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After these models are built, classification of fault signal is accomplished by computing the conditional likelihoods of the signal under each learned GMM model and selecting the model with the highest likelihood.
By clicking "Binding Model" and selecting specific models, users can select the other bindings and build up new bindings.
It works by building an initial model of all possible functional equations, simplifying the model, and selecting the best parameters for the simplified model.
The first approach involves running comparable tsunami simulations with a full-physics model and a linearized shallow water wave model, and selecting the initial condition on horizontal velocity for the shallow water model using the expressions presented by Xu and Song (2013), Titov et al.
In this case we fitted the regression model by assuming the dominant and the recessive model and selecting the best model on the basis of the Wald test measuring the goodness of fit.
The AR order and the values of the autocorrelation parameters were determined empirically by fitting AR models to the residuals from the initial model and selecting the best-fit models.
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To combine the seed model and selected models, we fed them into Modeller 7v7, and used them as templates to generate 10 new models for the protein.
Results from this model and selected comparisons with measurements are presented.
We present this model and selected examples of computational procedures illustrating it.
We used the experimental data to adjust the model and select the geometry and the turbulence model.
Hide the Left_wrapped model and select the Trim function from the Finish menu.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com