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For each model, we started with a maximal random-effects structure (Barr, Levy, Scheepers, & Tily, 2013); we then examined the correlations among the random effects, and for correlations that were near 1, we simplified the random effects-structure to prevent the models being over-parameterized (Bates, Kliegl, Vasishth, & Baayen, 2015).

In developing the latter model, we started with the evident constraint that, the more time spent on one movement, the less time remained for the other.

To understand the signature of selection on pathogen molecular variation in our model we started considering the invasion of the resident pathogen population by a slightly distinct variant (phenotypically).

In the present model, we started by incorporating the Ca2+ buffering formulations of Kurata et al. (36).

Because our design criteria depend on the process model, we started by fitting a GP model to the outputs of the numerical model available.

To select characteristics for evaluation in the logistic regression model, we started with clinical characteristics that are frequently and reliably measured in routine clinical practice (e.g., hypertension).

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To elaborate further, and in the context of this study's model, we start with Ricardo's perspective.

To get the static reservoir model, we start generating facies, and then, we simulate the diagenesis phases given the facies before going through the distribution of the petrophysical properties.

In order to find the best 1896 tsunami source model, we start from the northern part of the 2011 source model, compute the tsunami heights on the Sanriku coast and tsunami waveforms at tide gage stations, and compare them with the 1896 observations.

To formulate a specific model, we start with the general expression for the temperature-dependent part of the resistivity [77] given by ρ = 3 π ℏ e 2 v F 2 ∫ 0 2 k f v q 2 S q 2 1 2 k F 4 q 3 d q, Open image in new window (65).

For the reduced CS model, we start with the full Hodgkin–Huxley model, with m, n, h as gating variables and use the method of equivalent potentials as treated in [26], giving rise to the following form for the function g: G ( v, u ) = ( ∂ F ∂ h [ h ∞ ( v ) − h ∞ ( u ) τ h ( v ) ] + ∂ F ∂ n [ n ∞ ( v ) − n ∞ ( u ) τ n ( v ) ] ) / ( ∂ f ∂ h ∞ d h ∞ ( u ) d u + ∂ f ∂ n ∞ d n ∞ ( u ) d u ) (49).

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