Interactive explainer set
Small, deliberate tools for developing intuition. The first release focuses on Gaussian processes because kernels and posteriors become much easier to reason about once you can manipulate them directly.
Interactive explainer
Compare how different kernels change covariance structure and prior sample paths before fitting any data.
Kernels are often introduced abstractly, but the modeling consequences become clearer once you can see the covariance matrix and sampled functions change together.
Interactive explainer
Place noisy observations directly on the plot and watch the posterior mean and uncertainty band update in real time.
Posterior intuition is easiest to build when the model reacts immediately to new observations and hyperparameter changes.
Open to new collaborations