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For more reliable fault model estimation, we can consider the other approaches.
For the coseismic fault model estimation, we chose 60 GPS sites near the focal area.
In this fault model estimation, we considered four fault segments (F1 F4) that were suggested from interferograms.
From both the observations and model estimation, we discuss the propagation of compressional Pi 2 waves in the low-latitude magnetosphere.
In the fault model estimation, we first searched the presumed parameters for fault locations, sizes, dips, and strike by the trial-and-error method, and then improved the solution using the Levenberg Marquardt (LM) method (Marquardt 1963).
Following model estimation, we used receiver-operator curves (ROC), calculated on the holdout test set, to assess model performance.
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From the results of the model estimations we can observe the relevance that type of service has in determining type of OOPE and level of expenditures.
In this model estimation section, we establish the parameters of the stochastic model that is used for forecast.
To incorporate biological network information into the model estimation procedure, we adopted the network-constrained regularization framework proposed by Li et al [ 22].
In our model and estimation, we explicitly account for this endogenous effect to ensure that we measure the unbiased impact of the two types of mail on the two responses of the donors.
Model Estimation and Selection: We estimated the posterior distributions of the hierarchical Bayesian models using Markov chain Monte Carlo (MCMC) methods.
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CEO of Professional Science Editing for Scientists @ prosciediting.com