User:Rulrotkake/Books/Statistics and ML

Statistics and ML

 * Mathematics background
 * Stirling's approximation
 * Continuous mapping theorem


 * Distributions
 * Beta function
 * Dirichlet distribution
 * Multivariate normal distribution


 * Statistics fundamentals
 * Wald test
 * Student's t-test
 * F-test
 * Statistical power
 * Completeness (statistics)
 * Akaike information criterion
 * Analysis of variance
 * Bootstrapping (statistics)


 * Models and estimation
 * Expectation–maximization algorithm
 * Generalized linear model
 * Conjugate prior
 * Metropolis–Hastings algorithm
 * Reversible-jump Markov chain Monte Carlo
 * Kaplan–Meier estimator
 * Proportional hazards model
 * Singular value decomposition
 * Multidimensional scaling
 * Gaussian process
 * Stochastic optimization
 * Viterbi algorithm


 * Machine learning
 * Support vector machine