INFM 718A / LBSC 705 Information For Decision Making Lecture 7.

Slides:



Advertisements
Similar presentations
Probability How likely is an event to occur?
Advertisements

Lecture 13 Elements of Probability CSCI – 1900 Mathematics for Computer Science Fall 2014 Bill Pine.
Lecture 4A: Probability Theory Review Advanced Artificial Intelligence.
1 Probability Theory Dr. Deshi Ye
Probability.
© 2003 Prentice-Hall, Inc.Chap 4-1 Basic Probability IE 440 PROCESS IMPROVEMENT THROUGH PLANNED EXPERIMENTATION Dr. Xueping Li University of Tennessee.
PROBABILITY. SIMULATION THE ACT OF IMITATING AN ACTUAL EVENT, CONDITION, OR SITUATION. WE OFTEN USE SIMULATIONS TO MODEL EVENTS THAT ARE TOO LARGE OR.
Probability Simple Events
Section 5.1 and 5.2 Probability
Unit 4 Sections 4-1 & & 4-2: Sample Spaces and Probability  Probability – the chance of an event occurring.  Probability event – a chance process.
1 Introduction to Stochastic Models GSLM Outline  course outline course outline  Chapter 1 of the textbook.
Chapter 4 Using Probability and Probability Distributions
Probability and Statistics Dr. Saeid Moloudzadeh Sample Space and Events 1 Contents Descriptive Statistics Axioms of Probability Combinatorial.
Business Statistics: A Decision-Making Approach, 7e © 2008 Prentice-Hall, Inc. Chap 4-1 Business Statistics: A Decision-Making Approach 7 th Edition Chapter.
Pertemuan 03 Peluang Kejadian
Basic Probability. Theoretical versus Empirical Theoretical probabilities are those that can be determined purely on formal or logical grounds, independent.
INFM 718A / LBSC 705 Information For Decision Making Lecture 6.
Chapter 4 Basic Probability
Uncertainty in Life.
Visualizing Events Contingency Tables Tree Diagrams Ace Not Ace Total Red Black Total
Statistics for Managers Using Microsoft Excel, 5e © 2008 Pearson Prentice-Hall, Inc.Chap 4-1 Statistics for Managers Using Microsoft® Excel 5th Edition.
INFM 718A / LBSC 705 Information For Decision Making Lecture 8.
Formal Probability. The sum of probabilities for all possible outcomes of a trial must equal 1. Example: Flipping a Coin S = {Heads, Tails} P(H) = 0.5.
Section 4.3 The Addition Rules for Probability
The Laws of Probability As They Pertain To Genetics.
Probability and Probability Distributions
5.1 Basic Probability Ideas
A Survey of Probability Concepts Chapter 5. 2 GOALS 1. Define probability. 2. Explain the terms experiment, event, outcome, permutations, and combinations.
© 2003 Prentice-Hall, Inc.Chap 4-1 Business Statistics: A First Course (3 rd Edition) Chapter 4 Basic Probability.
“PROBABILITY” Some important terms Event: An event is one or more of the possible outcomes of an activity. When we toss a coin there are two possibilities,
Topics to be covered: Produce all combinations and permutations of sets. Calculate the number of combinations and permutations of sets of m items taken.
Simple Mathematical Facts for Lecture 1. Conditional Probabilities Given an event has occurred, the conditional probability that another event occurs.
AP STATISTICS Section 6.2 Probability Models. Objective: To be able to understand and apply the rules for probability. Random: refers to the type of order.
Special Topics. General Addition Rule Last time, we learned the Addition Rule for Mutually Exclusive events (Disjoint Events). This was: P(A or B) = P(A)
BA 201 Lecture 6 Basic Probability Concepts. Topics Basic Probability Concepts Approaches to probability Sample spaces Events and special events Using.
Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 4-1 Chapter 4 Basic Probability Business Statistics: A First Course 5 th Edition.
Section 7.2. Section Summary Assigning Probabilities Probabilities of Complements and Unions of Events Conditional Probability Independence Bernoulli.
A Survey of Probability Concepts
Statistics & Econometrics Statistics & Econometrics Statistics & Econometrics Statistics & Econometrics Statistics & Econometrics Statistics & Econometrics.
PROBABILITY, PROBABILITY RULES, AND CONDITIONAL PROBABILITY
Chapter 6. Probability What is it? -the likelihood of a specific outcome occurring Why use it? -rather than constantly repeating experiments to make sure.
Section 6.2 Probability Models. Sample Space The sample space S of a random phenomenon is the set of all possible outcomes. For a flipped coin, the sample.
1 1 Slide © 2007 Thomson South-Western. All Rights Reserved Chapter 4 Introduction to Probability Experiments, Counting Rules, and Assigning Probabilities.
Probability Definition : The probability of a given event is an expression of likelihood of occurrence of an event.A probability isa number which ranges.
How likely is it that…..?. The Law of Large Numbers says that the more times you repeat an experiment the closer the relative frequency of an event will.
Statistics Lecture Notes Dr. Halil İbrahim CEBECİ Chapter 05 Probability.
What is the probability of two or more independent events occurring?
Probability Concepts. 2 GOALS 1. Define probability. 2. Explain the terms experiment, event, outcome, permutations, and combinations. 3. Define the terms.
Unit 4 Section 3.1.
Probability Any event occurring as a result of a random experiment will usually be denoted by a capital letter from the early part of the alphabet. e.g.
PROBABILITY AND BAYES THEOREM 1. 2 POPULATION SAMPLE PROBABILITY STATISTICAL INFERENCE.
Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 4-1 Chapter 4 Basic Probability Business Statistics: A First Course 5 th Edition.
When a normal, unbiased, 6-sided die is thrown, the numbers 1 to 6 are possible. These are the ONLY ‘events’ possible. This means these are EXHAUSTIVE.
PROBABILITY 1. Basic Terminology 2 Probability 3  Probability is the numerical measure of the likelihood that an event will occur  The probability.
Statistics for Managers 5th Edition
Section Probability Models AP Statistics December 2, 2010.
LEQ: What are the basic rules of probability? 9.7.
Probability Lesson 1 Aims:
Introduction to Probability
Probability 100% 50% 0% ½ Will happen Won’t happen
Basic Business Statistics (8th Edition)
AND.
PROBABILITY The probability of an event is a value that describes the chance or likelihood that the event will happen or that the event will end with.
PROBABILITY.
Probability Trees By Anthony Stones.
Section 6.2 Probability Models
Elementary Statistics 8th Edition
Multi-Stage Events and Applications of Probability
A random experiment gives rise to possible outcomes, but any particular outcome is uncertain – “random”. For example, tossing a coin… we know H or T will.
Presentation transcript:

INFM 718A / LBSC 705 Information For Decision Making Lecture 7

Probability Likelihood that [an outcome] will occur when the uncertainty [related to it] is resolved. We are interested in objective uncertainty in this lecture. Outcomes need to be mutually exclusive and collectively exhaustive.

Examples Flipping a coin: 2 possible outcomes (heads or tails), with equal likelihoods, each with a probability of 1/2. Throwing a die: 6 possible outcomes (1,2, 3, 4, 5, 6), with equal likelihoods, each with a probability of 1/6. P (even) = 1/2 P (>2) = 4/6

Disjoint and Independent Events Disjoint events: Two (or more) events with no common outcomes. E.g.: P (2 and odd). Independent events: Two (or more) events, where knowing the outcome of one event will not provide any information about the probability of the other event. E.g.: Throwing a die and flipping a coin together.

Joint Probability Probability that two independent events will occur together. E.g.: Throw a dice, flip a coin, what is the probability that the die shows 1 and the coin shows heads. P (1 and H) = 1/6 * 1/2 =1/12

Conditional Probability Probability of an outcome, under the condition that another, dependent event has had a certain outcome. E.g.: Probability that the die shows 1, based on the information that it shows an odd number. P (1 | odd).

Bayes’ Theorem

AB

In-Class Exercises a) You can use different approaches: –Enumerate all possible outcomes –Use a numeric approach 4.1.b) Use a combination of approaches 4.1.c) Relate to the previous question, use Bayes’ Theorem

Probability Trees A type of visual model to represent joint probabilities. E.g.: A woman has two children. What is the probability that both children are boys?

Probability Trees First child is boy First child is girl ½ ½ Second child is boy Second child is girl ¼ ¼ ¼ ¼ Both children are boys

Probability Trees In-class Exercise 4.2.

Joint Probabilities Textbook example (pp ) –Blood test In-class Exercise 4.3.

Decision Trees Textbook example (pp ) –Medfly eradication Install TreePlan –BlackBoard > Course Documents > Add-Ins –Download and unzip the file –Open treeplan.xla in Excel In-class Exercise 5.1.