PROBABILITY REVIEW PART 2 PROBABILITY FOR TEXT ANALYTICS Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.

Slides:



Advertisements
Similar presentations
CORE 1 UNIT 8 Patterns of Chance
Advertisements

CORE 1 Patterns in Chance. Daily Starter Begin Handout.
MA 102 Statistical Controversies Monday, April 1, 2002 Today: Randomness and probability Probability models and rules Reading (for Wednesday): Chapter.
Samples vs. Distributions Distributions: Discrete Random Variable Distributions: Continuous Random Variable Another Situation: Sample of Data.
PROBABILITY REVIEW PART 9 CONDITIONAL PROBABILITY II Thomas Tiahrt, MA, PhD CSC492 – Advanced 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 4 PROBABILITY FOR TEXT ANALYTICS Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
PROBABILITY REVIEW PART 5 PROBABILITY FOR TEXT ANALYTICS Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
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.
AP STATISTICS.   Theoretical: true mathematical probability  Empirical: the relative frequency with which an event occurs in a given experiment  Subjective:
This is a discrete distribution. Poisson is French for fish… It was named due to one of its uses. For example, if a fish tank had 260L of water and 13.
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.
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.
Probability Distributions. A sample space is the set of all possible outcomes in a distribution. Distributions can be discrete or continuous.
Sets, Combinatorics, Probability, and Number Theory Mathematical Structures for Computer Science Chapter 3 Copyright © 2006 W.H. Freeman & Co.MSCS SlidesProbability.
Sets, Combinatorics, Probability, and Number Theory Mathematical Structures for Computer Science Chapter 3 Copyright © 2006 W.H. Freeman & Co.MSCS SlidesProbability.
L7.1b Continuous Random Variables CONTINUOUS RANDOM VARIABLES NORMAL DISTRIBUTIONS AD PROBABILITY DISTRIBUTIONS.
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.
CSC Discrete Mathematical Structures Dr. Karl Ricanek Jr.
Advanced Higher Statistics Data Analysis and Modelling Hypothesis Testing Statistical Inference AH.
4.1 Probability Distributions. Do you remember? Relative Frequency Histogram.
Discrete Math 6-1 Copyright © Genetic Computer School 2007 Lesson 6 Probability.
Lecture PowerPoint Slides Basic Practice of Statistics 7 th Edition.
Computer Vision Group Prof. Daniel Cremers Autonomous Navigation for Flying Robots Lecture 5.2: Recap on Probability Theory Jürgen Sturm Technische Universität.
Math b (Discrete) Random Variables, Binomial Distribution.
Appendix : Probability Theory Review Each outcome is a sample point. The collection of sample points is the sample space, S. Sample points can be aggregated.
Lecture V Probability theory. Lecture questions Classical definition of probability Frequency probability Discrete variable and probability distribution.
4.1 Probability Distributions Important Concepts –Random Variables –Probability Distribution –Mean (or Expected Value) of a Random Variable –Variance and.
Slide 5-1 Chapter 5 Probability and Random Variables.
Essential Statistics Chapter 91 Introducing Probability.
CHAPTER 10 Introducing Probability BPS - 5TH ED.CHAPTER 10 1.
Chapter 10 Introducing Probability BPS - 5th Ed. Chapter 101.
INFORMATION THEORY POLYNESIAN REVISITED Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
Statistics 3502/6304 Prof. Eric A. Suess Chapter 4.
Probability Definition : The probability of a given event is an expression of likelihood of occurrence of an event.A probability isa number which ranges.
8-3: Probability and Probability Distributions English Casbarro Unit 8.
1 7.3 RANDOM VARIABLES When the variables in question are quantitative, they are known as random variables. A random variable, X, is a quantitative variable.
11.7 Continued Probability. Independent Events ► Two events are independent if the occurrence of one has no effect on the occurrence of the other ► Probability.
MATH Section 3.1.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter 5 Discrete Random Variables.
Statistics and Probability Theory Lecture 12 Fasih ur Rehman.
Chapter IV Statistic and simple probability. A. Meaning of probability 1.Random event 2.Simple event 3.Relative frequent and probability of an event Relative.
Copyright ©2011 Brooks/Cole, Cengage Learning Random Variables Class 34 1.
Spring 2016 COMP 2300 Discrete Structures for Computation Donghyun (David) Kim Department of Mathematics and Physics North Carolina Central University.
Random Variables By: 1.
Unit 3: Probability.  You will need to be able to describe how you will perform a simulation  Create a correspondence between random numbers and outcomes.
Basic Practice of Statistics - 3rd Edition Introducing Probability
Advanced Higher Statistics
Graduate School of Information Sciences, Tohoku University
Applied Discrete Mathematics Week 7: Probability Theory
AP Statistics: Chapter 7
Probability and Statistics
©G Dear 2009 – Not to be sold/Free to use
Probability & Statistics Probability Theory Mathematical Probability Models Event Relationships Distributions of Random Variables Continuous Random.
Lecture Slides Elementary Statistics Twelfth Edition
Basic Practice of Statistics - 3rd Edition Introducing Probability
6.2/6.3 Probability Distributions and Distribution Mean
Basic Practice of Statistics - 3rd Edition Introducing Probability
Statistics Lecture 12.
ASV Chapters 1 - Sample Spaces and Probabilities
7.1: Discrete and Continuous Random Variables
Essential Statistics Introducing Probability
Basic Practice of Statistics - 5th Edition Introducing Probability
Sets, Combinatorics, Probability, and Number Theory
Presentation transcript:

PROBABILITY REVIEW PART 2 PROBABILITY FOR TEXT ANALYTICS Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics

Random Experiment  Outcome  Result cannot be predicted  But comes from the sample space

Random Variables 3

Events

Probability Theory

Equally Likely Probability Formula

Cardinality Notation

Probabilities 9  Numbers between 0 and 1 inclusive  Probability Distributions  Probability Functions

Probabilities 10

Computing Probability 11

References 12 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 2 13