Sentence examples for model parameters see from inspiring English sources

First, to estimate the camera noise model parameters (see Section "Camera noise modeling"), we analyze signal-dependent noise in a similar way as in [42], i.e., by computing the local noise standard deviation as a function of the local intensity.

To generate a large number of such sets and to test the generality of our analysis, we varied other model parameters; see Table 1 for details.

Changing model parameters (see main text and Methods) can lead to the characteristic rings observed in some squid species, with single light chromatophores at the centre and a radial centrifugal darkening gradient.

Underlying all the simulations in this section is a set of baseline model parameters; see Table 1.

Background rates estimated with models using a priori known model parameters (see Fig. 1c, e) and the models that allow simultaneous estimation along with (mu (t)) (see Fig. 1b, d) are nearly the same.

This implies that the direct, line-of-sight (LOS) contribution power outdoors is assumed to be 0 dB, thus the extracted model parameters (see Section 3) will be referred to the conditions just outside the building.

Type Coastal Lakes Open Built-up areas City centers 0.123 0.145 0.189 0.285 0.434 Figure 5 Model for simulating wind speed. is a white Gaussian noise with zero mean and unite variance, is the low-pass filter defined in (2), is a colored noise, is a model parameter (see Table 2), is the mean wind speed,, and is the resulting wind speed.

The results from the sensitivity analysis and the parameter identifiability analysis above showed that the identifiability was relatively low for several of the Niederer-model parameters (see the Results section).

Therefore, we used the data from experiment 1 to fit the model's parameters (see Methods, Appendix S1 and Fig. 3B, E) and then proceeded to compute the model's results for experiment 2. We could thus compare an ideal Bayesian model's performance with human behavior without problems of over-fitting.

As input, AshCalc takes a list of thickness(m), area km2) pairs for each isopach as well as additional model specific parameters (see Sections 2.2-2.4 2.2-2.4

A thorough sensitivity analysis of model input parameters (see Additional file 2) demonstrates the robustness of our results pertaining to infection control.