Parameter Sensitivity and Estimation: Difference between revisions
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The behavior of a model will depend critically on some parameters, and will have a weak dependence on other parameters. This dependence can be quantified by the normalized parameter sensitivity. This is defined as the differential change in the quantitative behavior of the model (some metric X) for a given fractional change in each parameter of interest. That it, the normalized parameter sensitivity is delta(X)/(delta(k<sub>i</sub>)/k<sub>i</sub>) = dX / d(log(k<sub>i</sub>)). If we are able to design experiments that increases the dependence of the behavior of the model on a given parameter (ie design experiments that increase the parameter sensitivity), then we can potentially gain more information about that parameter | The behavior of a model will depend critically on some parameters, and will have a weak dependence on other parameters. This dependence can be quantified by the normalized parameter sensitivity. This is defined as the differential change in the quantitative behavior of the model (some metric X) for a given fractional change in each parameter of interest. That it, the normalized parameter sensitivity is delta(X)/(delta(k<sub>i</sub>)/k<sub>i</sub>) = dX / d(log(k<sub>i</sub>)). If we are able to design experiments that increases the dependence of the behavior of the model on a given parameter (ie design experiments that increase the parameter sensitivity), then we can potentially gain more information about that parameter from the experimental results. Higher parameter sensitivity does not necessarily lead to better parameter estimates as parameter sensitivity does not take into account the correlation between parameters.<br> | ||
Revision as of 14:41, 2 February 2006
The behavior of a model will depend critically on some parameters, and will have a weak dependence on other parameters. This dependence can be quantified by the normalized parameter sensitivity. This is defined as the differential change in the quantitative behavior of the model (some metric X) for a given fractional change in each parameter of interest. That it, the normalized parameter sensitivity is delta(X)/(delta(ki)/ki) = dX / d(log(ki)). If we are able to design experiments that increases the dependence of the behavior of the model on a given parameter (ie design experiments that increase the parameter sensitivity), then we can potentially gain more information about that parameter from the experimental results. Higher parameter sensitivity does not necessarily lead to better parameter estimates as parameter sensitivity does not take into account the correlation between parameters.
