Hi everyone,
I’m having some trouble understanding Gaussian Process Regression (GPR) options in the Regression Learner App. There are three main choices for GPR models:
Predefined Kernel: I can directly choose a kernel (Rational Quadratic, Squared Exponential, Matern 5/2, or Exponential) if I know which one suits my data best.
All GPR Models (non-optimizable): If I’m unsure which kernel to use, I can select this option to try all non-optimizable GPR models.
Optimizable GPR: This option allows the hyperparameters to be optimized and has even more Kernels available.
Regardless of whether I choose the optimizable or non-optimizable version, each kernel has hyperparameters that can be tuned.
Here are my questions:
Automatic Hyperparameter Selection: For the standard model without optimization, the kernel parameters (hyperparameters) are automatically selected and are an initial best guess rather than the optimal ones to my understanding. If so, how are they estimated/selected, and why aren’t they optimal?
Optimization Process: When the optimization option is selected, the hyperparameters are found by maximizing the Log Marginal Likelihood function. So, they are not guessed, but these are the hyperparameters that best describe the data. So finding the best value is basically the optimization process?
Choosing Non-Optimal Hyperparameters: Why would someone use the version without the best hyperparameters? One reason I’ve encountered is that optimization can take too long with the same data. Are there other reasons to choose non-optimizable models?
Best Practices: Are there standard practices or guidelines for when to use each version of the GPR models? Or one usually starts with non-optimizable version and then go to the optimizable one if the results are not sufficient enough?
Output Differences: Both versions output forecasted data and the covariance matrix. Does the optimized version provide any additional information or benefits?
Plotting Covariance: How can I plot the covariance that quantifies the uncertainty of my predictions? This is one of the main advantages of using GPR, and I want to visualize it. I have found some sample code here https://ch.mathworks.com/help/stats/gaussian-process-regression-models.html But I am a bit confused, because my input is a table actually, so I do not have only one predictor but a table. So far I have always used “predictedData = trainedModel.predictFcn(Table); but this gives me only the forecasted data without the 95% prediction intervals
I’m generally confused about the differences between both versions and the results they produce. Any insights or explanations would be greatly appreciated.
Thanks in advance!
Best regards,
GeorgiHi everyone,
I’m having some trouble understanding Gaussian Process Regression (GPR) options in the Regression Learner App. There are three main choices for GPR models:
Predefined Kernel: I can directly choose a kernel (Rational Quadratic, Squared Exponential, Matern 5/2, or Exponential) if I know which one suits my data best.
All GPR Models (non-optimizable): If I’m unsure which kernel to use, I can select this option to try all non-optimizable GPR models.
Optimizable GPR: This option allows the hyperparameters to be optimized and has even more Kernels available.
Regardless of whether I choose the optimizable or non-optimizable version, each kernel has hyperparameters that can be tuned.
Here are my questions:
Automatic Hyperparameter Selection: For the standard model without optimization, the kernel parameters (hyperparameters) are automatically selected and are an initial best guess rather than the optimal ones to my understanding. If so, how are they estimated/selected, and why aren’t they optimal?
Optimization Process: When the optimization option is selected, the hyperparameters are found by maximizing the Log Marginal Likelihood function. So, they are not guessed, but these are the hyperparameters that best describe the data. So finding the best value is basically the optimization process?
Choosing Non-Optimal Hyperparameters: Why would someone use the version without the best hyperparameters? One reason I’ve encountered is that optimization can take too long with the same data. Are there other reasons to choose non-optimizable models?
Best Practices: Are there standard practices or guidelines for when to use each version of the GPR models? Or one usually starts with non-optimizable version and then go to the optimizable one if the results are not sufficient enough?
Output Differences: Both versions output forecasted data and the covariance matrix. Does the optimized version provide any additional information or benefits?
Plotting Covariance: How can I plot the covariance that quantifies the uncertainty of my predictions? This is one of the main advantages of using GPR, and I want to visualize it. I have found some sample code here https://ch.mathworks.com/help/stats/gaussian-process-regression-models.html But I am a bit confused, because my input is a table actually, so I do not have only one predictor but a table. So far I have always used “predictedData = trainedModel.predictFcn(Table); but this gives me only the forecasted data without the 95% prediction intervals
I’m generally confused about the differences between both versions and the results they produce. Any insights or explanations would be greatly appreciated.
Thanks in advance!
Best regards,
Georgi Hi everyone,
I’m having some trouble understanding Gaussian Process Regression (GPR) options in the Regression Learner App. There are three main choices for GPR models:
Predefined Kernel: I can directly choose a kernel (Rational Quadratic, Squared Exponential, Matern 5/2, or Exponential) if I know which one suits my data best.
All GPR Models (non-optimizable): If I’m unsure which kernel to use, I can select this option to try all non-optimizable GPR models.
Optimizable GPR: This option allows the hyperparameters to be optimized and has even more Kernels available.
Regardless of whether I choose the optimizable or non-optimizable version, each kernel has hyperparameters that can be tuned.
Here are my questions:
Automatic Hyperparameter Selection: For the standard model without optimization, the kernel parameters (hyperparameters) are automatically selected and are an initial best guess rather than the optimal ones to my understanding. If so, how are they estimated/selected, and why aren’t they optimal?
Optimization Process: When the optimization option is selected, the hyperparameters are found by maximizing the Log Marginal Likelihood function. So, they are not guessed, but these are the hyperparameters that best describe the data. So finding the best value is basically the optimization process?
Choosing Non-Optimal Hyperparameters: Why would someone use the version without the best hyperparameters? One reason I’ve encountered is that optimization can take too long with the same data. Are there other reasons to choose non-optimizable models?
Best Practices: Are there standard practices or guidelines for when to use each version of the GPR models? Or one usually starts with non-optimizable version and then go to the optimizable one if the results are not sufficient enough?
Output Differences: Both versions output forecasted data and the covariance matrix. Does the optimized version provide any additional information or benefits?
Plotting Covariance: How can I plot the covariance that quantifies the uncertainty of my predictions? This is one of the main advantages of using GPR, and I want to visualize it. I have found some sample code here https://ch.mathworks.com/help/stats/gaussian-process-regression-models.html But I am a bit confused, because my input is a table actually, so I do not have only one predictor but a table. So far I have always used “predictedData = trainedModel.predictFcn(Table); but this gives me only the forecasted data without the 95% prediction intervals
I’m generally confused about the differences between both versions and the results they produce. Any insights or explanations would be greatly appreciated.
Thanks in advance!
Best regards,
Georgi regression, optimization MATLAB Answers — New Questions

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