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  • ECE661Fall2008Kak
    If the brightness values in the x and y directions are thought of as random variables then C is a scaled version of their covariance matrix.
    14 KB (2,253 words) - 12:21, 9 January 2009
  • Lecture Schedule (ECE438BoutinSpring09)
    ...le="padding-right: 1em;" | Friday || 02/27/09 || Circular convolution, one random variable || 1.6.5., 3.1.1 ...ign="right" style="padding-right: 1em;" | Monday || 03/02/09 || two random variables || 3.1.2
    6 KB (689 words) - 07:59, 2 August 2010
  • Reading guide (ECE438BoutinSpring2009)
    ...an/ece438/lecture/module_1/1.1_signals/1.1.5_complex_variables.pdf complex variables] ==Random sequences ==
    8 KB (1,226 words) - 11:40, 1 May 2009
  • Meta Course List
    *[[ECE600|ECE 600]]: "Random Variables and Stochastic Processes"
    4 KB (474 words) - 07:08, 4 November 2013
  • Homework4 ECE302Fall2008sanghavi
    Let <math>X</math> denote a binomial random variable with parameters <math>(N, p)</math>. *(a) Show that <math>Y = N - X</math> is a binomial random variable with parameters <math>(N,1-p)</math>
    6 KB (883 words) - 12:55, 22 November 2011
  • Cheat1 ECE302Fall2008sanghavi
    '''Definition and basic concepts of random variables, PMFs''' Random Variable: a map/function from outcomes to real values
    3 KB (525 words) - 13:04, 22 November 2011
  • 4.3a Priyanka Savkar ECE302Fall2008sanghavi
    This part deals with Binomial Random Variables.
    401 B (68 words) - 15:04, 23 September 2008
  • 4.3 Joon Young Kim ECE302Fall2008sanghavi
    ...figure out what the point of this question, Is W one of the common random variables we have seen in class?, is. Is any way that I can prove that W is one of the common random variables?
    532 B (101 words) - 05:43, 24 September 2008
  • Homework5 ECE302Fall2008sanghavi
    ...e coupons in it, with all being equally likely. Let <math>X</math> be the (random) number of candy bars you eat before you have all coupons. What are the mea ...t is the PDF of <math>Y</math>? Is <math>Y</math> one of the common random variables?
    4 KB (656 words) - 12:56, 22 November 2011
  • Homework6 ECE302Fall2008sanghavi
    <math>X</math> is an exponential random variable with paramter <math>\lambda</math>. <math>Y = \mathrm{ceil}(X)</ma What is the PMF of <math>Y</math>? Is it one of the common random variables? (Hint: for all <math>k</math>, find the quantity <math>P(Y > k)</math>. T
    3 KB (449 words) - 12:57, 22 November 2011
  • Homework7 ECE302Fall2008sanghavi
    == Problem 1: Arbitrary Random Variables == Let <math>U</math> be a uniform random variable on [0,1].
    4 KB (596 words) - 12:57, 22 November 2011
  • Steven Streeter 6.4 ECE302Fall2008sanghavi
    ...comes (1/2)*e^(-d/2) which is the pdf. And, D is one of the common random variables because our pdf's are exponential with parameter lambda = 1/2.
    297 B (54 words) - 12:54, 16 October 2008
  • Cheat2 ECE302Fall2008sanghavi
    * For Continuous Random Variable: ==Theorem of Total Probability for Continuous Random Variables==
    4 KB (722 words) - 13:05, 22 November 2011
  • 7.4 Katie Pekkarinen ECE302Fall2008sanghavi
    The PDF of the sum of two independent random variables is the convolution of the two PDFs. The lecture notes from 10/10 are helpf
    133 B (23 words) - 19:13, 19 October 2008
  • Gregory Pajot 7.3 ECE302Fall2008sanghavi
    ...ding P[H2|H1], and H2 and H1 are both events rather than continuous random variables, we can do this. We don't have to worry about finding the conditional PDF
    333 B (64 words) - 10:26, 20 October 2008
  • Divyanshu Kamboj 7.3 ECE302Fall2008sanghavi
    ...e former is denoted P(A|X = 0) and the latter P(A|X = 1). Now define a new random variable Y, whose value is P(A|X = 0) if X = 0 and P(A|X = 1) if X = 1. Tha ...s said to be the conditional probability of the event A given the discrete random variable X:
    2 KB (332 words) - 16:52, 20 October 2008
  • Seraj Dosenbach ECE302Fall2008sanghavi
    We create variables : Therefore, in c to produce a random variable with a gaussian distribution you simply do the following
    560 B (112 words) - 18:03, 20 October 2008
  • Emir Kavurmacioglu ECE302Fall2008sanghavi
    OK, so what we have initially is a uniform random variable on the interval [0,1]. ...exponential random variable with λ=0.5 is made out of two gaussian random variables with the relationship '''<math>D=X^2+Y^2</math>'''
    1 KB (186 words) - 11:47, 21 October 2008
  • Homework10 ECE302Fall2008sanghavi
    == Problem 1: Random Point, Revisited== In the following problems, the random point (X , Y) is uniformly distributed on the shaded region shown.
    4 KB (703 words) - 12:58, 22 November 2011
  • ECE662:Glossary Old Kiwi
    ...observed should be the sum or mean of many independent random variables. (variables need not be iid)(See the PROOF ) undirected graphs (Markov random fields), probabilistic decision trees/models have a number of
    31 KB (4,832 words) - 18:13, 22 October 2010
  • ECE662:Homework 1 Old Kiwi
    ...ormally distributed random numbers : ex) RANDN(N) is an N-by-N matrix with random entries, chosen from a normal distribution with mean zero, variance one and ...ro generate a vecort of n-gaussian random variables ? can this be called a random vector ? BAsically my question is how do we simulate gaussian data whcih h
    10 KB (1,594 words) - 11:41, 24 March 2008
  • Lecture 15 - Parzen Window Method Old Kiwi
    ...ce of random variables since <math>p_i(\vec{x_0})</math> depends on random variables |sample_space_i|. What do we mean by convergence of a sequence of random variables (There are many definitions). We pick "Convergence in mean square" sense, i
    7 KB (1,212 words) - 08:38, 17 January 2013
  • Parametric Estimators Old Kiwi
    Deterministic (single, non-random) estimate of parameters, theta_ML ...Bayesian formulation, the parameters to be estimated are treated as random variables. The Bayes estimate is the one that minimizes the Bayes risk by minimizing
    6 KB (995 words) - 10:39, 20 May 2013
  • Publications Old Kiwi
    which datasets with tens or hundreds of thousands of variables are available. These areas include ...on for each criterion is compared with the optimal two-group separation of variables found by total enumeration of the possible groupings.
    39 KB (5,715 words) - 10:52, 25 April 2008
  • Gaussian distribution Old Kiwi
    ...imit Theorem`_ says that sum of independent identically distributed random variables approximate the normal distribution. So, considering the pattern recognitio The following histograms of N uniformly distributed random variables for different values of N can be given to visualize the [http://en.wikipedi
    2 KB (247 words) - 08:32, 10 April 2008
  • Central Limit Theorem Old Kiwi
    ...iable" being observed should be the sum or mean of many independent random variables.
    213 B (35 words) - 10:01, 31 March 2008
  • Principal Component Analysis Old Kiwi
    ...he principal components of a data set. The principal components are random variables of maximal variance constructed from linear combinations of the input featu
    657 B (104 words) - 01:45, 17 April 2008
  • EE662Sp10BayesExample
    ...</math> and <math>\mathbb{Y}</math> be jointly distributed discrete random variables with ranges <math>X = \{0, 1, 2, 3, 4\}</math> and <math>Y = \{0, 1, 2\}</m
    7 KB (948 words) - 04:35, 2 February 2010
  • Probability Formulas
    ...e-policy: -moz-initial;" colspan="2" | Expectation and Variance of Random Variables | align="right" style="padding-right: 1em;" | Binomial random variable with parameters n and p
    3 KB (491 words) - 12:54, 3 March 2015
  • Snikitha ListECE302
    ...tral density functions. Random processes and response of linear systems to random inputs.<br/><br/> <br/>ii. an ability to model complex families of signals by means of random processes.
    2 KB (231 words) - 07:20, 4 May 2010
  • Noteslecture8ECE662S10
    let X1,X2,...,Xn be n independent and identically distributed variables (i.i.d) with finite mean <math>\mu</math> and finite variance <math>\sigma^ More precisely the random variable <math>Z_n = \frac{\Sigma_{i=1}^n X_i - n \mu}{\sigma \sqrt{n}}</ma
    5 KB (806 words) - 09:08, 11 May 2010
  • HWK2MA527Fall2010
    ...I reduced it to [1 2 3; 0 -3 -3]. I'm not even sure whether plugging in random values was the right idea, but I'm stuck here. How do I proceed from here? ...That's like doing an experiment in science. You'd have to plug in lots of random values if you were doing science, but you'd miss the key points in math. Y
    4 KB (756 words) - 04:25, 8 September 2010
  • 2010 Fall ECE 600 Comer
    :*[[ECE 600 Sequences of Random Variables|ECE 600 Sequences of Random Variables]]
    2 KB (250 words) - 10:07, 16 December 2010
  • Decision Theory Glossary ECE662Spring2008
    ...observed should be the sum or mean of many independent random variables. (variables need not be iid)(See the PROOF ) undirected graphs (Markov random fields), probabilistic decision trees/models have a number of
    31 KB (4,787 words) - 18:21, 22 October 2010
  • ECE
    *[[2010_Fall_ECE_600_Comer|ECE 600]]: "Random Variables and Stochastic Processes"
    3 KB (380 words) - 18:29, 9 January 2015
  • ECE600
    = [[ECE]] 600: Random Variables and Stochastic Processes = :*[[ECE 600 Sequences of Random Variables|2. Sequences of Random Variables]]
    2 KB (238 words) - 12:14, 25 September 2013
  • ECE PhD QE CNSIP 2001 Problem1.1
    [[Category:random variables]] Question 1: Probability and Random Processes
    2 KB (273 words) - 17:40, 13 March 2015
  • ECE PhD QE CNSIP 2001 Problem1.2
    [[Category:random variables]] Question 1: Probability and Random Processes
    1 KB (191 words) - 17:42, 13 March 2015
  • ECE PhD QE CNSIP 2001 Problem1.3
    [[Category:random variables]] Question 1: Probability and Random Processes
    5 KB (928 words) - 17:46, 13 March 2015
  • ECE600 Example Addition of two independent Poisson random variables
    =Addition of two independent Poisson random variables = ...athbf{X}</math> and <math>\mathbf{Y}</math> are independent Poisson random variables with means <math>\lambda</math> and <math>\mu</math>, respectively.
    3 KB (557 words) - 12:11, 25 September 2013
  • ECE600 Example Two jointly distributed random variables
    == Example. Two jointly distributed random variables == Two joinly distributed random variables <math>\mathbf{X}</math> and <math>\mathbf{Y}</math> have joint pdf
    7 KB (1,103 words) - 05:27, 15 November 2010
  • ECE600 Example Addition of two independent Gaussian random variables
    == Example. Addition of two independent Gaussian random variables == ...is the pdf you determined in part (b)? What is the mean and variance of a random variable with this pdf?
    6 KB (939 words) - 04:20, 15 November 2010
  • ECE600 Example Addition of multiple independent Exponential random variables
    == Example. Addition of multiple independent Exponential random variables == ...h parameter <math>\lambda</math> and <math>\mathbf{N}</math> is Geometric random variable with parameter <math>p</math>. Find the distribution of <math>\mat
    2 KB (268 words) - 04:18, 15 November 2010
  • ECE 600 Prerequisites
    [[Category:random variables]] *[[ECE 600 Prerequisites Discrete Random Variables|Discrete Random Variables]]
    1 KB (139 words) - 13:13, 16 November 2010
  • ECE 600 Prerequisites Discrete Random Variables
    =='''1.4 Discrete Random Variables'''== ...}_{2},\cdots</math> are i.i.d. Bernoulli random variables, then Binomial random variable is defined as <math class="inline">\mathbf{X}=\mathbf{Y}_{1}+\math
    5 KB (921 words) - 11:25, 30 November 2010
  • ECE 600 Prerequisites Poisson Random Process
    ...s="inline">\mathbf{Y}_{1},\mathbf{Y}_{2},\cdots</math> are i.i.d. random variables. <math class="inline">\mathbf{N}\left(t\right)</math> is Poisson process <
    5 KB (920 words) - 11:26, 30 November 2010
  • ECE 600 Prerequisites Continuous Random Variables
    ='''1.6 Continuous Random Variables'''= ...tribution, then <math class="inline">\mathbf{Y}=\ln\mathbf{X}</math> is a random variable with Gaussian distribution. This distribution is characterized wit
    5 KB (843 words) - 11:27, 30 November 2010
  • ECE 600 Prerequisites Two Random Variables
    ='''1.10 Two Random Variables'''= ...bf{Y}</math> be two jointly-distributed, statistically independent random variables, having pdfs <math class="inline">f_{\mathbf{X}}\left(x\right)</math> and
    6 KB (952 words) - 11:31, 30 November 2010
  • ECE 600 Sequences of Random Variables
    [[Category:random variables]] =Sequences of Random Variables=
    1 KB (194 words) - 11:35, 30 November 2010
  • ECE 600 Convergence
    Given a random sequence <math class="inline">\mathbf{X}_{1}\left(\omega\right),\mathbf{X}_ We say a sequence of random variables converges everywhere (e) if the sequence <math class="inline">\mathbf{X}_{1
    10 KB (1,667 words) - 11:37, 30 November 2010

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