Predictive and Specialized Modeling • Gaussian Process
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Gaussian Process
Fit Data Using Smoothing Models
Use the Gaussian Process platform to model the relationship between a continuous response and one or more predictors. These types of models are common in computer simulation experiments, such as the output of finite element codes, and they often perfectly interpolate the data. Gaussian processes can deal with these no-error-term models, in which the same input values always results in the same output value.
The Gaussian Process platform fits a spatial correlation model to the data. The correlation of the response between two observations decreases as the values of the independent variables become more distant.
One purpose for using this platform is to obtain a prediction formula that can be used for further analysis and optimization.
Figure 14.1 Gaussian Process Prediction Surface Example
Contents 
Example of Gaussian Process
Launch the Gaussian Process Platform
The Gaussian Process Report
Actual by Predicted Plot
Model Report
Marginal Model Plots
Gaussian Process Platform Options
Additional Examples of the Gaussian Process Platform
Example of a Gaussian Process Model
Example of Gaussian Process Model with Categorical Predictors
Statistical Details for the Gaussian Process Platform
Models with Continuous Predictors
Models with Categorical Predictors
Variance Formula Parameterization
Model Fit Details

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Help created on 10/11/2018