Bayesian Models & Neural Networks

Tutorials and applied case studies for Bayesian linear regression, parametric Bayes, Bayesian classification, and Bayesian neural networks — using pyrox, gaussx, and NumPyro.

The full curriculum (parts, subparts, references, gaps, cross-listing with the GP and neural-fields lists) lives in TUTORIAL_MASTER_LIST.md.

Layout¶

• notebooks/A_regression/ — Bayesian linear regression, basis features, RFF/HSGP, likelihood + loss zoo, 9-model regression masterclass pick-apart
• notebooks/B_classification/ — Bayesian logistic / multinomial / Laplace / VI / MCMC / last-layer & SNGP classifiers
• notebooks/C_nn_gp_bridges/ — NNGP, NTK, deep kernels, deep RFF, functional priors, pathwise BNN
• notebooks/D_inference/ — point estimates, Laplace family, VI, sampling, last-layer, stochastic/implicit, tempering, distance-aware
• notebooks/E_ensembles/ — deep ensembles, ensemble runners, diversity strategies
• notebooks/F_calibration/ — ECE, temperature scaling, OOD, active learning
• notebooks/G_bayesian_neural_fields/ — Bayesian INR / NeRF / BNF (companion to ../neural_fields/)
• notebooks/H_applied/ — UCI benchmarks, BNN emulators, Bayesian PINNs, image/signal regression, capstone progressions

Companion lists¶

• Pure GPs → ../gaussian_processes/TUTORIAL_MASTER_LIST.md
• Neural fields / NeRF (deterministic) → ../neural_fields/TUTORIAL_MASTER_LIST.md
• Normalizing flows → ../gaussianization/TUTORIAL_MASTER_LIST.md
• Filtering / variational data assimilation → ../assimilation/docs/