9972 9544 6826 3 1 3 1 2 x

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: to have a standard normal probability distribution. 99.72% 95.44% 68.26% µ – 3σ µ – 1σ µ µ + 3σ µ + 1σ µ – 2σ x µ + 2σ Standard Normal Probability Distribution   Standard Normal Distribution Gaussian Parameter Estimation Converting to the Standard Normal Distribution z= x−µ σ Likelihood function We can think of z as a measure of the number of standard deviations x is from µ. 8 10/29/13 Maximum (Log) Likelihood Example 34 Gaussian models Gaussian Naïve Bayes Assume we have data that belongs to three classes, and assume a likelihood that follows a Gaussian distribution Likelihood function: P ( X i = x | Y = yk ) = p 1 2⇡ exp ik ✓ µik )2 (x 2 2 ik ◆ Need to estimate mean and variance for each feature in each class. 35 36 9 10/29/13 Summary Naïve Bayes classifier: ²་  What’s the assumption ²་  Why we make it ²་  How we learn it Naïve Bayes for discrete data Gaussian naïve Bayes 37 10...
View Full Document

This note was uploaded on 02/10/2014 for the course CS 545 taught by Professor Anderson,c during the Fall '08 term at Colorado State.

Ask a homework question - tutors are online