'model parameter values' is correct and usable in written English and can be used in a variety of different contexts.
For example: "The predictive accuracy of the model was evaluated based on the model parameter values."
The model parameter values reflect the most recent fit.
Model parameter values were estimated from the literature.
This is done by sampling model parameter values, a specified number of times, from a given distribution such as a Gaussian distribution, in order to incorporate the uncertainties on model parameter values in the fit.
To unfreeze model parameter values and allow them to vary, the command thaw ("ahelp thaw") is used.
Each run (4 in total) spanned 100 million generations, and the model parameter values and trees were sampled every 10,000 generations.
An innovative calibration protocol was applied to evaluate the model parameter values as well.
It returns an array of simulated fit statistic values, an array of True or False accepted/rejected Booleans, and a 2-D array of associated model parameter values.
An optimization method is one that is used to determine the vector of model parameter values, p0, for which the chosen fit statistic is minimized.
The arrays of model parameter values and associated fluxes from our two sample_flux runs may be written to file using the Sherpa save_arrays command, as shown below.
Initial model parameter values are no longer automatically guessed in Sherpa; the guess command is available for this purpose, but only for simple (i.e. single-component) models.
We can visualize the distribution of model parameter values resulting from the sample_flux simulations, for a particular model component, using the Sherpa plot_pdf or plot_cdf functions.
