PROBABILITY REVIEW PART 4 PROBABILITY FOR TEXT ANALYTICS Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.

Slides:



Advertisements
Similar presentations
1 1 Slide Introduction to Probability Probability Arithmetic and Conditional Probability Chapter 4 BA 201.
Advertisements

Unit 7: Probability Lesson 1
Chapter 4 Probability and Probability Distributions
Probability Probability involves the measurement of the uncertainty that the decision maker feels about the occurrence of some outcome.
©Brooks/Cole, 2001 Chapter 2 Introduction to The C Language.
Chapter 4 Probability.
©Brooks/Cole, 2001 Chapter 7 Text Files. ©Brooks/Cole, 2001 Figure 7-1.
1 Definitions Experiment – a process by which an observation ( or measurement ) is observed Sample Space (S)- The set of all possible outcomes (or results)
CSC482 INTRODUCTION TO TEXT ANALYTICS COURSE INTRODUCTION: PART ONE Thomas Tiahrt, MA, PhD.
PROBABILITY REVIEW PART 9 CONDITIONAL PROBABILITY II Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
MapR – HADOOP DEVELOPMENT IN A VIRTUAL MACHINE Thomas Tiahrt, MA, PhD CSC482 Introduction to Text Analytics.
INFORMATION THEORY BAYESIAN STATISTICS I Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
INFORMATION THEORY BAYESIAN STATISTICS II Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
PROBABILITY REVIEW PART 5 PROBABILITY FOR TEXT ANALYTICS Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
INTRODUCTION TO PYTHON PART 2 INPUT AND OUTPUT CSC482 Introduction to Text Analytics Thomas Tiahrt, MA, PhD.
TEXT CATEGORIZATION THE FEDERALIST - PART 2 Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
TEXT CATEGORIZATION THE FEDERALIST – PART 1 Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
Set, Combinatorics, Probability & Number Theory Mathematical Structures for Computer Science Chapter 3 Copyright © 2006 W.H. Freeman & Co.MSCS Slides Set,
INFORMATION RETRIEVAL VECTOR SPACE MODEL IN-DEPTH PART 3 Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
INFORMATION RETRIEVAL VECTOR SPACE MODEL IN-DEPTH PART 2 Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
Probability Rules l Rule 1. The probability of any event (A) is a number between zero and one. 0 < P(A) < 1.
Section 5.2 The Addition Rule and Complements
COURSE OVERVIEW ADVANCED TEXT ANALYTICS Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
INFORMATION RETRIEVAL LINEAR ALGEBRA REVIEW Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
MA-250 Probability and Statistics Nazar Khan PUCIT Lecture 10.
TEXT CATEGORIZATION THE FEDERALIST – PART 3 Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
INFORMATION RETRIEVAL VECTOR SPACE MODEL IN-DEPTH PART 1 Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
INFORMATION RETRIEVAL VECTOR SPACE MODEL IN-DEPTH PART 5 Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
INFORMATION THEORY CONDITIONAL ENTROPY Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
INFORMATION THEORY SIMPLIFIED POLYNESIAN LANGUAGE EXAMPLE Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
PROBABILITY REVIEW PART 2 PROBABILITY FOR TEXT ANALYTICS Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
The probable is what usually happens. Aristotle Chapter 6: Probability.
Course overview Course title: Discrete mathematics for Computer Science Instructors: Dr. Abdelouahid Derhab Credit.
CSC Discrete Mathematical Structures Dr. Karl Ricanek Jr.
Random Experiment Random Variable: Continuous, Discrete Sample Space: S Event: A, B, E Null Event Complement of an Event A’ Union of Events (either, or)
1 1 Slide © 2016 Cengage Learning. All Rights Reserved. Probability is a numerical measure of the likelihood Probability is a numerical measure of the.
1 Discrete Structures – CNS2300 Text Discrete Mathematics and Its Applications (5 th Edition) Kenneth H. Rosen Chapter 5 Counting.
Chapter 4 Probability ©. Sample Space sample space.S The possible outcomes of a random experiment are called the basic outcomes, and the set of all basic.
Sample space the collection of all possible outcomes of a chance experiment –Roll a dieS={1,2,3,4,5,6}
GOOGLE N-GRAMS ON AMAZON WEB SERVICES PART 2 Thomas Tiahrt, MA, PhD Computer Science 482 – Introduction to Text Analytics.
26134 Business Statistics Tutorial 7: Probability Key concepts in this tutorial are listed below 1. Construct contingency table.
INFORMATION THEORY POLYNESIAN REVISITED Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
Statistics 3502/6304 Prof. Eric A. Suess Chapter 4.
1 1 Slide Introduction to Probability Assigning Probabilities and Probability Relationships Chapter 4 BA 201.
Sixth lecture Concepts of Probabilities. Random Experiment Can be repeated (theoretically) an infinite number of times Has a well-defined set of possible.
Dongfang Xu School of Information
This is a discrete distribution. Situations that can be modeled with the binomial distribution must have these 4 properties: Only two possible outcomes.
Chapter 10 – Data Analysis and Probability 10.7 – Probability of Compound Events.
Warm UP 1.Which pair of events are mutually exclusive: a.Has ridden a roller coaster, has ridden a ferris wheel b.Owns a classical music CD, owns a jazz.
THE MATHEMATICAL STUDY OF RANDOMNESS. SAMPLE SPACE the collection of all possible outcomes of a chance experiment  Roll a dieS={1,2,3,4,5,6}
1 Discrete Structures - CSIS2070 Text Discrete Mathematics and Its Applications Kenneth H. Rosen Chapter 4 Counting.
STATISTICS 6.0 Conditional Probabilities “Conditional Probabilities”
AP Statistics Monday, 09 November 2015 OBJECTIVE TSW investigate the basics of probability. ASSIGNMENTS DUE (wire basket) –WS Counting Principle #1 TODAY’S.
Probability I.
Probability.
Probability I.
Chapter 4 Probability.
Statistics 300: Introduction to Probability and Statistics
Probability I.
Counting and Probability Section 12.1: Sets and Counting IBTWW…
Set, Combinatorics, Probability & Number Theory
Probability I.
Probability I.
Chapter Sets &Venn Diagrams.
Probability I.
The probable is what usually happens. Aristotle
Probability Rules Rule 1.
Counting to 100 Counting by ones

Business and Economics 7th Edition
Presentation transcript:

PROBABILITY REVIEW PART 4 PROBABILITY FOR TEXT ANALYTICS Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics

Counting for Probability Computation 2

3

Kinds of Probability 4  Five categories of probabilities  Marginal probabilities  Probability of a complement  Joint (Intersection) probabilities  Union probabilities  Conditional probabilities

Eight-Sided Die 5  Eight faces on each die  Dots on a die are called ‘pips’

Imaginary Eight-Sided Die 6

Marginal Probabilities 7 OutcomeProbability {1}1/8 {2}1/8 {3}1/8 {4}1/8 {5}1/8 {6}1/8 {7}1/8 {8}1/8

Examples of Marginal Probabilities 8

Complementary Probabilities 9 Outcome {1} {2} {3} {4} {5} {6} {7} {8}

Complementary Examples 10

References 11 Sources: Foundations of Statistical Natural Language Processing, by Christopher Manning and Hinrich Schütze The MIT Press Discrete Mathematics with Applications, by Susanna S. Epp Brooks/Cole, Cengage Learning

The end has come. End of Probability Slides Part 4 12