Chapter 3 Single Random Variables and Probability Distributions (Part 1)
|
|
- Stanley Gregory
- 5 years ago
- Views:
Transcription
1 Chapter 3 Single Random Variables and Probability Distributions (Part 1)
2 Contents What is a Random Variable? Probability Distribution Functions Cumulative Distribution Function Probability Density Function Common Random Variables and their Distribution Functions Transformation of a Single Random Variable Averages of Random Variables Characteristic Function Chebyshev's Inequality Computer Generation of Random Variables
3 What is a Random Variable? Why random variable?: It is easier to describe and manipulate outcomes and events of the chance eperiments using numerical values rather than in words. purpose of a random variable maps each point in S into a point on R 1. This type of transform or mapping is called a function.
4 What is a Random Variable? (Cont.) For eample, = () where is the random variable, is its specific value, and denotes the outcome of a chance eperiment. Figure 3.1: A r.v. is a mapping of the sample space into a real line.
5 What is a Random Variable? (Cont.) NOTE: Events are now described by the random variable, rather than in words, and corresponding probability of the event can be calculated via r.v. Eample: For a chance eperiment of tossing a fair coin, define a r.v. (): (head) = 1 and (tail) = 0 Then, using the equally likely assignment of probability, we have: P( = 1) = ½ and P( = 0) = ½ Value assignment of a r.v. entirely depends on the convenience, i.e. values 0 and 1 would be more convenient to handle than values and e.
6 What is a Random Variable? (Cont.) Eample 3.1 Consider an eperiment of rolling a pair of dice, and find the probability of all possible values of the sum. Solution: Define a r.v. as the sum of the two dice, then referring the Table 3.1 and applying the equally likely assignment of probability.
7 What is a Random Variable? (Cont.) Eample 3.1 (Cont.) Table 3.1: Die 1 Die 2 1 spot 2 spot 3 spot 4 spot 5 spot 6 spot 1 spot spot spot spot spot spot We have: P( = 2) = 1/36, P( = 3) = 2/36=1/18, P( = 4) = 3/36 = 1/12, P( = 5) = 4/36 = 1/9, P(= 6) = 5/36, P( = 7) = 6/36 = 1/6, P( = 8) = 5/36, P(= 9) = 4/36 = 1/9, P( = 10) = 3/36 = 1/12, P( = 11) = 2/36 = 1/18, P( = 12) = 1/36
8 What is a Random Variable? (Cont.) FACT: Functions of r.v. are ALSO r.v.'s. For instance: Y e, W ln, U cos, V 2
9 What is a Random Variable? (Cont.) Categorization of random variables: 1. Discrete random variable assumes only a countable number of values. (e.g.) rolling dice: Eample 3.1 (only 11 values) 2. Continuous random variable can assume a continuum of values. (e.g.) weather vane: angle of the indicator (any value from 0 to 2 radian) 3. Mied random variable is a combination of above two types.
10 Probability Distribution Functions Description of r.v. using probability: In case of discrete r.v.: probability mass distribution tabulate (or plot) probability of its values Two types of general methods of description: (1) Cumulative (probability) distribution function: cdf (2) Probability density function: pdf These apply to and work for all three types of r.v., i.e., continuous, discrete, and mied random variables.
11 Cumulative Distribution Function Definition 3.1 Cumulative Distribution Function: The cdf of a random variable is defined as follows: F ( ) P( )
12 Cumulative Distribution Function (Cont.) Eample 3.3 Consider a chance eperiment of rolling a pair of fair dice, and define a r.v. as the sum of the numbers showing up. Find the cdf of. (refer to Eample 3.1) Solution: Since the cdf is defined as F () = P( ), for instance if = 3, we have: ( ) P 2 3 P 2 P 3 F P 1,1 P1,2 2,1 P1,1 P1,2 P2, Notice that: F (-) = P() = 0, and F () = P(S) = 1.
13 Cumulative Distribution Function (Cont.) Eample 3.3 (Cont.) Figure 3.2: The cdf of, which is the sum of two dice. F ( ) 0, , , , , , , , , , , , 12
14 Cumulative Distribution Function (Cont.) General properties of cdf: 1. Limiting values: lim F ( ) 0 and lim ( ) 1 2. The cdf is right hand continuous, i.e.: F ( ) lim F ( ) The cdf F () is monotonous non-decreasing function of. 4. The probability of having values b/w 1 and 2 is given by: P F ) F ( ) F 1 2 ( 2 1
15 The CDF (Cont.) Eample 3.4 A coin is tossed three times. Let r.v. be the number of heads in three trials. S = {0, 1, 2, 3} For a small positive number 1 F (1 ) P[ 1 ] P{0 heads} F (1) P[ 1] P{0 or 1heads} 8 8 For a small positive number F (1 ) P[ 1 ] P{0 or 1 heads}
16 The CDF (Cont.) Eample 3.4 (Cont.) CDF Derivative of F () with respect to. 3) ( 8 1 2) ( 8 3 1) ( 8 3 ) ( 8 1 ) ( u u u u F 3) ( 8 1 2) ( 8 3 1) ( 8 3 ) ( 8 1 ) ( f
17 Cumulative Distribution Function (Cont.) Brief verification: 1. Notice that the inverse images of at = and = are respectively: 1 ( ) and 2. This is due to the fact that the cdf is defined as F () P( ) rather than F () P( < ): detailed proof is omitted! To prove this, we need the so called continuity aiom, which is beyond the scope of this class: will be discussed at the graduate level course. 3. Follows from property # 4. 1 ( ) S
18 Cumulative Distribution Function (Cont.) Brief verification (Cont.): 4. Let 1 2, then we have: which is equivalent to: Rearranging the above provides: 0 ) ( ) ( ) ( F F P ) ( ) ( ) ( F P F ) ( P P P P
19 Cumulative Distribution Function (Cont.) Eample 3.4 Find the probability that the sum of two dice is between 3 and 7 inclusive. Solution: From the definition and properties of cdf, and using figure 3.1, we have: P 3 7 F (7) F 1 36 Note that there 20 outcomes favorable to the event, thus applying the equally likely assignment probability, we get 20/36. (3 ) F (7) F (2)
20 Cumulative Distribution Function (Cont.) Eample 3.5 Is F () below is a valid cdf? F ( ) 1 tan 2 Solution: Check if all the properties of cdf are satisfied: self study Figure 3.3: Suitable cdf.
21 Probability Density Function Definition 3.2 Probability Density Function: The pdf of a (continuous) random variable is defined as follows: f df ( ) ( ) d
22 Probability Density Function (Cont.) NOTE: interpretation of pdf!!! Using the definition of derivative, we can rewrite pdf as: For sufficiently small, we can remove the limit, and thus: f P F F ) ( ) ( ) ( F F f ) ( ) ( lim ) ( 0
23 Probability Density Function (Cont.) General properties of pdf: Since the pdf of a r.v. is the derivative of the cdf, cdf F () can be epressed as the integration of the pdf f (), i.e.: F ( ) f ( u) du 1. The pdf is a non-negative function, i.e.: f ( ) 0, 2. The area under pdf is unity, i.e.: f ( ) d 1 3. The probability of having values b/w 1 and 2 is given by: 2 P( 1 2) f ( ) d 1
24 Probability Density Function (Cont.) Brief verification: 1. Since the cdf is non-decreasing, and pdf is the derivative ( or slope) of it, it must be non-negative. 2. Using the relationship b/w the cdf and pdf, we have: f def ( ) d F ( ) 1
25 Probability Density Function (Cont.) Brief verification (Cont.): 3. From the property of the cdf, and using the relationship b/w the cdf and pdf, we have: P( 1 2) F ( 2) F ( 1 ) f f ( u) du ( u) du 1 f ( u) du
26 Probability Density Function (Cont.) Eample 3.6 Obtain the pdf of a r.v. whose cdf is given as: (eample 3.5) F ( ) 1 tan 2 and find the probability of an event in {2 < 5}. Solution: d 1 Since tan d 1 f ( ) , we easily get :
27 Probability Density Function (Cont.) Eample 3.6 (Cont.) Using the property of the cdf, we obtain: P(2 5) tan 5 tan Figure 3.4: The pdf of in Eample 3.6.
28 Probability Density Function (Cont.) Remark: The definition of the pdf can generally be applied to both continuous and discrete random variables!!! Motive: Since the cdf of a discrete r.v. can generally be epressed as the sum of the weighted & shifted unit step function u(), i.e.: F N ( ) p u( i1 i i ) Recall that the unit step function is defined as u() 1 for 0 and 0 elsewhere.
29 Probability Density Function (Cont.) Motive (Cont.): The derivative of u() is the unit impulse function () as: du( ) ( ) d the pdf f () of a discrete r.v. can generally be described as the sum of the weighted & shifted unit impulse function (), i.e.: f N ( ) p ( i1 i i )
30 Probability Density Function (Cont.) Eample 3.7 Epress the pdf of the r.v. discussed in Eample 3.3. Solution: The cdf of in terms of u() can be epressed as: ] [ 36 1 ) ( u u u u u u u u u u u F
31 Probability Density Function (Cont.) Eample 3.7 (Cont.) Solution (Cont.): Taking the derivative, we find the pdf to be: ] [ 36 1 ) ( f
32 Probability Density Function (Cont.) Figure 3.5: The pdf of in Eample 3.7
33 Common Random Variables and Their Distribution Functions Commonly occurring and widely used r.v.'s are discussed In terms of their cdf & pdf
34 Common Random Variables and Their Distribution Functions (Cont.) Uniform Random Variable: The uniform random variable is defined in terms of its probability density function as follows: 1 b a, a b, b a f ( ) 0, otherwise By integration, we can derive its cumulative distribution function (cdf) to be: 0, a F ( ) a b a, a b 1, b
35 Common Random Variables and Their Distribution Functions (Cont.) Figure 3.6: Uniform r.v.: (a) pdf, (b) cdf in case of a=0 & b=5
36 Common Random Variables and Their Distribution Functions (Cont.) Eample 3.8 Resistors are known to be uniformly distributed within 10% tolerance range. Find the probability that a nominal 1000 resistor has a value b/w 990 and Solution: Since the tolerance region is bounded in the interval [900; 1100] centered at the nominal value of 1000, the pdf of the resistance R is given by: f R 1 200, ( r) 0, 900 r 1100 otherwise
37 Common Random Variables and Their Distribution Functions (Cont.) Eample 3.8 (Cont.) Solution (Cont.): Therefore, the probability that the resistor has a resistance value in the range of [990; 1010] is then: P( dr R 1010 )
38 Common Random Variables and Their Distribution Functions (Cont.) Gaussian Random Variable: The Gaussian random variable is defined in terms of its probability density function as follows: 2 2 m 2 e f ( ) 2 2 where m and 2 are parameters called the mean and variance respectively. The cdf can be derived by direct integration of the pdf, which cannot be epressed in a closed form, however...
39 Common Random Variables and Their Distribution Functions (Cont.) Gaussian Random Variable (Cont.): Instead, we use either of the following functions defined as: (1) Q function: 1 2 u 2 Q( ) e du 2 whose numerical values are tabulated in Appendi C. (2) Error function: 2 erf ( ) e 0 2 u du
40 Common Random Variables and Their Distribution Functions (Cont.) Gaussian Random Variable (Cont.): In terms of the Q function, the cdf of the Gaussian r.v. is given by: m F ( ) 1 Q derivation: assignment Question: Epress Q and error functions in terms of each other: assignment
41 Common Random Variables and Their Distribution Functions (Cont.) Figure: Illustration of integration for Q and error functions.
42 Common Random Variables and Their Distribution Functions (Cont.) Figure 3.7: Gaussian distribution for m = 5 and = 2: (a) pdf; (b) cdf.
43 Common Random Variables and Their Distribution Functions (Cont.) Eample 3.9 A mechanical component, whose 5mm thickness (T) is known to have a Gaussian distribution w/ m = 5 and = Find the probability that T is less than 4.9mm OR greater than 5.1mm. Solution: We use here the relationship Q() = 1 - Q( ) for < 0. P( T 4.9mm OR T 5.1mm) 1 P(4.9mm T 5.1mm) F Q Q F T T Q 0.05 Q 2 Q2 1 1 Q 2 Q
44 Common Random Variables and Their Distribution Functions (Cont.) Eponential Random Variable 사건들이발생하는시간의간격 장치와시스템의수명을모형화 f e ( ) u( ), 0 F e ( ) (1 ) u( )
45 Common Random Variables and Their Distribution Functions (Cont.) Gamma Random Variable: The Gamma random variable has its probability density function as follows: f ( ) b c ( b) b1 e c u( ), b and c 0 where (b) is the gamma function given by the integral: 0 b1 y ( b) y e dy It can be shown by evaluation that (1) = 1 and (½ )= ½, and by replacing b by b+1, we can also show that (b+1) = b(b), which in turn provides that (n+1) = n! in case of b an integer n.
46 Common Random Variables and Their Distribution Functions (Cont.) Special cases: 1. Chi-square pdf: (statistics) where b and c are replaced by b = n/2 and c = 2 2. Erlang pdf: (queuing theory) where b = n is an integer. ) ( 2) ( 2 1 ) ( u e n f n n ) ( 1! ) ( 1 u e n c f c n n
47 Common Random Variables and Their Distribution Functions (Cont.) Figure 3.8: Plots of (a) chi-square pdf and (b) Erlang pdf.
48 Common Random Variables and Their Distribution Functions (Cont.) Cauchy Random Variable: The Cauchy random variable has its probability density function as follows: f ( ) 2 2 Corresponding cdf is given by: F ( ) tan 2 Note: Eample 3.5 with figure 3.3 is the case of Cauchy r.v. for = 1.
49 Common Random Variables and Their Distribution Functions (Cont.) Binomial Random Variable: The r.v. is binomially distributed if it takes on the non-negative integer values 0, 1, 2,, n with probabilities: where p + q = 1. Corresponding pdf and the cdf, using the unit impulse function, are given by: n k k n k k q p k n f 0 ) ( n k q p k n k P k n k, 2, 0,1,, ) ( k k n k q p k n F ) (
50 Common Random Variables and Their Distribution Functions (Cont.) Remark: This is the distribution describing the number (k) of heads occurring in n tosses of a fair coin, in which case p = q = 0.5. Figure 3.9: The pdf of binomial r.v. for n = 10: (a) p = q = 1/2 ; (b) p =1/5, q = 4/5.
51 Common Random Variables and Their Distribution Functions (Cont.) Geometric Random Variable: Suppose we flip a biased coin w/ probability of a head is p whereas the probability of tail is 1- p. Then, the probability of getting the head (success) for the first time at the k-th toss is given by: P(first succes at trial k) P( k) where p is the probability of success. called the geometric distribution k1 1 p p, k 1, 2, corresponds to the geometric mean of k - 1 and k + 1 values.
52 Common Random Variables and Their Distribution Functions (Cont.) Poisson Random Variable: The r.v. is Poisson with parameter a if it takes on the non-negative integer values 0, 1, 2, with probabilities: k a a P( k) e, k 0,1, k! 2, Corresponding cdf of a Poisson r.v. is given by: F ( ) k a e k! k a
53 Common Random Variables and Their Distribution Functions (Cont.) Figure 3.10: Poisson pdf (a) a = 0.9; (b) a = 0.2.
54 Common Random Variables and Their Distribution Functions (Cont.) Limiting Forms of Binomial and Poisson Distributions: Theorem 3.1 De Moivre-Laplace theorem: For n sufficiently large, the binomial distribution can be approimated by the samples of a Gaussian curve properly scaled and shifted as: p k q nk where m = np and 2 = npq respectively. Proof: omit e km 2 2 2, n 1
55 Common Random Variables and Their Distribution Functions (Cont.) Corresponding cdf of a binomial r.v., based on the De Moivre-Laplace theorem, can be approimated as: np F ( ) 1 Q npq where Q() is the Q-function.
56 Common Random Variables and Their Distribution Functions (Cont.) Figure 3.11: Binomial cdf and De Moivre-Laplace approimation for n = 10: (a) p = 0.2; (b) a = 0.5.
57 Common Random Variables and Their Distribution Functions (Cont.) Limiting Forms of Binomial and Poisson Distributions (Cont.): Theorem 3.2 : The Poisson distribution approaches to a Gaussian distribution for a >> 1 as: k a e k! a Proof: e ka 2 2a 2a for n 1, This is due to the fact that a binomial distribution approaches the Poisson distribution with a = np if n >> 1 and p << 1, and applying the De Moivre-Laplace theorem proves it! p 1, np a
58
59 Common Random Variables and Their Distribution Functions (Cont.) Eample 3.11 In a digital communication system, the probability of an error is What is the probability of 3 errors in transmission of 5000 bits? Solution: This has a binomial distribution, and the Poisson approimation with n = 5000, p = 10-3, and k = 3 shows: P(3 errors) e ! whereas the eact value of the probability using the binomial distribution is:
60 Common Random Variables and Their Distribution Functions (Cont.) Poisson Points and Eponential Probability Density Function: Consider a finite interval T (fig. 3.12), with the probability of k occurrence of an event ( ni : arrival of electrons at certain point) in this interval obeys a Poisson distribution, i.e.: k T T P( k) e, k 0,1, k! 2, where is the number of events per unit time.
61 Common Random Variables and Their Distribution Functions (Cont.) Poisson Points and Eponential Probability Density Function (Cont.): Then, the interval from an arbitrarily selected point and the net event is a r.v. W following an eponential distribution with its pdf as: f W w ( w) e u( w) Figure 3.12: Poisson points on a finite interval T.
62 Common Random Variables and Their Distribution Functions (Cont.) Poisson Points and Eponential Probability Density Function (Cont.): Proof: First, find the cdf of W as: w FW ( w) P( W w) 1 P( 0) 1 e, w 0 {W w} corresponds to the event that there is at least one Poisson event in the interval (0, w). Differentiating it, we obtain: f W d w ( w) FW ( w) e u( w) dw
63 Common Random Variables and Their Distribution Functions (Cont.) Eample 3.12 Raindrops impinge on a tin roof at a rate of 100/sec. What is the probability that the interval between adjacent raindrops is greater than 1(msec), and 10(msec)? Solution: This can be approimated by a Poisson point process with = 100, and therefore: P( W P( W ) 1 P( W 1 ) 1 P( W e e ) ) e e
64 Common Random Variables and Their Distribution Functions (Cont.) Pascal Random Variable: The r.v. has a Pascal distribution if it takes on the positive integer values 1, 2, 3, with probabilities: n 1 k nk P( k) p q for k 1, 2,, k 1 and q 1 p
65 Common Random Variables and Their Distribution Functions (Cont.) Note: 1. For eample, in a sequence of coin tossing, the probability of getting the k-th head on the n-th toss obeys a Pascal distribution, where p = prob. of a head. 2. Reasoning: We get the first k-1 heads in any order in the first n-1 tosses, which is binomial, and then must get a head on the n-th toss. 3. Geometric distribution is a special case of the Pascal distribution. Eample 3.13 Self study
66 Common Random Variables and Their Distribution Functions (Cont.) Hypergeometric Random Variable: Consider a bo containing N items, K of which are defective. Then, the probability of obtaining k defective items in a selection of n items without replacement follows the hypergeometric distribution: K N K k n k P ( k), k 0,1, 2,, n N n Eample 3.14 Self study
Chapter 2: The Random Variable
Chapter : The Random Variable The outcome of a random eperiment need not be a number, for eample tossing a coin or selecting a color ball from a bo. However we are usually interested not in the outcome
More informationRandom variable X is a mapping that maps each outcome s in the sample space to a unique real number x, x. X s. Real Line
Random Variable Random variable is a mapping that maps each outcome s in the sample space to a unique real number,. s s : outcome Sample Space Real Line Eamples Toss a coin. Define the random variable
More informationRandom variable X is a mapping that maps each outcome s in the sample space to a unique real number x, < x <. ( ) X s. Real Line
Random Variable Random variable is a mapping that maps each outcome s in the sample space to a unique real number, <
More informationRandom Variable. Discrete Random Variable. Continuous Random Variable. Discrete Random Variable. Discrete Probability Distribution
Random Variable Theoretical Probability Distribution Random Variable Discrete Probability Distributions A variable that assumes a numerical description for the outcome of a random eperiment (by chance).
More informationChapter 2. Random Variable. Define single random variables in terms of their PDF and CDF, and calculate moments such as the mean and variance.
Chapter 2 Random Variable CLO2 Define single random variables in terms of their PDF and CDF, and calculate moments such as the mean and variance. 1 1. Introduction In Chapter 1, we introduced the concept
More informationSystem Simulation Part II: Mathematical and Statistical Models Chapter 5: Statistical Models
System Simulation Part II: Mathematical and Statistical Models Chapter 5: Statistical Models Fatih Cavdur fatihcavdur@uludag.edu.tr March 29, 2014 Introduction Introduction The world of the model-builder
More informationLecture Notes 2 Random Variables. Random Variable
Lecture Notes 2 Random Variables Definition Discrete Random Variables: Probability mass function (pmf) Continuous Random Variables: Probability density function (pdf) Mean and Variance Cumulative Distribution
More informationEE/CpE 345. Modeling and Simulation. Fall Class 5 September 30, 2002
EE/CpE 345 Modeling and Simulation Class 5 September 30, 2002 Statistical Models in Simulation Real World phenomena of interest Sample phenomena select distribution Probabilistic, not deterministic Model
More informationStochastic Processes. Review of Elementary Probability Lecture I. Hamid R. Rabiee Ali Jalali
Stochastic Processes Review o Elementary Probability bili Lecture I Hamid R. Rabiee Ali Jalali Outline History/Philosophy Random Variables Density/Distribution Functions Joint/Conditional Distributions
More informationRS Chapter 2 Random Variables 9/28/2017. Chapter 2. Random Variables
RS Chapter Random Variables 9/8/017 Chapter Random Variables Random Variables A random variable is a convenient way to epress the elements of Ω as numbers rather than abstract elements of sets. Definition:
More informationPlotting data is one method for selecting a probability distribution. The following
Advanced Analytical Models: Over 800 Models and 300 Applications from the Basel II Accord to Wall Street and Beyond By Johnathan Mun Copyright 008 by Johnathan Mun APPENDIX C Understanding and Choosing
More informationLecture Notes 2 Random Variables. Discrete Random Variables: Probability mass function (pmf)
Lecture Notes 2 Random Variables Definition Discrete Random Variables: Probability mass function (pmf) Continuous Random Variables: Probability density function (pdf) Mean and Variance Cumulative Distribution
More informationName: Firas Rassoul-Agha
Midterm 1 - Math 5010 - Spring 016 Name: Firas Rassoul-Agha Solve the following 4 problems. You have to clearly explain your solution. The answer carries no points. Only the work does. CALCULATORS ARE
More informationContinuous-Valued Probability Review
CS 6323 Continuous-Valued Probability Review Prof. Gregory Provan Department of Computer Science University College Cork 2 Overview Review of discrete distributions Continuous distributions 3 Discrete
More informationESS011 Mathematical statistics and signal processing
ESS011 Mathematical statistics and signal processing Lecture 9: Gaussian distribution, transformation formula for continuous random variables, and the joint distribution Tuomas A. Rajala Chalmers TU April
More informationReview of Probability. CS1538: Introduction to Simulations
Review of Probability CS1538: Introduction to Simulations Probability and Statistics in Simulation Why do we need probability and statistics in simulation? Needed to validate the simulation model Needed
More informationRelationship between probability set function and random variable - 2 -
2.0 Random Variables A rat is selected at random from a cage and its sex is determined. The set of possible outcomes is female and male. Thus outcome space is S = {female, male} = {F, M}. If we let X be
More informationChapter 2: Random Variables
ECE54: Stochastic Signals and Systems Fall 28 Lecture 2 - September 3, 28 Dr. Salim El Rouayheb Scribe: Peiwen Tian, Lu Liu, Ghadir Ayache Chapter 2: Random Variables Example. Tossing a fair coin twice:
More informationn px p x (1 p) n x. p x n(n 1)... (n x + 1) x!
Lectures 3-4 jacques@ucsd.edu 7. Classical discrete distributions D. The Poisson Distribution. If a coin with heads probability p is flipped independently n times, then the number of heads is Bin(n, p)
More informationIntroduction...2 Chapter Review on probability and random variables Random experiment, sample space and events
Introduction... Chapter...3 Review on probability and random variables...3. Random eperiment, sample space and events...3. Probability definition...7.3 Conditional Probability and Independence...7.4 heorem
More informationIntroduction to Probability Theory for Graduate Economics Fall 2008
Introduction to Probability Theory for Graduate Economics Fall 008 Yiğit Sağlam October 10, 008 CHAPTER - RANDOM VARIABLES AND EXPECTATION 1 1 Random Variables A random variable (RV) is a real-valued function
More informationCME 106: Review Probability theory
: Probability theory Sven Schmit April 3, 2015 1 Overview In the first half of the course, we covered topics from probability theory. The difference between statistics and probability theory is the following:
More informationChapter 1. Sets and probability. 1.3 Probability space
Random processes - Chapter 1. Sets and probability 1 Random processes Chapter 1. Sets and probability 1.3 Probability space 1.3 Probability space Random processes - Chapter 1. Sets and probability 2 Probability
More informationELEG 3143 Probability & Stochastic Process Ch. 2 Discrete Random Variables
Department of Electrical Engineering University of Arkansas ELEG 3143 Probability & Stochastic Process Ch. 2 Discrete Random Variables Dr. Jingxian Wu wuj@uark.edu OUTLINE 2 Random Variable Discrete Random
More informationRandom Variables Example:
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 5 s. Then X could be 0, 1, 2, 3, 4, 5, 6. X = 0 corresponds to the
More informationChapter 1 Statistical Reasoning Why statistics? Section 1.1 Basics of Probability Theory
Chapter 1 Statistical Reasoning Why statistics? Uncertainty of nature (weather, earth movement, etc. ) Uncertainty in observation/sampling/measurement Variability of human operation/error imperfection
More informationRandom Variables. Definition: A random variable (r.v.) X on the probability space (Ω, F, P) is a mapping
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 5 s. Then X could be 0, 1, 2, 3, 4, 5, 6. X = 0 corresponds to the
More informationSystem Simulation Part II: Mathematical and Statistical Models Chapter 5: Statistical Models
System Simulation Part II: Mathematical and Statistical Models Chapter 5: Statistical Models Fatih Cavdur fatihcavdur@uludag.edu.tr March 20, 2012 Introduction Introduction The world of the model-builder
More informationReview of Elementary Probability Lecture I Hamid R. Rabiee
Stochastic Processes Review o Elementar Probabilit Lecture I Hamid R. Rabiee Outline Histor/Philosoph Random Variables Densit/Distribution Functions Joint/Conditional Distributions Correlation Important
More informationChapter 1 Probability Theory
Review for the previous lecture Eample: how to calculate probabilities of events (especially for sampling with replacement) and the conditional probability Definition: conditional probability, statistically
More informationChapter 2. Discrete Distributions
Chapter. Discrete Distributions Objectives ˆ Basic Concepts & Epectations ˆ Binomial, Poisson, Geometric, Negative Binomial, and Hypergeometric Distributions ˆ Introduction to the Maimum Likelihood Estimation
More informationLecture 3. Discrete Random Variables
Math 408 - Mathematical Statistics Lecture 3. Discrete Random Variables January 23, 2013 Konstantin Zuev (USC) Math 408, Lecture 3 January 23, 2013 1 / 14 Agenda Random Variable: Motivation and Definition
More informationB.N.Bandodkar College of Science, Thane. Subject : Computer Simulation and Modeling.
B.N.Bandodkar College of Science, Thane Subject : Computer Simulation and Modeling. Simulation is a powerful technique for solving a wide variety of problems. To simulate is to copy the behaviors of a
More informationThings to remember when learning probability distributions:
SPECIAL DISTRIBUTIONS Some distributions are special because they are useful They include: Poisson, exponential, Normal (Gaussian), Gamma, geometric, negative binomial, Binomial and hypergeometric distributions
More informationComputer Science, Informatik 4 Communication and Distributed Systems. Simulation. Discrete-Event System Simulation. Dr.
Simulation Discrete-Event System Simulation Chapter 4 Statistical Models in Simulation Purpose & Overview The world the model-builder sees is probabilistic rather than deterministic. Some statistical model
More informationStatistics and Econometrics I
Statistics and Econometrics I Random Variables Shiu-Sheng Chen Department of Economics National Taiwan University October 5, 2016 Shiu-Sheng Chen (NTU Econ) Statistics and Econometrics I October 5, 2016
More informationMath 180A. Lecture 16 Friday May 7 th. Expectation. Recall the three main probability density functions so far (1) Uniform (2) Exponential.
Math 8A Lecture 6 Friday May 7 th Epectation Recall the three main probability density functions so far () Uniform () Eponential (3) Power Law e, ( ), Math 8A Lecture 6 Friday May 7 th Epectation Eample
More informationDiscrete Random Variables
CPSC 53 Systems Modeling and Simulation Discrete Random Variables Dr. Anirban Mahanti Department of Computer Science University of Calgary mahanti@cpsc.ucalgary.ca Random Variables A random variable is
More informationECE Lecture 4. Overview Simulation & MATLAB
ECE 450 - Lecture 4 Overview Simulation & MATLAB Random Variables: Concept and Definition Cumulative Distribution Functions (CDF s) Eamples & Properties Probability Distribution Functions (pdf s) 1 Random
More informationRandom variables, Expectation, Mean and Variance. Slides are adapted from STAT414 course at PennState
Random variables, Expectation, Mean and Variance Slides are adapted from STAT414 course at PennState https://onlinecourses.science.psu.edu/stat414/ Random variable Definition. Given a random experiment
More informationMultiple Random Variables
Multiple Random Variables Joint Probability Density Let X and Y be two random variables. Their joint distribution function is F ( XY x, y) P X x Y y. F XY ( ) 1, < x
More informationRandom variables. DS GA 1002 Probability and Statistics for Data Science.
Random variables DS GA 1002 Probability and Statistics for Data Science http://www.cims.nyu.edu/~cfgranda/pages/dsga1002_fall17 Carlos Fernandez-Granda Motivation Random variables model numerical quantities
More informationfor valid PSD. PART B (Answer all five units, 5 X 10 = 50 Marks) UNIT I
Code: 15A04304 R15 B.Tech II Year I Semester (R15) Regular Examinations November/December 016 PROBABILITY THEY & STOCHASTIC PROCESSES (Electronics and Communication Engineering) Time: 3 hours Max. Marks:
More informationChap 2.1 : Random Variables
Chap 2.1 : Random Variables Let Ω be sample space of a probability model, and X a function that maps every ξ Ω, toa unique point x R, the set of real numbers. Since the outcome ξ is not certain, so is
More informationII. Probability. II.A General Definitions
II. Probability II.A General Definitions The laws of thermodynamics are based on observations of macroscopic bodies, and encapsulate their thermal properties. On the other hand, matter is composed of atoms
More informationSuppose 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.
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. Coin A is flipped until a head appears, then coin B is flipped until
More informationThe random variable 1
The random variable 1 Contents 1. Definition 2. Distribution and density function 3. Specific random variables 4. Functions of one random variable 5. Mean and variance 2 The random variable A random variable
More informationRecitation 2: Probability
Recitation 2: Probability Colin White, Kenny Marino January 23, 2018 Outline Facts about sets Definitions and facts about probability Random Variables and Joint Distributions Characteristics of distributions
More informationWeek 2. Review of Probability, Random Variables and Univariate Distributions
Week 2 Review of Probability, Random Variables and Univariate Distributions Probability Probability Probability Motivation What use is Probability Theory? Probability models Basis for statistical inference
More information1: PROBABILITY REVIEW
1: PROBABILITY REVIEW Marek Rutkowski School of Mathematics and Statistics University of Sydney Semester 2, 2016 M. Rutkowski (USydney) Slides 1: Probability Review 1 / 56 Outline We will review the following
More informationLecture 2 Binomial and Poisson Probability Distributions
Binomial Probability Distribution Lecture 2 Binomial and Poisson Probability Distributions Consider a situation where there are only two possible outcomes (a Bernoulli trial) Example: flipping a coin James
More informationChapter 3, 4 Random Variables ENCS Probability and Stochastic Processes. Concordia University
Chapter 3, 4 Random Variables ENCS6161 - Probability and Stochastic Processes Concordia University ENCS6161 p.1/47 The Notion of a Random Variable A random variable X is a function that assigns a real
More informationChapter 2 Random Variables
Stochastic Processes Chapter 2 Random Variables Prof. Jernan Juang Dept. of Engineering Science National Cheng Kung University Prof. Chun-Hung Liu Dept. of Electrical and Computer Eng. National Chiao Tung
More informationContinuous Random Variables
1 Continuous Random Variables Example 1 Roll a fair die. Denote by X the random variable taking the value shown by the die, X {1, 2, 3, 4, 5, 6}. Obviously the probability mass function is given by (since
More informationLecture 2. Binomial and Poisson Probability Distributions
Durkin, Lecture 2, Page of 6 Lecture 2 Binomial and Poisson Probability Distributions ) Bernoulli Distribution or Binomial Distribution: Consider a situation where there are only two possible outcomes
More informationProbability Distribution
Economic Risk and Decision Analysis for Oil and Gas Industry CE81.98 School of Engineering and Technology Asian Institute of Technology January Semester Presented by Dr. Thitisak Boonpramote Department
More informationRVs and their probability distributions
RVs and their probability distributions RVs and their probability distributions In these notes, I will use the following notation: The probability distribution (function) on a sample space will be denoted
More informationMath 105 Course Outline
Math 105 Course Outline Week 9 Overview This week we give a very brief introduction to random variables and probability theory. Most observable phenomena have at least some element of randomness associated
More informationMATH Notebook 5 Fall 2018/2019
MATH442601 2 Notebook 5 Fall 2018/2019 prepared by Professor Jenny Baglivo c Copyright 2004-2019 by Jenny A. Baglivo. All Rights Reserved. 5 MATH442601 2 Notebook 5 3 5.1 Sequences of IID Random Variables.............................
More informationSTAT2201. Analysis of Engineering & Scientific Data. Unit 3
STAT2201 Analysis of Engineering & Scientific Data Unit 3 Slava Vaisman The University of Queensland School of Mathematics and Physics What we learned in Unit 2 (1) We defined a sample space of a random
More informationDiscrete Probability Distribution
Shapes of binomial distributions Discrete Probability Distribution Week 11 For this activity you will use a web applet. Go to http://socr.stat.ucla.edu/htmls/socr_eperiments.html and choose Binomial coin
More informationSTAT509: Discrete Random Variable
University of South Carolina September 16, 2014 Motivation So far, we have already known how to calculate probabilities of events. Suppose we toss a fair coin three times, we know that the probability
More informationStatistics 100A Homework 5 Solutions
Chapter 5 Statistics 1A Homework 5 Solutions Ryan Rosario 1. Let X be a random variable with probability density function a What is the value of c? fx { c1 x 1 < x < 1 otherwise We know that for fx to
More informationMath 493 Final Exam December 01
Math 493 Final Exam December 01 NAME: ID NUMBER: Return your blue book to my office or the Math Department office by Noon on Tuesday 11 th. On all parts after the first show enough work in your exam booklet
More informationChapter 3. Chapter 3 sections
sections 3.1 Random Variables and Discrete Distributions 3.2 Continuous Distributions 3.4 Bivariate Distributions 3.5 Marginal Distributions 3.6 Conditional Distributions 3.7 Multivariate Distributions
More informationDiscrete Random Variable
Discrete Random Variable Outcome of a random experiment need not to be a number. We are generally interested in some measurement or numerical attribute of the outcome, rather than the outcome itself. n
More informationProbability and Statistics Concepts
University of Central Florida Computer Science Division COT 5611 - Operating Systems. Spring 014 - dcm Probability and Statistics Concepts Random Variable: a rule that assigns a numerical value to each
More informationDS-GA 1002 Lecture notes 2 Fall Random variables
DS-GA 12 Lecture notes 2 Fall 216 1 Introduction Random variables Random variables are a fundamental tool in probabilistic modeling. They allow us to model numerical quantities that are uncertain: the
More informationRandom variables, distributions and limit theorems
Questions to ask Random variables, distributions and limit theorems What is a random variable? What is a distribution? Where do commonly-used distributions come from? What distribution does my data come
More informationMATH Solutions to Probability Exercises
MATH 5 9 MATH 5 9 Problem. Suppose we flip a fair coin once and observe either T for tails or H for heads. Let X denote the random variable that equals when we observe tails and equals when we observe
More informationBrief Review of Probability
Maura Department of Economics and Finance Università Tor Vergata Outline 1 Distribution Functions Quantiles and Modes of a Distribution 2 Example 3 Example 4 Distributions Outline Distribution Functions
More informationEXAM. Exam #1. Math 3342 Summer II, July 21, 2000 ANSWERS
EXAM Exam # Math 3342 Summer II, 2 July 2, 2 ANSWERS i pts. Problem. Consider the following data: 7, 8, 9, 2,, 7, 2, 3. Find the first quartile, the median, and the third quartile. Make a box and whisker
More informationEE5319R: Problem Set 3 Assigned: 24/08/16, Due: 31/08/16
EE539R: Problem Set 3 Assigned: 24/08/6, Due: 3/08/6. Cover and Thomas: Problem 2.30 (Maimum Entropy): Solution: We are required to maimize H(P X ) over all distributions P X on the non-negative integers
More informationECON 5350 Class Notes Review of Probability and Distribution Theory
ECON 535 Class Notes Review of Probability and Distribution Theory 1 Random Variables Definition. Let c represent an element of the sample space C of a random eperiment, c C. A random variable is a one-to-one
More information(Ch 3.4.1, 3.4.2, 4.1, 4.2, 4.3)
3 Probability Distributions (Ch 3.4.1, 3.4.2, 4.1, 4.2, 4.3) Probability Distribution Functions Probability distribution function (pdf): Function for mapping random variables to real numbers. Discrete
More informationFault-Tolerant Computer System Design ECE 60872/CS 590. Topic 2: Discrete Distributions
Fault-Tolerant Computer System Design ECE 60872/CS 590 Topic 2: Discrete Distributions Saurabh Bagchi ECE/CS Purdue University Outline Basic probability Conditional probability Independence of events Series-parallel
More information1 Presessional Probability
1 Presessional Probability Probability theory is essential for the development of mathematical models in finance, because of the randomness nature of price fluctuations in the markets. This presessional
More informationLecture 2: Repetition of probability theory and statistics
Algorithms for Uncertainty Quantification SS8, IN2345 Tobias Neckel Scientific Computing in Computer Science TUM Lecture 2: Repetition of probability theory and statistics Concept of Building Block: Prerequisites:
More information1. Discrete Distributions
Virtual Laboratories > 2. Distributions > 1 2 3 4 5 6 7 8 1. Discrete Distributions Basic Theory As usual, we start with a random experiment with probability measure P on an underlying sample space Ω.
More informationX = X X n, + X 2
CS 70 Discrete Mathematics for CS Fall 2003 Wagner Lecture 22 Variance Question: At each time step, I flip a fair coin. If it comes up Heads, I walk one step to the right; if it comes up Tails, I walk
More informationCentral Limit Theorem and the Law of Large Numbers Class 6, Jeremy Orloff and Jonathan Bloom
Central Limit Theorem and the Law of Large Numbers Class 6, 8.5 Jeremy Orloff and Jonathan Bloom Learning Goals. Understand the statement of the law of large numbers. 2. Understand the statement of the
More informationMonty Hall Puzzle. Draw a tree diagram of possible choices (a possibility tree ) One for each strategy switch or no-switch
Monty Hall Puzzle Example: You are asked to select one of the three doors to open. There is a large prize behind one of the doors and if you select that door, you win the prize. After you select a door,
More informationChapter 3 Discrete Random Variables
MICHIGAN STATE UNIVERSITY STT 351 SECTION 2 FALL 2008 LECTURE NOTES Chapter 3 Discrete Random Variables Nao Mimoto Contents 1 Random Variables 2 2 Probability Distributions for Discrete Variables 3 3 Expected
More information5. Conditional Distributions
1 of 12 7/16/2009 5:36 AM Virtual Laboratories > 3. Distributions > 1 2 3 4 5 6 7 8 5. Conditional Distributions Basic Theory As usual, we start with a random experiment with probability measure P on an
More informationTheorem 1.7 [Bayes' Law]: Assume that,,, are mutually disjoint events in the sample space s.t.. Then Pr( )
Theorem 1.7 [Bayes' Law]: Assume that,,, are mutually disjoint events in the sample space s.t.. Then Pr Pr = Pr Pr Pr() Pr Pr. We are given three coins and are told that two of the coins are fair and the
More informationLecture 10: Probability distributions TUESDAY, FEBRUARY 19, 2019
Lecture 10: Probability distributions DANIEL WELLER TUESDAY, FEBRUARY 19, 2019 Agenda What is probability? (again) Describing probabilities (distributions) Understanding probabilities (expectation) Partial
More informationChapter 6 Continuous Probability Distributions
Math 3 Chapter 6 Continuous Probability Distributions The observations generated by different statistical experiments have the same general type of behavior. The followings are the probability distributions
More informationTopic 3: The Expectation of a Random Variable
Topic 3: The Expectation of a Random Variable Course 003, 2017 Page 0 Expectation of a discrete random variable Definition (Expectation of a discrete r.v.): The expected value (also called the expectation
More informationBusiness Statistics PROBABILITY DISTRIBUTIONS
Business Statistics PROBABILITY DISTRIBUTIONS CONTENTS Probability distribution functions (discrete) Characteristics of a discrete distribution Example: uniform (discrete) distribution Example: Bernoulli
More informationChapter 5. Statistical Models in Simulations 5.1. Prof. Dr. Mesut Güneş Ch. 5 Statistical Models in Simulations
Chapter 5 Statistical Models in Simulations 5.1 Contents Basic Probability Theory Concepts Discrete Distributions Continuous Distributions Poisson Process Empirical Distributions Useful Statistical Models
More informationChapter 2: Elements of Probability Continued
MSCS6 Chapter : Elements of Probability Continued Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science Copyright 7 by MSCS6 Agenda.7 Chebyshev s Inequality and the law of Large
More informationCMPSCI 240: Reasoning Under Uncertainty
CMPSCI 240: Reasoning Under Uncertainty Lecture 5 Prof. Hanna Wallach wallach@cs.umass.edu February 7, 2012 Reminders Pick up a copy of B&T Check the course website: http://www.cs.umass.edu/ ~wallach/courses/s12/cmpsci240/
More informationProbability Rules. MATH 130, Elements of Statistics I. J. Robert Buchanan. Fall Department of Mathematics
Probability Rules MATH 130, Elements of Statistics I J. Robert Buchanan Department of Mathematics Fall 2018 Introduction Probability is a measure of the likelihood of the occurrence of a certain behavior
More informationChapter 4 Multiple Random Variables
Chapter 4 Multiple Random Variables Chapter 41 Joint and Marginal Distributions Definition 411: An n -dimensional random vector is a function from a sample space S into Euclidean space n R, n -dimensional
More informationChapter 3: Random Variables 1
Chapter 3: Random Variables 1 Yunghsiang S. Han Graduate Institute of Communication Engineering, National Taipei University Taiwan E-mail: yshan@mail.ntpu.edu.tw 1 Modified from the lecture notes by Prof.
More informationHomework 3 solution (100points) Due in class, 9/ (10) 1.19 (page 31)
Homework 3 solution (00points) Due in class, 9/4. (0).9 (page 3) (a) The density curve forms a rectangle over the interval [4, 6]. For this reason, uniform densities are also called rectangular densities
More informationLecture 13 (Part 2): Deviation from mean: Markov s inequality, variance and its properties, Chebyshev s inequality
Lecture 13 (Part 2): Deviation from mean: Markov s inequality, variance and its properties, Chebyshev s inequality Discrete Structures II (Summer 2018) Rutgers University Instructor: Abhishek Bhrushundi
More informationLecture Notes 1 Probability and Random Variables. Conditional Probability and Independence. Functions of a Random Variable
Lecture Notes 1 Probability and Random Variables Probability Spaces Conditional Probability and Independence Random Variables Functions of a Random Variable Generation of a Random Variable Jointly Distributed
More informationIAM 530 ELEMENTS OF PROBABILITY AND STATISTICS LECTURE 3-RANDOM VARIABLES
IAM 530 ELEMENTS OF PROBABILITY AND STATISTICS LECTURE 3-RANDOM VARIABLES VARIABLE Studying the behavior of random variables, and more importantly functions of random variables is essential for both the
More information(Ch 3.4.1, 3.4.2, 4.1, 4.2, 4.3)
3 Probability Distributions (Ch 3.4.1, 3.4.2, 4.1, 4.2, 4.3) Probability Distribution Functions Probability distribution function (pdf): Function for mapping random variables to real numbers. Discrete
More information