Conditional Probability, Independence, Bayes Theorem Spring January 1, / 23

Similar documents
Conditional Probability, Independence, Bayes Theorem Spring January 1, / 28

Conditional Probability, Independence, Bayes Theorem Spring 2018

Conditional Probability, Independence and Bayes Theorem Class 3, Jeremy Orloff and Jonathan Bloom

Comparison of Bayesian and Frequentist Inference

Probabilistic models

P (A B) P ((B C) A) P (B A) = P (B A) + P (C A) P (A) = P (B A) + P (C A) = Q(A) + Q(B).

Bayesian Updating: Discrete Priors: Spring 2014

18.05 Problem Set 5, Spring 2014 Solutions

2.4 Conditional Probability

Lecture 3. January 7, () Lecture 3 January 7, / 35

Vina Nguyen HSSP July 6, 2008

EE126: Probability and Random Processes

Lecture 5: Introduction to Probability

the time it takes until a radioactive substance undergoes a decay

Probabilistic models

Lecture 2. Conditional Probability

18.05 Final Exam. Good luck! Name. No calculators. Number of problems 16 concept questions, 16 problems, 21 pages

Probability: Part 1 Naima Hammoud

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 10

Intermediate Math Circles November 15, 2017 Probability III

6.041/6.431 Spring 2009 Quiz 1 Wednesday, March 11, 7:30-9:30 PM. SOLUTIONS

Discrete Mathematics and Probability Theory Spring 2016 Rao and Walrand Note 14

13-5 Probabilities of Independent and Dependent Events

Probability and Independence Terri Bittner, Ph.D.

Chapter 1 Principles of Probability

Great Theoretical Ideas in Computer Science

CS 361: Probability & Statistics

Conditional probability

Probability deals with modeling of random phenomena (phenomena or experiments whose outcomes may vary)

Some Basic Concepts of Probability and Information Theory: Pt. 1

ORF 245 Fundamentals of Statistics Chapter 5 Probability

Formalizing Probability. Choosing the Sample Space. Probability Measures

Class 8 Review Problems 18.05, Spring 2014

MAT 271E Probability and Statistics

Class 26: review for final exam 18.05, Spring 2014

Exam 2 Practice Questions, 18.05, Spring 2014

Lecture 1. ABC of Probability

1. When applied to an affected person, the test comes up positive in 90% of cases, and negative in 10% (these are called false negatives ).

Conditional Probability. CS231 Dianna Xu

STAT:5100 (22S:193) Statistical Inference I

STA Module 4 Probability Concepts. Rev.F08 1

LECTURE 1. 1 Introduction. 1.1 Sample spaces and events

Introduction to Probability 2017/18 Supplementary Problems

PROBABILITY: AN INTERACTIVE REVIEW, II CS1951A INTRO TO DATA SCIENCE DAN POTTER

2. Conditional Probability

Name: Exam 2 Solutions. March 13, 2017

COS 424: Interacting with Data. Lecturer: Dave Blei Lecture #2 Scribe: Ellen Kim February 7, 2008

Tutorial 3: Random Processes

Confidence Intervals for Normal Data Spring 2014

Practice Exam 1: Long List 18.05, Spring 2014

Monty Hall Puzzle. Draw a tree diagram of possible choices (a possibility tree ) One for each strategy switch or no-switch

STAT509: Probability

Probability- describes the pattern of chance outcomes

2. AXIOMATIC PROBABILITY

Compute f(x θ)f(θ) dθ

Chapter 7 Wednesday, May 26th

3.2 Probability Rules

Discrete Probability

Lectures Conditional Probability and Independence

Outline Conditional Probability The Law of Total Probability and Bayes Theorem Independent Events. Week 4 Classical Probability, Part II

Stat 225 Week 2, 8/27/12-8/31/12, Notes: Independence and Bayes Rule

Math 493 Final Exam December 01

Tutorial 2: Probability

Lecture Lecture 5

Axiomatic Foundations of Probability. Definition: Probability Function

Denker FALL Probability- Assignment 6

Mathematical Foundations of Computer Science Lecture Outline October 18, 2018

Introduction to Probability Theory, Algebra, and Set Theory

18.05 Practice Final Exam

ELEG 3143 Probability & Stochastic Process Ch. 1 Probability

Lecture Notes 1 Basic Probability. Elements of Probability. Conditional probability. Sequential Calculation of Probability

Exam 1 Practice Questions I solutions, 18.05, Spring 2014

Probability, Statistics, and Bayes Theorem Session 3

Independence Solutions STAT-UB.0103 Statistics for Business Control and Regression Models

Chapter 14. From Randomness to Probability. Copyright 2012, 2008, 2005 Pearson Education, Inc.

Conditional Probability

Final Examination. Adrian Georgi Josh Karen Lee Min Nikos Tina. There are 12 problems totaling 150 points. Total time is 170 minutes.

Chapter 2. Conditional Probability and Independence. 2.1 Conditional Probability

First Digit Tally Marks Final Count

Statistika pro informatiku

MATH 3510: PROBABILITY AND STATS July 1, 2011 FINAL EXAM

Lecture 9. Expectations of discrete random variables

Mutually Exclusive Events

Ch 14 Randomness and Probability

ORF 245 Fundamentals of Statistics Chapter 1 Probability

Elementary Discrete Probability

Conditional Probability

Probability: Terminology and Examples Class 2, Jeremy Orloff and Jonathan Bloom

Is this a fair game? Great Expectations. win $1 for each match lose $1 if no match

With Question/Answer Animations. Chapter 7

Probability and Statistics Notes

Class 8 Review Problems solutions, 18.05, Spring 2014

Lecture 04: Conditional Probability. Lisa Yan July 2, 2018

4 Lecture 4 Notes: Introduction to Probability. Probability Rules. Independence and Conditional Probability. Bayes Theorem. Risk and Odds Ratio

Chapter 6: Probability The Study of Randomness

Topic 5: Probability. 5.4 Combined Events and Conditional Probability Paper 1

Chapter 2 Class Notes

Presentation on Theo e ry r y o f P r P o r bab a il i i l t i y

STAT 201 Chapter 5. Probability

Uncertainty. Russell & Norvig Chapter 13.

Transcription:

Conditional Probability, Independence, Bayes Theorem 18.05 Spring 2014 January 1, 2017 1 / 23

Sample Space Confusions 1. Sample space = set of all possible outcomes of an experiment. 2. The size of the set is NOT the sample space. 3. Outcomes can be sequences of numbers. Examples. 1. Roll 5 dice: Ω = set of all sequences of 5 numbers between 1 and 6, e.g. (1, 2, 1, 3, 1, 5) Ω. The size Ω = 6 5 is not a set. 2. Ω = set of all sequences of 10 birthdays, e.g. (111, 231, 3, 44, 55, 129, 345, 14, 24, 14) Ω. Ω = 365 10 3. n some number, Ω = set of all sequences of n birthdays. Ω = 365 n. January 1, 2017 2 / 23

Conditional Probability the probability of A given B. P(A B) P(A B) =, provided P(B) = 0. P(B) B A B A Conditional probability: Abstractly and for coin example January 1, 2017 3 / 23

Table/Concept Question (Work with your tablemates, then everyone click in the answer.) Toss a coin 4 times. Let A = at least three heads B = first toss is tails. 1. What is P(A B)? (a) 1/16 (b) 1/8 (c) 1/4 (d) 1/5 2. What is P(B A)? (a) 1/16 (b) 1/8 (c) 1/4 (d) 1/5 January 1, 2017 4 / 23

Table Question Steve is very shy and withdrawn, invariably helpful, but with little interest in people, or in the world of reality. A meek and tidy soul, he has a need for order and structure and a passion for detail. What is the probability that Steve is a librarian? What is the probability that Steve is a farmer? From Judgment under uncertainty: heuristics and biases by Tversky and Kahneman. January 1, 2017 5 / 23

Multiplication Rule, Law of Total Probability Multiplication rule: P(A B) = P(A B) P(B). Law of total probability: If B 1, B 2, B 3 partition Ω then P(A) = P(A B 1 ) + P(A B 2 ) + P(A B 3 ) = P(A B 1 )P(B 1 ) + P(A B 2 )P(B 2 ) + P(A B 3 )P(B 3 ) Ω B 1 A B 1 A B 2 A B 3 B 2 B 3 January 1, 2017 6 / 23

Trees Organize computations Compute total probability Compute Bayes formula Example. : Game: 5 red and 2 green balls in an urn. A random ball is selected and replaced by a ball of the other color; then a second ball is drawn. 1. What is the probability the second ball is red? 2. What is the probability the first ball was red given the second ball was red? First draw Second draw 5/7 2/7 R 1 G 1 4/7 3/7 6/7 1/7 R 2 G 2 R 2 G 2 January 1, 2017 7 / 23

Concept Question: Trees 1 y A 1 A 2 z x B 1 B 2 B 1 B 2 C 1 C 2 C 1 C 2 C 1 C 2 C 1 C 2 1. The probability x represents (a) P(A 1 ) (b) P(A 1 B 2 ) (c) P(B 2 A 1 ) (d) P(C 1 B 2 A 1 ). January 1, 2017 8 / 23

Concept Question: Trees 2 y A 1 A 2 z x B 1 B 2 B 1 B 2 C 1 C 2 C 1 C 2 C 1 C 2 C 1 C 2 2. The probability y represents (a) P(B 2 ) (b) P(A 1 B 2 ) (c) P(B 2 A 1 ) (d) P(C 1 B 2 A 1 ). January 1, 2017 9 / 23

Concept Question: Trees 3 y A 1 A 2 z x B 1 B 2 B 1 B 2 C 1 C 2 C 1 C 2 C 1 C 2 C 1 C 2 3. The probability z represents (a) P(C 1 ) (b) P(B 2 C 1 ) (c) P(C 1 B 2 ) (d) P(C 1 B 2 A 1 ). January 1, 2017 10 / 23

Concept Question: Trees 4 y A 1 A 2 z x B 1 B 2 B 1 B 2 C 1 C 2 C 1 C 2 C 1 C 2 C 1 C 2 4. The circled node represents the event (a) C 1 (b) B 2 C 1 (c) A 1 B 2 C 1 (d) C 1 B 2 A 1. January 1, 2017 11 / 23

Let s Make a Deal with Monty Hall One door hides a car, two hide goats. The contestant chooses any door. Monty always opens a different door with a goat. (He can do this because he knows where the car is.) The contestant is then allowed to switch doors if she wants. What is the best strategy for winning a car? (a) Switch (b) Don t switch (c) It doesn t matter January 1, 2017 12 / 23

Board question: Monty Hall Organize the Monty Hall problem into a tree and compute the probability of winning if you always switch. Hint first break the game into a sequence of actions. January 1, 2017 13 / 23

Independence Events A and B are independent if the probability that one occurred is not affected by knowledge that the other occurred. Independence P(A B) = P(A) (provided P(B) 0) P(B A) = P(B) (provided P(A) 0) (For any A and B) P(A B) = P(A)P(B) January 1, 2017 14 / 23

Table/Concept Question: Independence (Work with your tablemates, then everyone click in the answer.) Roll two dice and consider the following events A = first die is 3 B = sum is 6 C = sum is 7 A is independent of (a) B and C (b) B alone (c) C alone (d) Neither B or C. January 1, 2017 15 / 23

Bayes Theorem Also called Bayes Rule and Bayes Formula. Allows you to find P(A B) from P(B A), i.e. to invert conditional probabilities. P(A B) = P(B A) P(A) P(B) Often compute the denominator P(B) using the law of total probability. January 1, 2017 16 / 23

Board Question: Evil Squirrels Of the one million squirrels on MIT s campus most are good-natured. But one hundred of them are pure evil! An enterprising student in Course 6 develops an Evil Squirrel Alarm which she offers to sell to MIT for a passing grade. MIT decides to test the reliability of the alarm by conducting trials. January 1, 2017 17 / 23

Evil Squirrels Continued When presented with an evil squirrel, the alarm goes off 99% of the time. When presented with a good-natured squirrel, the alarm goes off 1% of the time. (a) If a squirrel sets off the alarm, what is the probability that it is evil? (b) Should MIT co-opt the patent rights and employ the system? January 1, 2017 18 / 23

One solution (This is a base rate fallacy problem) We are given: P(nice) = 0.9999, P(evil) = 0.0001 (base rate) P(alarm nice) = 0.01, P(alarm evil) = 0.99 P(evil alarm) = P(alarm evil)p(evil) P(alarm) P(alarm evil)p(evil) = P(alarm evil)p(evil) + P(alarm nice)p(nice) (0.99)(0.0001) = (0.99)(0.0001) + (0.01)(0.9999) 0.01 January 1, 2017 19 / 23

Squirrels continued Summary: Probability a random test is correct = 0.99 Probability a positive test is correct 0.01 These probabilities are not the same! Alternative method of calculation: Evil Nice Alarm 99 9999 10098 No alarm 1 989901 989902 100 999900 1000000 January 1, 2017 20 / 23

Washington Post, hot off the press Annual physical exam is probably unnecessary if you re generally healthy For patients, the negatives include time away from work and possibly unnecessary tests. Getting a simple urinalysis could lead to a false positive, which could trigger a cascade of even more tests, only to discover in the end that you had nothing wrong with you. Mehrotra says. http://www.washingtonpost.com/national/health-science/ annual-physical-exam-is-probably-unnecessary-if-youre-generally 2013/02/08/2c1e326a-5f2b-11e2-a389-ee565c81c565_story.html January 1, 2017 21 / 23

Table Question: Dice Game 1 2 3 The Randomizer holds the 6-sided die in one fist and the 8-sided die in the other. The Roller selects one of the Randomizer s fists and covertly takes the die. The Roller rolls the die in secret and reports the result to the table. Given the reported number, what is the probability that the 6-sided die was chosen? (Find the probability for each possible reported number.) January 1, 2017 22 / 23

MIT OpenCourseWare https://ocw.mit.edu 18.05 Introduction to Probability and Statistics Spring 2014 For information about citing these materials or our Terms of Use, visit: https://ocw.mit.edu/terms.