Transcription of Bayesian Decision Theory - gatech.edu
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Bayesian Decision Theory - gatech.edu
Bayesian Decision TheoryChapter2 (Duda, Hart & Stork)CS 7616 -Pattern RecognitionHenrik I ChristensenGeorgia Tech. Bayesian Decision Theory Design classifiers to recommend decisionsthat minimize some total expected risk . The simplest riskis the classification error ( , costs are equal). Typically, the riskincludes the costassociated with different State of nature (random variable): , 1for sea bass, 2for salmon Probabilities P( 1)and P( 2)(priors): , prior knowledge of how likely is to get a sea bass or a salmon Probability density function p(x) (evidence): , how frequently we will measure a pattern with feature value x( , xcorresponds to lightness) Terminology (cont d) Conditional probability density p(x/ j)(likelihood) : , how frequently we will measure a
Bayesian Decision Theory Chapter2 (Duda, Hart & Stork) CS 7616 - Pattern Recognition Henrik I Christensen Georgia Tech. Bayesian Decision Theory • Design classifiers to recommend decisionsthat ... – This is a 1-D optimization problem, regardless to the dimensionality
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