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Interactive 3D/Gaussian Process
Kernel
ℓ (length scale)1.00
σ_n (noise)0.100
State
Observations 0
Kernel RBF
ℓ 1.00
σ_n 0.100
Gaussian Process.
Click canvas to add observations. GP posterior shrinks uncertainty near data points. Larger ℓ = smoother functions. Noise σ_n controls observation fidelity.

Gaussian Process - Interactive Visualization

A Gaussian Process defines a distribution over functions. The prior GP generates smooth random functions (controlled by the kernel length scale ℓ). Each observation constrains the posterior: the mean passes through observations, and uncertainty (2σ bands) narrows near data points and widens far away. Click to add observations and watch the posterior collapse toward the truth.

  • Click canvas to add observation points and watch posterior update
  • Adjust length scale ℓ: large = smooth functions, small = wiggly
  • Adjust noise σ_n: see how noisy observations increase posterior uncertainty
  • Toggle RBF vs Matern kernel to see different smoothness assumptions
  • Foundation for Bayesian optimization, hyperparameter tuning, and surrogate models

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.