
Nevertheless, diffusion coefficient is not measured accurately unless all other contributing mass transfer parameters are considered, included in the modeling, and estimated correctly.
The sensitivities of the model estimated concentrations for correctly classifying the upper 50th and upper 75th percentiles of the measured PCE concentrations were 63% and 59%, respectively.
Model parameters are estimated correctly for a broader range of branch lengths in ML than in Bayesian analyses; this may explain, at least in part, ML's superior performance at most branch lengths/depths.
The model estimated that the odds of correctly responding were 4.41 times higher, 95% CI [1.22, 15.96], among students who received instruction that did not include gesture to the equations than among students who received instruction that did include gestures to the equations.
The percentile categories of the model estimated and measured total trihalomethane (THM) concentrations were identical in 74.8 – 85.1% of samples (depending on the water supply region), and the sensitivities of the model estimated concentrations for correctly classifying a "high" THM exposure level ranged from 70.2%to84.5%5%.
These results appear to conflict with previous work suggesting that Bayesian branch lengths are estimated correctly unless the model is under- or over-parameterized [ 39, 40].
The optoelectronic noise model is correctly estimated on true raw data by other authors, e.g.,[5], only if the range of nonlinearity is carefully avoided by the estimation procedure.
In this article, we investigate major factors that influence the energy consumed in clustered multi-hop WSNs using the probabilistic cluster-head selection method and propose a novel energy model to correctly estimate the energy consumed in a network.
