JMP 11 Online Documentation (English)
Discovering JMP
Using JMP
Basic Analysis
Essential Graphing
Profilers
Design of Experiments Guide
Fitting Linear Models
Specialized Models
Multivariate Methods
Quality and Process Methods
Reliability and Survival Methods
Consumer Research
Scripting Guide
JSL Syntax Reference
JMP iPad Help
Capabilities Index
Specialized Models
• Gaussian Process
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Gaussian Process
Fit Data Using Smoothing Models
The Gaussian Process platform is used to model the relationship between a continuous response and one or more continuous predictors. These models are common in areas like 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 result in the same output value.
The Gaussian Process platform fits a spatial correlation model to the data, where the correlation of the response between two observations decreases as the values of the independent variables become more distant.
The main purpose for using this platform is to obtain a prediction formula that can be used for further analysis and optimization.
Example of a Gaussian Process Prediction Surface
Contents
Launching the Platform
The Gaussian Process Report
Actual by Predicted Plot
Model Report
Marginal Model Plots
Platform Options
Borehole Hypercube Example