Bayesian

Advanced Algorithms

Probability

expectation random‑variables distribution frequentist bayesian

Topics Axioms of Probability (Kolmogorov) Random Variables & Distributions Expectation & Variance Bayesian Probability Stochastic Processes & Markov Chains Important Theorems Kolmogorov’s Axioms Law of Large Numbers (Weak & Strong) Central Limit Theorem (CLT) Bayes’ Theorem Markov & Chebyshev Inequalities Borel-Cantelli Lemma Martingale Convergence Theorem

Regularised Regression

bayesian map posterior regularisation overfitting bias‑variance

This page is for the closed form solutions (where they exist) and approximation solutions to Regularised Regressions.

We will also understand that regularisation is sensible artifact once we consider its MAP (maximum a posteriori) derivation.