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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...
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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.
 Fall '08
 Anderson,C
 Machine Learning

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