Merging Model Objects
You can merge models of the same structure to obtain a single
model with parameters that are statistically weighed means of the
parameters of the individual models. When computing the merged model,
the covariance matrices of the individual models determine the weights
of the parameters.
You can perform the merge operation for the idarx, idgrey, idpoly, idproc,
and idss model objects.
Note
Each merge operation merges the same type of model object. |
Merging models is an alternative to merging data sets into a
single multiexperiment data set, and then estimating a model for the
merged data. Whereas merging data sets assumes that the signal-to-noise
ratios are about the same in the two experiments, merging models allows
greater variations in model uncertainty, which might result from greater
disturbances in an experiment.
When the experimental conditions are about the same, merge the
data instead of models. This approach is more efficient and typically
involves better-conditioned calculations. For more information about
merging data sets into a multiexperiment data set, see Creating Multiexperiment Data at the Command Line.
For more information about merging models, see the merge reference page.
 | Concatenating Model Objects | | Nonlinear Black-Box Model Identification |  |
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