ECEN4503 Random Signals Lecture #18 24 February 2014 Dr. George Scheets n Read 5.3 & 5.5 n Problems 5.1, 5.4, 5.11 n Exam #1 Friday n Quiz 4 Results Hi.

Slides:



Advertisements
Similar presentations
CS 410 Applied Algorithms Applied Algorithms Lecture #7 Counting.
Advertisements

Chapter 8: Prediction Eating Difficulties Often with bivariate data, we want to know how well we can predict a Y value given a value of X. Example: With.
Lectures prepared by: Elchanan Mossel Yelena Shvets Introduction to probability Stat 134 FAll 2005 Berkeley Follows Jim Pitman’s book: Probability Section.
Section 7.4 (partially). Section Summary Expected Value Linearity of Expectations Independent Random Variables.
ECEN5533 Modern Communications Theory Lecture #119 August 2014 Dr. George Scheets n Review Chapter
 What is the equation of the line, in slope- intercept form, that passes through (2, 4) and is perpendicular to 5x+7y-1=0.
1 ECE310 – Lecture 23 Random Signal Analysis 04/27/01.
Jointly distributed 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”
ECEN3714 Network Analysis Lecture #9 2 February 2015 Dr. George Scheets n Read 13.8 n Problems: 13.16a, 19a,
ECEN5633 Radar Theory Lecture #16 5 March 2015 Dr. George Scheets n Read 12.1 n Problems 11.1, 3, & 4 n Corrected.
ECEN3714 Network Analysis Lecture #6 26 January 2015 Dr. George Scheets n Read 13.5 n Problems: 13.8, 10, 12.
Great Theoretical Ideas In Computer Science Steven Rudich, Anupam GuptaCS Spring 2004 Lecture 22April 1, 2004Carnegie Mellon University
1 Chapter 8: The Binomial and Geometric Distributions 8.1Binomial Distributions 8.2Geometric Distributions.
SWBAT… define and evaluate functions Agenda 1. Warm-Up (10 min) 2. Review hw#3 & hw#4 (25 min) Warm-Up: 1.) How would the graph of y = |x| + 5 transform.
Topic 1: Descriptive Statistics CEE 11 Spring 2001 Dr. Amelia Regan These notes draw liberally from the class text, Probability and Statistics for Engineering.
ECEN4503 Random Signals Lecture #39 21 April 2014 Dr. George Scheets n Read 10.1, 10.2 n Problems: 10.3, 5, 7, 12,14 n Exam #2 this Friday: Mappings →
Two Random Variables W&W, Chapter 5. Joint Distributions So far we have been talking about the probability of a single variable, or a variable conditional.
Chapters 7 and 10: Expected Values of Two or More Random Variables
Great Theoretical Ideas in Computer Science.
4.1 Probability Distributions. Do you remember? Relative Frequency Histogram.
Lesson Objective: Understand what an algorithm is and be able to use them to solve a simple problem.
ECEN4533 Data Communications Lecture #1511 February 2013 Dr. George Scheets n Review C.1 - C.3 n Problems: Web 7, 8, & 9 n Quiz #1 < 11 February (Async.
Chapter 5 The Binomial Probability Distribution and Related Topics.
Unit 9: Probability, Statistics and Percents Section 1: Relative Frequency and Probability The frequency of something is how often it happens Relative.
Probability Distributions. We need to develop probabilities of all possible distributions instead of just a particular/individual outcome Many probability.
ECEN4523 Commo Theory Lecture #10 9 September 2015 Dr. George Scheets n Read Chapter 3.6 – n Problems:
Solve by using the ELIMINATION method The goal is to eliminate one of the variables by performing multiplication on the equations. Multiplication is not.
Continuous Random Variables Lecture 25 Section Mon, Feb 28, 2005.
Digital Image Processing (Digitaalinen kuvankäsittely) Exercise 2
MM1D2d: Use expected value to predict outcomes
ECEN4503 Random Signals Lecture #6 26 January 2014 Dr. George Scheets n Read: 3.2 & 3.3 n Problems: 2.28, 3.3, 3.4, 3.7 (1 st Edition) n Problems: 2.61,
1 Topic 5 - Joint distributions and the CLT Joint distributions –Calculation of probabilities, mean and variance –Expectations of functions based on joint.
ECEN4503 Random Signals Lecture #24 10 March 2014 Dr. George Scheets n Read 8.1 n Problems , 7.5 (1 st & 2 nd Edition) n Next Quiz on 28 March.
A life insurance company sells a term insurance policy to a 21-year-old male that pays $100,000 if the insured dies within the next 5 years. The probability.
COMPSCI 102 Introduction to Discrete Mathematics.
ECEN4503 Random Signals Lecture #1 & 2; 13 & 15 January 2014 n Read Chapter 1 n Read Sections n Problems 2.3, 2.8, 2.9, 2.10 (1 ST edition) n.
ECEN5633 Radar Theory Lecture #17 10 March 2015 Dr. George Scheets n Read 12.2 n Problems 11.5, 8, & 12.5 n Corrected.
ECEN5633 Radar Theory Lecture #3 20 January 2015 Dr. George Scheets n Read 2.1 & 2.5 n Problems 1.11, 14, & 16.
Modeling Discrete Variables Lecture 22, Part 1 Sections 6.4 Fri, Oct 13, 2006.
Computer simulation Sep. 9, QUIZ 2 Determine whether the following experiments have discrete or continuous out comes A fair die is tossed and the.
ECEN4533 Data Communications Lecture #1818 February 2013 Dr. George Scheets n Problems: 2011 Exam #1 n Corrected Design #1 u Due 18 February (Live) u 1.
ECEN4503 Random Signals Lecture #39 15 April 2013 Dr. George Scheets n Read: 10.3, 11.1 n Problems: 11.1, 11.4, 11.15, (1 st Edition) n Problems:
Great Theoretical Ideas in Computer Science for Some.
BINOMIAL AND GEOMETRIC OLYMPICS!!!! Sit with your group and choose a team name. Preferably something corny that has to do with statistics Today’s game.
ECEN5533 Modern Communications Theory Lecture #111 January 2016 Dr. George Scheets n Review Chapter
Combining Two Random Variables: Means and Variances Lesson
Continuous Random Variables Lecture 24 Section Tue, Oct 18, 2005.
ECEN3513 Signal Analysis Lecture #4 28 August 2006 n Read section 1.5 n Problems: 1.5-2a-c, 1.5-4, & n Quiz Friday (Chapter 1 and/or Correlation)
ECEN4523 Commo Theory Lecture #38 16 November 2015 Dr. George Scheets n Read 8.6 & 8.7 n Problems: 8.6-1, 3,
ECEN4523 Commo Theory Lecture #29 26 October 2015 Dr
ECEN4523 Commo Theory Lecture #42 30 November 2015 Dr. George Scheets n Read 11.3 n Problems: & 4 n Final.
ECEN4503 Random Signals Lecture #30 31 March 2014 Dr. George Scheets n Problems 8.7a & b, 8.11, 8.12a-c (1st Edition) n Problems 8.11a&b, 8.15, 8.16 (2nd.
ECEN5533 Modern Commo Theory Lesson # February 2016 Dr
Oliver Schulte Machine Learning 726
ENGS2613 Intro Electrical Science Week 15 Dr. George Scheets
Continuous Random Variables
ECEN5533. Modern Communications Theory Lecture #12. 8 February 2016 Dr
ECEN5533. Modern Communications Theory Lecture #6. 25 January 2016 Dr
Suppose you roll two dice, and let X be sum of the dice. Then X is
Chapter 5-1 Exponents.
Review
Spatial operations and transformations
Calculus II (MAT 146) Dr. Day Friday, Oct 7, 2016
III. More Discrete Probability Distributions
Further Topics on Random Variables: Derived Distributions
Further Topics on Random Variables: Derived Distributions
Berlin Chen Department of Computer Science & Information Engineering
Further Topics on Random Variables: Derived Distributions
Spatial operations and transformations
Presentation transcript:

ECEN4503 Random Signals Lecture #18 24 February 2014 Dr. George Scheets n Read 5.3 & 5.5 n Problems 5.1, 5.4, 5.11 n Exam #1 Friday n Quiz 4 Results Hi = 10.0, Low = 2.3, Average = 8.29 σ = 1.89

ECEN4503 Random Signals Lecture #19 26 February 2014 Dr. George Scheets n Read 5.6 & 5.7 n Problems: 5.8abd, 5.12, 5.16 n Test next time!!

Exam #1 n Covers Chapter 1 – 5.3 (text) n Day #1 - 2nd Order PDF's (notes) n Open books, notes. n Closed instructor, neighbor. n For All Quizzes & Tests... u Calculators (highly recommended) u Smart Phones are NOT allowed

Possible Exam Topics S Reading Chapters HW Lectures Anything in the circles is fair game.

Budget Your Time!!!!!!!!!!! n 4 page exam, 25 points per page n Instructor not available for hints n Typically u 1 page solvable < minutes u 2 pages solvable in minutes u 1 page to separate Men from Boys & Girls from Women n Recommendation u 1st pass: Spend < minutes/page u 2nd pass: Go to problem(s) you know best

2nd Order PDF's n Volume should equal 1 n To get marginal PDF f X (x) or f Y (Y) u Integrate out variable you don't want f XY (x,y) dx dy = 1 f XY (x,y) dy = f X (x)

3D Histogram Probabilities will sum to Age Weight 11 f AW (a,w) dw = f A (a)f AW (21,w) dw = f A (21)

Marginal PDF - Age 30/85 20/85 10/85 Probability

Age & Middle Finger Length S.I.? Linear Regression Length = *Age If S.I., slope = 0

S.I. Random Variables n P(X∩Y) = P(X)P(Y) n f XY (x,y) = f X (x) f Y (y) n E[XY] =E[X]E[Y] u ∫∫xy f XY (x,y)dxdy

Sum of S.I. Random Variables n Given a noise voltage N... Time Volts 0

Bin Count Time Volts

...With Uniform Voltage PDF... n volts f N (n) 0+1 1/2 Suppose you add the number 6 to this. What's the output PDF of N+6?

Time domain signal is now... Time Volts 6

PDF of Y = N + 6 y volts f Y (y) /2

f Y (y) = f N (n) f 6 (n) n volts f N (n) 0+1 1/2 n volts f 6 (n) +6 1 Convolution of an arbitrary function with a delta function Shifts and centers function's origin to delta function's location.

Add together two die rolls x dots f die roll #1 (x) /6 x dots f die roll #2 (x) /6 Shift & center origin of arbitrary function (delta functions on left) to location of delta functions on the right. Multiply. Repeat 6 times. Add overlaps.

Add together two S.I. die rolls y dots f sum (y) /36 4/36 2/36 1/36 3/36 5/36

Matlab Convolution Tool n This code can help you understand convolution. Available near the bottom of the link below. n This code can help you understand convolution. Available near the bottom of the link below.