Assuming normally distributed data! Naïve Bayes Classifier.

Slides:



Advertisements
Similar presentations
Announcements •Homework due Tuesday. •Office hours Monday 1-2 instead of Wed. 2-3.
Advertisements

The Normal Distribution
INC 551 Artificial Intelligence Lecture 11 Machine Learning (Continue)
Econ 338/envr 305 clicker questions
Psy302 Quantitative Methods
The Central Limit Theorem Today, we will learn a very powerful tool for sampling probabilities and inferential statistics: The Central Limit Theorem.
ONE SAMPLE t-TEST FOR THE MEAN OF THE NORMAL DISTRIBUTION Let sample from N(μ, σ), μ and σ unknown, estimate σ using s. Let significance level =α. STEP.
Sample size computations Petter Mostad
Multi-Class Object Recognition Using Shared SIFT Features
Statistics Lecture 20. Last Day…completed 5.1 Today Parts of Section 5.3 and 5.4.
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Statistical inference (Sec. )
Kernel Methods Part 2 Bing Han June 26, Local Likelihood Logistic Regression.
Simple Bayesian Supervised Models Saskia Klein & Steffen Bollmann 1.
Standard error of estimate & Confidence interval.
Lesson 12-1 Algebra Check Skills You’ll Need 12-4
Review of normal distribution. Exercise Solution.
1  The goal is to estimate the error probability of the designed classification system  Error Counting Technique  Let classes  Let data points in class.
Econ 3790: Business and Economics Statistics Instructor: Yogesh Uppal
Bayesian Networks. Male brain wiring Female brain wiring.
ME 322: Instrumentation Lecture 3 January 27, 2012 Professor Miles Greiner.
Empirical Research Methods in Computer Science Lecture 7 November 30, 2005 Noah Smith.
Classification Techniques: Bayesian Classification
Statistics 300: Elementary Statistics Section 6-5.
Estimation Chapter 8. Estimating µ When σ Is Known.
6.3 THE CENTRAL LIMIT THEOREM. DISTRIBUTION OF SAMPLE MEANS  A sampling distribution of sample means is a distribution using the means computed from.
§ 5.3 Normal Distributions: Finding Values. Probability and Normal Distributions If a random variable, x, is normally distributed, you can find the probability.
Chapter 3: Maximum-Likelihood Parameter Estimation l Introduction l Maximum-Likelihood Estimation l Multivariate Case: unknown , known  l Univariate.
1 1 Slide © 2007 Thomson South-Western. All Rights Reserved Chapter 8 Interval Estimation Population Mean:  Known Population Mean:  Known Population.
PERT/Activity Diagrams, Completion Probability and the Z Score
Review Normal Distributions –Draw a picture. –Convert to standard normal (if necessary) –Use the binomial tables to look up the value. –In the case of.
Confidence Intervals for a Population Mean, Standard Deviation Unknown.
Term 1 Week 7 Warm Ups. Warm Up 9/22/14 1.Students were asked to measure the width of their desks in centimeters. Identify the outlier, and describe how.
STA 2023 Section 5.4 Sampling Distributions and the Central Limit Theorem.
Central Limit Theorem Let X 1, X 2, …, X n be n independent, identically distributed random variables with mean  and standard deviation . For large n:
Example A population has a mean of 200 and a standard deviation of 50. A random sample of size 100 will be taken and the sample mean x̄ will be used to.
Introduction to Micro-economics Technote Creating a distribution; calculating a probability.
Bayesian Learning. Bayes Classifier A probabilistic framework for solving classification problems Conditional Probability: Bayes theorem:
Advanced Math Topics 9.2/9.3 Estimating the Population Mean From a Large Sample.
Text Classification and Naïve Bayes Formalizing the Naïve Bayes Classifier.
Estimation and Confidence Intervals
Estimating Population Means (Large Samples)
Chapter 3: Maximum-Likelihood Parameter Estimation
Sampling Distributions
Assistant Professor of Public Policy
Sec. 7-5: Central Limit Theorem
Monthly utility bills factor into local cost of living measures
CH 5: Multivariate Methods
Chapter Six Normal Curves and Sampling Probability Distributions
Normal Distribution Many things closely follow a Normal Distribution:
Classification Techniques: Bayesian Classification
Degrees of Freedom The number of degrees of freedom, n, equal the number of data points, N, minus the number of independent restrictions (constraints),
How to Find Data Values (X) Given Specific Probabilities
The Normal Probability Distribution Summary
Candy Machine.
Sampling Distribution
Sampling Distribution
Introduction to Probability & Statistics The Central Limit Theorem
SWBAT: Review sampling distributions of sample proportions and means
Year-3 The standard deviation plus or minus 3 for 99.2% for year three will cover a standard deviation from to To calculate the normal.
Chapter 3: Averages and Variation
Sampling Distributions
CHAPTER 15 SUMMARY Chapter Specifics
Sampling Distribution of the Mean
If the question asks: “Find the probability if...”
7.3 Sample Means HW: p. 454 (49-63 odd, 65-68).
Multivariate Methods Berlin Chen
1.7.2 Multinomial Naïve Bayes
Logistic Regression [Many of the slides were originally created by Prof. Dan Jurafsky from Stanford.]
The Normal Distribution
Presentation transcript:

Assuming normally distributed data! Naïve Bayes Classifier

Training Estimate class priors The number of members of each class over the total number of trials Estimate class conditional means For each feature, average over feature values of each class Estimate class conditional standard deviations For each feature, standard deviation of each feature for each class

Testing For each new sample, using normal distribution for each class Compute the probability of being member of each class by adding prior to the above probability.