Stat 13 Lecture 18 Bayes theorem How to update probability of occurrence? Prior probability (  i = prior for theory i) Posterior probability (updated.

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Presentation transcript:

Stat 13 Lecture 18 Bayes theorem How to update probability of occurrence? Prior probability (  i = prior for theory i) Posterior probability (updated probability for theory i ) Tumor classification Handwritten digit/character recognition ( data / feature) Prob (data | class i ) = (often given by experiments or by reasoning); suppose D is observed; denote prob by f(data=D | class= i ); then Prob (class i | data=D)= posterior for class i =  i f(data=D | class = i ) / sum of  j f(data=D| class j ) ; where j goes from 1 to k; k is the total number of classes

HIV test Prob (Positive|HIV)=.98 Suppose Tom is tested positive. What is the chance that he has HIV ?.98 ???? What other information is needed ?

Enzyme-linked immunosorbent assay assay (ELISA) test : gives a quantity called MAR (mean absorbance ratio for HIV antibodies) MARHealthy donorHIV patients < total29788 Pro(positive| Healthy)= false positive rate =22/297=.074