10 min readProbabilistic Reasoning
Learning the Numbers in a Probability Model
Where the numbers in a Bayesian network or a Gaussian actually come from: the three-step maximum-likelihood recipe worked through on discrete and continuous parameters, the Beta prior that repairs what it does to an unseen event, naive Bayes and the single zero count that destroys it, and the EM algorithm for the case where the counts cannot be taken at all - with every figure computed rather than asserted.
ProbabilityStatisticsArtificial Intelligence