Statistical Methods in Applied Computer Science Lecture Notes Preliminary for course 2D5342, Data Mining, Jan-April 2006.
Stefan Arnborg, Kth Nada · 2006
We overview fundamental inference principles for hypothesis and deci-sion choice, parameter(state) estimation and tracking methods. We also explore methods for finding dependencies and graphical models, latent variables and robust decision trees in a joint Bayesian framework. We also consider the most important methods for performing Bayesian inference in various settings: Analytic integration using conjugate families of dis-tributions and likelihoods, discretization, Monte Carlo as well as Markov Chain Monte Carlo and particle filters. We overview related probabilis-tic methods such as robust Bayesian analysis, evidence theory and PAC learning.