Lecture 10 Pairs of Random Variables Last Time Pairs of R.Vs. Joint CDF Joint PMF Marginal PMF Reading Assignment: Chapter 4.1 – 4.3 Probability & Stochastic.

Slides:



Advertisements
Similar presentations
MOMENT GENERATING FUNCTION AND STATISTICAL DISTRIBUTIONS
Advertisements

CS433: Modeling and Simulation
Set theory and Bayes’ Rule Sets and set operations Axioms of probability Probabilities of sets Bayes’ rule Chapter 2 of Haldar and Mahadevan’s Probability,
Random Variables ECE460 Spring, 2012.
STAT 497 APPLIED TIME SERIES ANALYSIS
Ch 4 & 5 Important Ideas Sampling Theory. Density Integration for Probability (Ch 4 Sec 1-2) Integral over (a,b) of density is P(a
DEPARTMENT OF HEALTH SCIENCE AND TECHNOLOGY STOCHASTIC SIGNALS AND PROCESSES Lecture 1 WELCOME.
Probability Review 1 CS479/679 Pattern Recognition Dr. George Bebis.
SGG Theory of Probability1 Probability Distribution Assoc. Prof. Dr. Abdul Hamid b. Hj. Mar Iman Former Director Centre for Real Estate Studies Faculty.
Jean Walrand EECS – U.C. Berkeley
Statistics Lecture 18. Will begin Chapter 5 today.
Probability Theory Part 2: Random Variables. Random Variables  The Notion of a Random Variable The outcome is not always a number Assign a numerical.
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.
1 Review of Probability Theory [Source: Stanford University]
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Course outline and schedule Introduction Event Algebra (Sec )
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Course outline and schedule Introduction (Sec )
Lecture II-2: Probability Review
Chapter 21 Random Variables Discrete: Bernoulli, Binomial, Geometric, Poisson Continuous: Uniform, Exponential, Gamma, Normal Expectation & Variance, Joint.
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.
Sampling Distributions  A statistic is random in value … it changes from sample to sample.  The probability distribution of a statistic is called a sampling.
Review of Probability.
1 Performance Evaluation of Computer Systems By Behzad Akbari Tarbiat Modares University Spring 2009 Introduction to Probabilities: Discrete Random Variables.
Pairs of Random Variables Random Process. Introduction  In this lecture you will study:  Joint pmf, cdf, and pdf  Joint moments  The degree of “correlation”
STAT 552 PROBABILITY AND STATISTICS II
Discrete Random Variables: PMFs and Moments Lemon Chapter 2 Intro to Probability
Probability for Estimation (review) In general, we want to develop an estimator for systems of the form: We will primarily focus on discrete time linear.
General information CSE : Probabilistic Analysis of Computer Systems
Random variables Petter Mostad Repetition Sample space, set theory, events, probability Conditional probability, Bayes theorem, independence,
Short Resume of Statistical Terms Fall 2013 By Yaohang Li, Ph.D.
CSE 531: Performance Analysis of Systems Lecture 2: Probs & Stats review Anshul Gandhi 1307, CS building
Winter 2006EE384x1 Review of Probability Theory Review Session 1 EE384X.
Lecture 12 Vector of Random Variables
Ch2: Probability Theory Some basics Definition of Probability Characteristics of Probability Distributions Descriptive statistics.
Lecture 6 Discrete Random Variables: Definition and Probability Mass Function Last Time Families of DRVs Cumulative Distribution Function (CDF) Averages.
Probability theory Petter Mostad Sample space The set of possible outcomes you consider for the problem you look at You subdivide into different.
STA347 - week 51 More on Distribution Function The distribution of a random variable X can be determined directly from its cumulative distribution function.
Multiple Random Variables Two Discrete Random Variables –Joint pmf –Marginal pmf Two Continuous Random Variables –Joint Distribution (PDF) –Joint Density.
Computer Vision Group Prof. Daniel Cremers Autonomous Navigation for Flying Robots Lecture 5.2: Recap on Probability Theory Jürgen Sturm Technische Universität.
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.
Probability Refresher. Events Events as possible outcomes of an experiment Events define the sample space (discrete or continuous) – Single throw of a.
Random Variables and Stochastic Processes – Lecture#13 Dr. Ghazi Al Sukkar Office Hours:
EE 5345 Multiple Random Variables
Lecture 14 Sums of Random Variables Last Time (5/21, 22) Pairs Random Vectors Function of Random Vectors Expected Value Vector and Correlation Matrix Gaussian.
IE 300, Fall 2012 Richard Sowers IESE. 8/30/2012 Goals: Rules of Probability Counting Equally likely Some examples.
Lecture 8 Continuous Random Variables Last Time Continuous Random Variables (CRVs) CDF Probability Density Functions (PDF) Expected Values Families of.
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.
CDA6530: Performance Models of Computers and Networks Chapter 3: Review of Practical Stochastic Processes.
Lecture 15 Parameter Estimation Using Sample Mean Last Time Sums of R. V.s Moment Generating Functions MGF of the Sum of Indep. R.Vs Sample Mean (7.1)
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.
ELEC 303, Koushanfar, Fall’09 ELEC 303 – Random Signals Lecture 9 – Continuous Random Variables: Joint PDFs, Conditioning, Continuous Bayes Farinaz Koushanfar.
SS r SS r This model characterizes how S(t) is changing.
Introduction to Probability and Bayesian Decision Making Soo-Hyung Kim Department of Computer Science Chonnam National University.
5 pair of RVs.
Chapter 5 Joint Probability Distributions and Random Samples  Jointly Distributed Random Variables.2 - Expected Values, Covariance, and Correlation.3.
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.
Random Variables By: 1.
Lecture 5 Discrete Random Variables: Definition and Probability Mass Function Last Time Reliability Problems Definitions of Discrete Random Variables Probability.
AP Statistics Chapter 8 Section 2. If you want to know the number of successes in a fixed number of trials, then we have a binomial setting. If you want.
Probabilistic Analysis of Computer Systems
Statistics Lecture 19.
Lecture 6 Continuous Random Variables-I
Simulation Statistics
Outline Introduction Signal, random variable, random process and spectra Analog modulation Analog to digital conversion Digital transmission through baseband.
Review of Probability Theory
TM 605: Probability for Telecom Managers
Probability & Statistics Probability Theory Mathematical Probability Models Event Relationships Distributions of Random Variables Continuous Random.
Statistics Lecture 12.
Chapter 2. Random Variables
CS723 - Probability and Stochastic Processes
Lecture 11 – Stochastic Processes
Presentation transcript:

Lecture 10 Pairs of Random Variables Last Time Pairs of R.Vs. Joint CDF Joint PMF Marginal PMF Reading Assignment: Chapter 4.1 – 4.3 Probability & Stochastic Processes Yates & Goodman (2nd Edition) NTUEE SCC_04_

Makeup Classes I will attend Networking 2009 in Aachen, Germany, and need to make-up the classes of 5/14 & 5/15 (3 hours) 4/30 17:30 – 18:20, 5/7 17:30 – 18:20, 5/8 8:10 – 9:00 Probability & Stochastic Processes Yates & Goodman (2nd Edition) NTUEE SCC_04_

Lecture 10: Pair of R.V.s This Week Pairs of R.Vs. Marginal PMF (Cont.) Joint PDF Marginal PDF Functions of Two R.Vs Expected Values Conditioning by an Event Reading Assignment: Sections Probability & Stochastic Processes Yates & Goodman (2nd Edition) NTUEE SCC_04_

Lecture 10: Pairs of R.Vs Next Week: Random Vectors Probability Models of N Random Variables Vector Notation Marginal Probability Functions Independence of R.Vs and Random Vectors Function of Random Vectors Expected Value Vector and Correlation Matrix Gaussian Random Vectors Sums of R. V.s Expected Values of Sums PDF of the Sum of Two R.V.s Moment Generating Functions Reading Assignment: Sections Probability & Stochastic Processes Yates & Goodman (2nd Edition) NTUEE SCC_04_

Brain Teaser Current level of influenza pandemic alert raised from phase 4 to 5 !! WHO 04/29/2009 Q1: What does the alert level mean? Q2: What is the probability that more than 10% of this class will be inffected?  Challenge: Can you use probability theory, computer and statistical data from the web to estimate Prob(>10% of this class infected in 6 months)?

Lessons from SARS Simulating SARS for Public Health Policy 孫春在 By Prof. 孫春在 (NTUEE79 系友 ) and his group Simulating SARS-short.ppt

日光下並無新事 ( 傳道書一、 9-10) Smallpox: inoculation or not? - One urgent social issue in France from 1750 to Over 10 percent London and Paris populations killed by Smallpox - Risky procedure using live smallpox pustules and could lead to death - Parisians’ choice: 1 in 7 longer-term chance of dying of smallpox versus 1 in 200 short-term chance of dying from the inoculation - Complicating issues: the generally small average life expectancy in Paris Faculty of Medicine and Theology, U. of Paris strongly against Daniel Bernoulli applied his probability model and analysis social_and_medical_statistics_Bernoulli.ppt - calculated the number of persons likely to be killed by smallpox in a given time. - calculated the gain in life expectancy from inoculation for any given age - favoured inoculation Jean D'Alembert disagreed with the analysis Source: ppt

Chapter 4 Pairs of Random Variables

Recall the Definition of a R.V

What about these two experiments? Example 2: X Y H T

(Relate to what you learned in Chapter 1: P(A) =  P(A|Bi)P(Bi) =  P(A, Bi), where Bi are mutually exlusive and exhaustive)

Example: If a men and his fiancée are scheduled to meet between 11:30 and 12:00. Suppose that they arrive at random times. What is the probability that they meet within 10 minutes of arrival?

Q: What would you do to develop the theory for a pair of C.R.Vs