Experimental Design
| Introduction | Experiment Design | Model Optimization | Links |


Experimental Design

assumes you know the neighborhood of the optimal parameters and answers the question of how to efficiently look around the neighborhood. This requires you assume something about the experimental model. This is a local implementation of GOSSET.

GOSSET Documentation = minimum prediction experimental variance.

Similar programs not locally implemented:

  • INFAC = Incomplete Factorial Experiment Design (similar to a linear model as calculated by GOSSET)

  • SAmBa computes the minimal number of experiments required for a solution to be found.

Model Optimization

 uses the coefficients and parameters from your experiments to estimate what the most likely model is. This is done using the Akaike information criterion stepwise regression. This is a maximum information algorithm to estimate what the best model is given experimental results.