Lecture 12. Poisson random variables

Similar documents
18.175: Lecture 13 Infinite divisibility and Lévy processes

18.175: Lecture 17 Poisson random variables

Lecture 16. Lectures 1-15 Review

Lecture 14. More discrete random variables

Lecture 18. Uniform random variables

18.440: Lecture 19 Normal random variables

BINOMIAL DISTRIBUTION

Lecture 10. Variance and standard deviation

18.440: Lecture 26 Conditional expectation

18.440: Lecture 28 Lectures Review

Lecture 9. Expectations of discrete random variables

18.440: Lecture 28 Lectures Review

success and failure independent from one trial to the next?

Suppose that you have three coins. Coin A is fair, coin B shows heads with probability 0.6 and coin C shows heads with probability 0.8.

Discrete Distributions

ST 371 (V): Families of Discrete Distributions

(b). What is an expression for the exact value of P(X = 4)? 2. (a). Suppose that the moment generating function for X is M (t) = 2et +1 3

Lecture 23. Random walks

Notes on Continuous Random Variables

Topic 3: The Expectation of a Random Variable

Practice Midterm 2 Partial Solutions

Topic 9 Examples of Mass Functions and Densities

Class 26: review for final exam 18.05, Spring 2014

Poisson approximations

Things to remember when learning probability distributions:

18.175: Lecture 8 Weak laws and moment-generating/characteristic functions

Common Discrete Distributions

Lecture 3. Discrete Random Variables

6 The normal distribution, the central limit theorem and random samples

EE126: Probability and Random Processes

Lecture 2: Discrete Probability Distributions

Special distributions

Lecture 2. Binomial and Poisson Probability Distributions

Random variables. DS GA 1002 Probability and Statistics for Data Science.

Math/Stat 352 Lecture 8

CS 237: Probability in Computing

Massachusetts Institute of Technology

Methods of Mathematics

Lecture 13. Poisson Distribution. Text: A Course in Probability by Weiss 5.5. STAT 225 Introduction to Probability Models February 16, 2014

SDS 321: Introduction to Probability and Statistics

Conditional distributions (discrete case)

6.041/6.431 Fall 2010 Quiz 2 Solutions

p. 4-1 Random Variables

Lecture 7. Bayes formula and independence

Lecture 7. Sums of random variables

Probability Distributions - Lecture 5

Chapter 4 : Discrete Random Variables

Math Bootcamp 2012 Miscellaneous

Guidelines for Solving Probability Problems

I. Introduction to probability, 1

a zoo of (discrete) random variables

18.175: Lecture 14 Infinite divisibility and so forth

Random Variable. Discrete Random Variable. Continuous Random Variable. Discrete Random Variable. Discrete Probability Distribution

Lecture 14. Text: A Course in Probability by Weiss 5.6. STAT 225 Introduction to Probability Models February 23, Whitney Huang Purdue University

Random Variables. Lecture 6: E(X ), Var(X ), & Cov(X, Y ) Random Variables - Vocabulary. Random Variables, cont.

18.440: Lecture 25 Covariance and some conditional expectation exercises

Lecture 2 Binomial and Poisson Probability Distributions

Mathematical Statistics 1 Math A 6330

6.207/14.15: Networks Lecture 4: Erdös-Renyi Graphs and Phase Transitions

Lecture Notes 7 Random Processes. Markov Processes Markov Chains. Random Processes

18.175: Lecture 2 Extension theorems, random variables, distributions

Binomial in the Limit

CSE 103 Homework 8: Solutions November 30, var(x) = np(1 p) = P r( X ) 0.95 P r( X ) 0.

Lecture notes for probability. Math 124

Class 8 Review Problems 18.05, Spring 2014

n px p x (1 p) n x. p x n(n 1)... (n x + 1) x!

S n = x + X 1 + X X n.

Chapter 6 Continuous Probability Distributions

Probability and Statistics

18.05 Exam 1. Table of normal probabilities: The last page of the exam contains a table of standard normal cdf values.

MAKING MONEY FROM FAIR GAMES: EXAMINING THE BOREL-CANTELLI LEMMA

Massachusetts Institute of Technology Department of Electrical Engineering & Computer Science 6.041/6.431: Probabilistic Systems Analysis

What is the probability of a goal in a 3-minute slot? Over 30 3-minute slots, what is the probability of: 0 goals? 1 goal? 2 goals?

Introduction. Probability and distributions

Binomial and Poisson Probability Distributions

Statistics, Probability Distributions & Error Propagation. James R. Graham

Midterm Exam 1 Solution

Moments. Raw moment: February 25, 2014 Normalized / Standardized moment:

STAT 414: Introduction to Probability Theory

Name: Firas Rassoul-Agha

X = X X n, + X 2

Great Theoretical Ideas in Computer Science

STAT 418: Probability and Stochastic Processes

Probability and Statistics Notes

Part IA Probability. Definitions. Based on lectures by R. Weber Notes taken by Dexter Chua. Lent 2015

Central Limit Theorem and the Law of Large Numbers Class 6, Jeremy Orloff and Jonathan Bloom

Stat 134 Fall 2011: Notes on generating functions

BNAD 276 Lecture 5 Discrete Probability Distributions Exercises 1 11

CSE 312: Foundations of Computing II Quiz Section #10: Review Questions for Final Exam (solutions)

(Practice Version) Midterm Exam 2

Bernoulli Trials, Binomial and Cumulative Distributions

Practice Exam 1: Long List 18.05, Spring 2014

1. Let X be a random variable with probability density function. 1 x < f(x) = 0 otherwise

STAT/MA 416 Answers Homework 6 November 15, 2007 Solutions by Mark Daniel Ward PROBLEMS

MATH 3510: PROBABILITY AND STATS June 15, 2011 MIDTERM EXAM

Binomial random variable

Poisson Chris Piech CS109, Stanford University. Piech, CS106A, Stanford University

Stat 100a, Introduction to Probability.

1 Normal Distribution.

Transcription:

18.440: Lecture 12 Poisson random variables Scott Sheffield MIT 1

Outline Poisson random variable definition Poisson random variable properties Poisson random variable problems 2

Outline Poisson random variable definition Poisson random variable properties Poisson random variable problems 3

Poisson random variables: motivating questions How many raindrops hit a given square inch of sidewalk during a ten minute period? How many people fall down the stairs in a major city on a given day? How many plane crashes in a given year? How many radioactive particles emitted during a time period in which the expected number emitted is 5? How many calls to call center during a given minute? How many goals scored during a 90 minute soccer game? How many notable gaffes during 90 minute debate? Key idea for all these examples: Divide time into large number of small increments. Assume that during each increment, there is some small probability of thing happening (independently of other increments). 4

Remember what e is? The number e is defined by e = lim n (1 + 1/n) n. It s the amount of money that one dollar grows to over a year when you have an interest rate of 100 percent, continuously compounded. Similarly, e λ = lim n (1 + λ/n) n. It s the amount of money that one dollar grows to over a year when you have an interest rate of 100λ percent, continuously compounded. It s also the amount of money that one dollar grows to over λ years when you have an interest rate of 100 percent, continuously compounded. Can also change sign: e λ = lim n (1 λ/n) n. 5

Bernoulli random variable with n large and np = λ Let λ be some moderate-sized number. Say λ = 2 or λ = 3. Let n be a huge number, say n = 10 6. Suppose I have a coin that comes up heads with probability λ/n and I toss it n times. How many heads do I expect to see? Answer: np = λ. Let k be some moderate sized number (say k = 4). What is the probability that I see exactly k heads? Binomial B n formula: n n(n 1)(n 2)...(n k+1) p k (1 p) n k = p k (1 p) n k. k k! This is approximately λk (1 p) n k λk e λ k! k!. A Poisson random variable X with parameter λ satisfies λ k k! P{X = k} = e λ for integer k 0. 6

Outline Poisson random variable definition Poisson random variable properties Poisson random variable problems 7

Outline Poisson random variable definition Poisson random variable properties Poisson random variable problems 8

Probabilities sum to one A Poisson random variable X with parameter λ satisfies λ p(k) = P{X = k} = k e λ for integer k 0. How can we show that k=0 p(k) = 1? k! Use Taylor expansion e λ = k=0 k!. λ k 9

Expectation A Poisson random variable X with parameter λ satisfies λ P{X = k} = k k! e λ for integer k 0. What is E [X ]? We think of a Poisson random variable as being (roughly) a Bernoulli (n, p) random variable with n very large and p = λ/n. This would suggest E [X ] = λ. Can we show this directly from the formula for P{X = k}? By definition of expectation λ k λ λ k λ E [X ] = P{X = k}k = k e = e. k! (k 1)! k=0 k=0 k=1 Setting j = k 1, this is λ e λ = λ. λ j j=0 j! 10

Variance λ k Given P{X = k} = e λ for integer k 0, what is Var[X ]? k! Think of X as (roughly) a Bernoulli (n, p) random variable with n very large and p = λ/n. This suggests Var[X ] npq λ (since np λ and q = 1 p 1). Can we show directly that Var[X ] = λ? Compute E [X 2 ] = P{X = k}k 2 = k 2 λk e λ λ k 1 = λ k e λ. k! (k 1)! k=0 k=0 k=1 Setting j = k 1, this is λ j λ λ (j + 1) e = λe [X + 1] = λ(λ + 1). j! j=0 Then Var[X ] = E [X 2 ] E [X ] 2 = λ(λ + 1) λ 2 = λ. 11

Outline Poisson random variable definition Poisson random variable properties Poisson random variable problems 12

Outline Poisson random variable definition Poisson random variable properties Poisson random variable problems 13

Poisson random variable problems A country has an average of 2 plane crashes per year. How reasonable is it to assume the number of crashes is Poisson with parameter 2? Assuming this, what is the probability of exactly 2 crashes? Of zero crashes? Of four crashes? A city has an average of five major earthquakes a century. What is the probability that there is an earthquake in a given decade (assuming the number of earthquakes per decade is Poisson)? If both candidates average one major gaffe per debate, what is the probably that the first has at least one major gaffe and the second doesn t? (What assumptions are we making?) A casino deals one million five-card poker hands per year. Approximate the probability that there are exactly 2 royal flush hands during a given year. 14

MIT OpenCourseWare http://ocw.mit.edu 18.440 Probability and Random Variables Spring 2014 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.