Gaussian Process Optimization: Choose the Next Experiment

Gaussian process optimization is useful when every experiment is expensive. Instead of trying random guesses, it builds a probabilistic surrogate from the observations you already have, then asks where a new measurement could most improve the best known result.

The Gaussian Process Surrogate Optimizer Lab makes that reasoning visible: orange points are observations, the blue curve is the posterior mean, the band is uncertainty, and the lower chart shows expected improvement.

Why This Is Different From Curve Fitting

A curve fit gives one line. A Gaussian process gives a line plus uncertainty. That uncertainty is what lets the model propose a next experiment instead of merely explaining the past.

Expected Improvement

Expected improvement balances exploitation and exploration. A point can be attractive because the posterior mean is high, because uncertainty is high, or because both are true.

How To Use The Lab

1. Open the lab and run the sample first.
2. Replace the x:y observations with normalized public experiment scores.
3. Adjust length scale when the curve is too wiggly or too stiff.
4. Adjust noise variance when observations are noisy.
5. Export the posterior CSV and receipt before sharing a recommendation.

What This Does Not Prove

This is browser math for planning and education. It is not medical advice, legal advice, financial advice, certified scientific proof, traffic proof, ranking proof, revenue proof or AdSense approval proof.