Xiaoyan Zhu (朱晓燕)
Dept. of Computer Science & Technology
Xi’an Jiaotong University
E-Mail: xjtueden@163.com
Office: Room B-559, Xi-Yi House, Main Campus
Machine Learning & Data Mining
— Bayesian Learning
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Overview
◼ Introduction
◼ Bayes theorem
◼ Minimum description length hypotheses
◼ Bayes optimal classifier
◼ Gibbs algorithm, Bagging Classifiers
◼ Naive Bayes classifier
◼ Bayesian belief networks
Machine Learning & Data Mining
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Thomas Bayes (1702 - 1761)
Bayesian theory of probability was set out in 1764. His
conclusions were accepted by Laplace in 1781, rediscovered
by Condorcet, and remained unchallenged until Boole
questioned them.
Machine Learning & Data Mining
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Bayesian Learning
◼ Probabilistic approach to inference
◼ Assumption
◆Quantities of interest are governed by probability
distribution
◆Optimal decisions can be made by reasoning about
probabilities and observations
◼ Bayesian learning provides quantitative
approach to weighing how evidence supports
alternative hypotheses
Machine Learning & Data Mining
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Introduction
◼ Bayesian Decision Theory is a fundamental
statistical approach that quantifies the
tradeoffs between various decisions using
probabilities and costs that accompany such
decisions.
◆First, we will assume that all probabilities are
known.
◆Then, we will study the cases where the
probabilistic structure is not completely known.
Machine Learning & Data Mining
