Multinomial Distribution

Slides:



Advertisements
Similar presentations
Chapter 3 Some Special Distributions Math 6203 Fall 2009 Instructor: Ayona Chatterjee.
Advertisements

Exponential and Poisson Chapter 5 Material. 2 Poisson Distribution [Discrete] Poisson distribution describes many random processes quite well and is mathematically.
Exponential Distribution
Many useful applications, especially in queueing systems, inventory management, and reliability analysis. A connection between discrete time Markov chains.
Discrete Uniform Distribution
Random Variable A random variable X is a function that assign a real number, X(ζ), to each outcome ζ in the sample space of a random experiment. Domain.
Review of Basic Probability and Statistics
Engineering Probability and Statistics - SE-205 -Chap 4 By S. O. Duffuaa.
Probability Distributions
1 Review of Probability Theory [Source: Stanford University]
QBM117 Business Statistics
Probability theory 2011 Outline of lecture 7 The Poisson process  Definitions  Restarted Poisson processes  Conditioning in Poisson processes  Thinning.
A random variable that has the following pmf is said to be a binomial random variable with parameters n, p The Binomial random variable.
Class notes for ISE 201 San Jose State University
Week 51 Relation between Binomial and Poisson Distributions Binomial distribution Model for number of success in n trails where P(success in any one trail)
Chapter 21 Random Variables Discrete: Bernoulli, Binomial, Geometric, Poisson Continuous: Uniform, Exponential, Gamma, Normal Expectation & Variance, Joint.
4-1 Continuous Random Variables 4-2 Probability Distributions and Probability Density Functions Figure 4-1 Density function of a loading on a long,
Chapter 4. Continuous Probability Distributions
Exponential Distribution & Poisson Process
1 Exponential Distribution & Poisson Process Memorylessness & other exponential distribution properties; Poisson process and compound P.P.’s.
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. Discrete Random Variables Chapter 4.
Chapter 5 Some Discrete Probability Distributions.
The Poisson Process. A stochastic process { N ( t ), t ≥ 0} is said to be a counting process if N ( t ) represents the total number of “events” that occur.
Exponential and Chi-Square Random Variables
Week 41 Continuous Probability Spaces Ω is not countable. Outcomes can be any real number or part of an interval of R, e.g. heights, weights and lifetimes.
Topic 4 - Continuous distributions
CPSC 531: Probability Review1 CPSC 531:Distributions Instructor: Anirban Mahanti Office: ICT Class Location: TRB 101.
Chapter 5 Statistical Models in Simulation
Tch-prob1 Chap 3. Random Variables The outcome of a random experiment need not be a number. However, we are usually interested in some measurement or numeric.
Winter 2006EE384x1 Review of Probability Theory Review Session 1 EE384X.
Random Variables & Probability Distributions Outcomes of experiments are, in part, random E.g. Let X 7 be the gender of the 7 th randomly selected student.
Poisson Random Variable Provides model for data that represent the number of occurrences of a specified event in a given unit of time X represents the.
Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter 5 Discrete Random Variables.
1 Topic 3 - Discrete distributions Basics of discrete distributions Mean and variance of a discrete distribution Binomial distribution Poisson distribution.
STA347 - week 31 Random Variables Example: We roll a fair die 6 times. Suppose we are interested in the number of 5’s in the 6 rolls. Let X = number of.
Chapter 01 Probability and Stochastic Processes References: Wolff, Stochastic Modeling and the Theory of Queues, Chapter 1 Altiok, Performance Analysis.
Exam 2: Rules Section 2.1 Bring a cheat sheet. One page 2 sides. Bring a calculator. Bring your book to use the tables in the back.
Topic 3 - Discrete distributions Basics of discrete distributions - pages Mean and variance of a discrete distribution - pages ,
4.3 More Discrete Probability Distributions NOTES Coach Bridges.
Random Variables Example:
Chapter 6: Continuous Probability Distributions A visual comparison.
 Recall your experience when you take an elevator.  Think about usually how long it takes for the elevator to arrive.  Most likely, the experience.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter 5 Discrete Random Variables.
Engineering Probability and Statistics - SE-205 -Chap 3 By S. O. Duffuaa.
Chapter 6: Continuous Probability Distributions A visual comparison.
1 Discrete Probability Distributions Hypergeometric & Poisson Distributions Dr. Jerrell T. Stracener, SAE Fellow Leadership in Engineering EMIS 7370/5370.
Chap 5-1 Chapter 5 Discrete Random Variables and Probability Distributions Statistics for Business and Economics 6 th Edition.
Random Variables By: 1.
Week 61 Poisson Processes Model for times of occurrences (“arrivals”) of rare phenomena where λ – average number of arrivals per time period. X – number.
ENGG 2040C: Probability Models and Applications Andrej Bogdanov Spring Continuous Random Variables.
4-1 Continuous Random Variables 4-2 Probability Distributions and Probability Density Functions Figure 4-1 Density function of a loading on a long,
Probability Distributions: a review
Chapter 4 Continuous Random Variables and Probability Distributions
Engineering Probability and Statistics - SE-205 -Chap 4
Continuous Probability Distributions Part 2
The Exponential and Gamma Distributions
Exponential Distribution & Poisson Process
The Poisson Process.
Probability Distributions
Pertemuan ke-7 s/d ke-10 (minggu ke-4 dan ke-5)
Chapter 7: Sampling Distributions
Chapter 5 Statistical Models in Simulation
Probability Distributions
Probability Review for Financial Engineers
Some Discrete Probability Distributions
STATISTICAL MODELS.
Continuous Probability Distributions Part 2
Elementary Statistics
Chapter 3 : Random Variables
Geometric Poisson Negative Binomial Gamma
Presentation transcript:

Multinomial Distribution The Binomial distribution can be extended to describe number of outcomes in a series of independent trials each having more than 2 possible outcomes. If a given trail can result in the k outcomes E1, E2, …, Ek with probabilities p1, p2, …, pk, then the probability distribution of the random variables X1, X2, …, Xk, representing the number of occurrences for E1, E2, …, Ek in n independent trials is with , and STA286 week 5

Poisson Processes Recall, the Poisson random variable counts the number of events occurring in a time (or space) interval where λ (a parameter of the distribution) is the rate of the occurrence of the events per one unit of time (or space). Very often we are interested in the number of events occurring in t units of “time”, “space”, “area”, or “volume”. The model for that case is the Poisson Process. It has the following properties: The number of outcomes occurring in one time interval (or other specified region) is independent of the number that occurs in any other disjoint time interval. Process possessing this property is said to have no memory. The probability that a single outcome will occur during a very short time interval is proportional to the length of the time interval and does not depend on the number of outcomes occurring outside this time interval. The probability that more then one outcome will occur in such a short time interval is negligible. The probability distribution for the random variable that counts the number of events per t units of time is given by… STA286 week 5

The Uniform distribution X has a uniform[0,1] distribution. The pdf of X is given by: In general, if X has a Uniform[a, b] distribution, b > a. The pdf of X is given by: The mean and variance of the Uniform distribution are …. STA286 week 5

The Exponential Distribution A random variable X that counts the waiting time for rare phenomena has Exponential(λ) distribution. The parameter of the distribution λ = average number of occurrences per unit of time (space etc.). The pdf of X is given by: Questions: Is this a valid pdf? What is the cdf of X? Note: The textbook uses different parameterization λ = 1/β. STA286 week 5

Important Facts about Exponential Distribution The Exponential random variable possess and important property called Memoryless property. It is described as follows: The Exponential distribution is often used to describe lifetime of machines or other devices. (Read more on Section 6.7). The mean and Variance of the Exponential distribution are… The Exponential distribution very often describe the time until the occurrence of a Poisson event or the time between Poisson events. STA286 week 5

More on Poisson Processes Model for times of occurrences (“arrivals”) of rare phenomena where λ – average number of arrivals per one unit of time period. X – number of arrivals in t units of time period. How long do I have to wait until the first arrival? Let Y = waiting time for the first arrival (a continuous r.v.) then we have Therefore, which is the CDF of the Exponential distribution. The waiting time for the first occurrence of an event when the number of events follows a Poisson distribution is Exponentially distributed. STA286 week 5

The Gamma distribution A random variable X is said to have a gamma distribution with parameters α > 0 and λ > 0 if and only if the density function of X is where Note: the quantity г(α) is known as the gamma function. It is defined for α > 0 and has the following properties: г(1) = 1 г(α + 1) = α г(α) г(n) = (n – 1)! if n is an integer. STA286 week 5