bayesian-interpretation-regularization-prior

IN premise — entries/2026/06/21/wiki-Supervised_learning-chunk-2.md

Created 2026-06-21T09:55:53+00:00

The regularization penalty in structural risk minimization corresponds to the negative log prior, making the regularized objective equivalent to posterior probability

Dependents

These beliefs depend on this one:

• IN regularization-as-bias-variance-engineering — Regularization is fundamentally bias-variance engineering — it deliberately increases bias to reduce variance, with L2 producing diffuse weight shrinkage and L1 producing sparsity, both interpretable as Bayesian priors on the weight distribution.