Regression that knows what it doesn't know. Fit a distribution over functions to your data and watch the uncertainty band widen wherever evidence runs out.
Gaussian Process StudioLive
Bayesian regression with uncertainty
Controls
A Gaussian process fits a whole distribution over functions to your data. Click the canvas to add training points, or drag them around, and watch the posterior mean, 95% band, and sample functions update live.
Reading this result: The posterior mean interpolates the 9 points and the 95% band collapses at each observation (up to the noise σ_n=0.12) and widens in the gaps between them — that widening is the model honestly reporting where it has no evidence.
Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.
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How it works
Gaussian process regression (also called kriging) places a prior over functions defined by an RBF kernel k(x,x')=σ²exp(−(x−x')²/2ℓ²), then conditions on your observations to get a posterior. The posterior mean interpolates the data while the 95% confidence band collapses at each point and widens in the gaps — an honest map of where the model has evidence and where it is guessing. The length-scale ℓ controls how far each point's influence reaches: short ℓ gives a wiggly fit, long ℓ a smooth one. Everything runs client-side with a from-scratch Cholesky solve of the kernel system — no libraries.
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Ask the AI about this model
The math, the assumptions, real-world uses, or a code translation — explained for this exact simulation.
Is this Gaussian process regression kriging tool really free?▾
Yes. Gaussian Process runs entirely in your browser using your device's own compute, so local use is free forever. You only pay Compute Tokens if you scale a job to the cloud.
Do I need to install anything?▾
No. Everything runs client-side in a modern browser — no downloads, no license, no account required to start.
Can I save or share my simulation?▾
Create a free account to save projects, and use a shareable embed or minted DOI to publish a live, interactive version anywhere.
How accurate are the results?▾
The solver uses established numerical methods, but results are for research and educational purposes and should be validated against experiment or professional review before you rely on them.