learning bayesian networks from data — maximum-likelihood estimation of cpts, additive smoothing, handling missing data with expectation-maximisation, and structure learning from chow-liu trees to score-based search.
Maximum-Likelihood
2026-08-31
a loss function is not a detail of training — it is the definition of the problem. choose squared error and you have asked for the conditional mean; choose absolute error and you have asked for the median; choose hinge and you have asked only for the decision boundary; choose cross-entropy and you have asked for the whole probability. 𐃏 this page catalogues the standard losses, proves what each one’s minimiser actually is, and draws the classic picture that unifies the classification zoo: every one of them is a bribe paid to make the 0–1 loss differentiable.