Revision as of 10:46, 21 April 2014 by Wang957 (Talk | contribs)


Bayes rule in practice
A slecture by Lu Wang

(partially based on Prof. Mireille Boutin's ECE 662 lecture)



1. Bayes rule for Gaussian data

    Given data x ∈ Rd and N categories {wi}, i=1,2,…,N, we decide which category the data corresponds to by computing the probability of the N events. We’ll pick the category with the largest probability. Mathematically, this can be interpreted as:


$ \max_{w_{i}} \rho \left(w_{i}|x\right) $


According to Bayes rule:


$ \max_{w_{i}} \rho \left(w_{i}|x\right) = \max_{w_{i}} \rho \left(x|w_{i}\right)Prob(w_{i}) $


In our case, the data is distributed as Gaussian. So we have,

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