6 vector RVs. 6-1: probability distribution A radio transmitter sends a signal to a receiver using three paths. Let X1, X2, and X3 be the signals that.

Slides:



Advertisements
Similar presentations
Chapter 2 Multivariate Distributions Math 6203 Fall 2009 Instructor: Ayona Chatterjee.
Advertisements

1 Continuous random variables Continuous random variable Let X be such a random variable Takes on values in the real space  (-infinity; +infinity)  (lower.
Random Variables ECE460 Spring, 2012.
Estimation  Samples are collected to estimate characteristics of the population of particular interest. Parameter – numerical characteristic of the population.
Review of Basic Probability and Statistics
TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE UNIVARIATE TRANSFORMATIONS.
1 Def: Let and be random variables of the discrete type with the joint p.m.f. on the space S. (1) is called the mean of (2) is called the variance of (3)
Jean Walrand EECS – U.C. Berkeley
Statistics Lecture 18. Will begin Chapter 5 today.
Test 2 Stock Option Pricing
Probability Theory Part 2: Random Variables. Random Variables  The Notion of a Random Variable The outcome is not always a number Assign a numerical.
Probability Distributions – Finite RV’s Random variables first introduced in Expected Value def. A finite random variable is a random variable that can.
G. Cowan Lectures on Statistical Data Analysis Lecture 2 page 1 Statistical Data Analysis: Lecture 2 1Probability, Bayes’ theorem 2Random variables and.
Assignment 2 Chapter 2: Problems  Due: March 1, 2004 Exam 1 April 1, 2004 – 6:30-8:30 PM Exam 2 May 13, 2004 – 6:30-8:30 PM Makeup.
Statistics Lecture 20. Last Day…completed 5.1 Today Section 5.2 Next Day: Parts of Section 5.3 and 5.4.
1 Review of Probability Theory [Source: Stanford University]
Today Today: Chapter 5 Reading: –Chapter 5 (not 5.12) –Exam includes sections from Chapter 5 –Suggested problems: 5.1, 5.2, 5.3, 5.15, 5.25, 5.33,
Tch-prob1 Chapter 4. Multiple Random Variables Ex Select a student’s name from an urn. S In some random experiments, a number of different quantities.
A random variable that has the following pmf is said to be a binomial random variable with parameters n, p The Binomial random variable.
Chapter 4 Joint Distribution & Function of rV. Joint Discrete Distribution Definition.
NIPRL Chapter 2. Random Variables 2.1 Discrete Random Variables 2.2 Continuous Random Variables 2.3 The Expectation of a Random Variable 2.4 The Variance.
Distribution Function properties. Density Function – We define the derivative of the distribution function F X (x) as the probability density function.
Sampling Distributions  A statistic is random in value … it changes from sample to sample.  The probability distribution of a statistic is called a sampling.
Prof. SankarReview of Random Process1 Probability Sample Space (S) –Collection of all possible outcomes of a random experiment Sample Point –Each outcome.
Pairs of Random Variables Random Process. Introduction  In this lecture you will study:  Joint pmf, cdf, and pdf  Joint moments  The degree of “correlation”
ST3236: Stochastic Process Tutorial 10
Winter 2006EE384x1 Review of Probability Theory Review Session 1 EE384X.
Statistics for Engineer Week II and Week III: Random Variables and Probability Distribution.
CHAPTER 4 Multiple Random Variable
PROBABILITY AND STATISTICS FOR ENGINEERING Hossein Sameti Department of Computer Engineering Sharif University of Technology Two Functions of Two Random.
N– variate Gaussian. Some important characteristics: 1)The pdf of n jointly Gaussian R.V.’s is completely described by means, variances and covariances.
Multiple Random Variables Two Discrete Random Variables –Joint pmf –Marginal pmf Two Continuous Random Variables –Joint Distribution (PDF) –Joint Density.
1 Two Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization: Suppose X and Y are two random.
One Random Variable Random Process.
Lecture 11 Pairs and Vector of Random Variables Last Time Pairs of R.Vs. Marginal PMF (Cont.) Joint PDF Marginal PDF Functions of Two R.Vs Expected Values.
 Random variables can be classified as either discrete or continuous.  Example: ◦ Discrete: mostly counts ◦ Continuous: time, distance, etc.
7 sum of RVs. 7-1: variance of Z Find the variance of Z = X+Y by using Var(X), Var(Y), and Cov(X,Y)
Operations on Multiple Random Variables
1 8. One Function of Two Random Variables Given two random variables X and Y and a function g(x,y), we form a new random variable Z as Given the joint.
Geology 6600/7600 Signal Analysis 02 Sep 2015 © A.R. Lowry 2015 Last time: Signal Analysis is a set of tools used to extract information from sequences.
Math 4030 – 6a Joint Distributions (Discrete)
TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE
Continuous Random Variable (1) Section Continuous Random Variable What is the probability that X is equal to x?
Probability and Moment Approximations using Limit Theorems.
MULTIPLE RANDOM VARIABLES A vector random variable X is a function that assigns a vector of real numbers to each outcome of a random experiment. e.g. Random.
One Function of Two Random Variables
Chapter 31 Conditional Probability & Conditional Expectation Conditional distributions Computing expectations by conditioning Computing probabilities by.
1 Review of Probability and Random Processes. 2 Importance of Random Processes Random variables and processes talk about quantities and signals which.
5 pair of RVs.
Geology 6600/7600 Signal Analysis 04 Sep 2014 © A.R. Lowry 2015 Last time: Signal Analysis is a set of tools used to extract information from sequences.
Chapter 5 Joint Probability Distributions and Random Samples  Jointly Distributed Random Variables.2 - Expected Values, Covariance, and Correlation.3.
Random Variables By: 1.
Statistics Lecture 19.
Jointly distributed random variables
Cumulative distribution functions and expected values
Some Rules for Expectation
Tutorial 7: General Random Variables 3
Problem 1.
6.3 Sampling Distributions
Chapter 3 : Random Variables
CS723 - Probability and Stochastic Processes
5 pair of RVs.
Berlin Chen Department of Computer Science & Information Engineering
Further Topics on Random Variables: Derived Distributions
Berlin Chen Department of Computer Science & Information Engineering
Further Topics on Random Variables: Derived Distributions
HKN ECE 313 Exam 2 Review Session
Berlin Chen Department of Computer Science & Information Engineering
1/2555 สมศักดิ์ ศิวดำรงพงศ์
Further Topics on Random Variables: Derived Distributions
Presentation transcript:

6 vector RVs

6-1: probability distribution A radio transmitter sends a signal to a receiver using three paths. Let X1, X2, and X3 be the signals that arrive at the receiver along each path, whose joint CDF is known. Find the probability that maximum signal is less than or equal to 5. Find the probability that X1 is less than of equal to 5

6-2: joint pmf

6-3: joint pdf Let X 1 be uniform in [0, 1], X 2 be uniform in [0,X 1 ], and X 3 be uniform in [0,X 2 ]. Note that X 3 is also the product of three uniform random variables. Find the joint pdf of X and the marginal pdf of X 3.

6-4: joint characteristic function Suppose U and V are independent zero- mean, unit-variance Gaussian random variables X = U+V Y = 2U+V Find the joint characteristic function of X and Y, and find E[XY].