• .../vise.www.ecn.purdue.edu/VISE/ee438L/lab1/pdf/lab1.pdf Lab on discrete and continuous signals] ==Random sequences ==
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  • * For Continuous Random Variable: ==Theorem of Total Probability for Continuous Random Variables==
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  • ...r 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. That is ...to be the conditional probability of the event A given the discrete random variable X:
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  • <math>f_x(X; \theta)</math> (continuous) <math>P_\theta(\theta)</math> (continuous)
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  • == Problem 1: Random Point, Revisited== In the following problems, the random point (X , Y) is uniformly distributed on the shaded region shown.
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  • ...dom variable" being observed should be the sum or mean of many independent random variables. (variables need not be iid)(See the PROOF ) ...nly statistical model that is needed is the conditional model of the class variable given the measurement. This conditional model can be obtained from a joint
    31 KB (4,832 words) - 18:13, 22 October 2010
  • ...-j 2 \pi f t} dt</math> <br> A CTFT( continuous time fourier transform) is continuous in both the time and frequency domain. Give example here. ...(samples per second). &nbsp;Then the DTFT provides an approximation of the continuous time Fourier transform.
    13 KB (2,348 words) - 13:25, 2 December 2011
  • ...dom variable" being observed should be the sum or mean of many independent random variables. (variables need not be iid)(See the PROOF ) ...nly statistical model that is needed is the conditional model of the class variable given the measurement. This conditional model can be obtained from a joint
    31 KB (4,787 words) - 18:21, 22 October 2010
  • ='''1.6 Continuous Random Variables'''= ...on, then <math class="inline">\mathbf{Y}=\ln\mathbf{X}</math> is a random variable with Gaussian distribution. This distribution is characterized with two par
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  • 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">\ma
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  • ...ead of mapping each <math class="inline">\omega\in\mathcal{S}</math> of a random experiment to a number <math class="inline">\mathbf{X}\left(\omega\right)</ ...andom about the sample functions. The randomness comes from the underlying random experiment.
    16 KB (2,732 words) - 11:47, 30 November 2010
  • ...1 dime. One of the boxes is selected at random, and a coin is selected at random from that box. The coin selected is a quater. What is the probability that – A = Box selected at random contains at least one dime.
    22 KB (3,780 words) - 07:18, 1 December 2010
  • ...<math class="inline">q=1-p</math> . Given that a set of twins selected at random are of the same sex, what is the probability they are fraternal? ...h class="inline">\mathbf{X}</math> in a baseball game is a Poisson random variable. If the probability of a no-hit game is 1/3 , what is the probability of ha
    12 KB (2,205 words) - 07:20, 1 December 2010
  • ...irst coin is fair and the second coin has two heads. One coin is picked at random and tossed two times. It shows heads both times. What is the probability th ...mathbf{Y}_{t}</math> by jointly wide sense stationary continous parameter random processes with <math class="inline">E\left[\left|\mathbf{X}\left(0\right)-\
    9 KB (1,534 words) - 08:33, 27 June 2012
  • ...>\mathbf{Y}</math> be two independent identically distributed exponential random variables having mean <math class="inline">\mu</math> . Let <math class="in ...deals with the exponential random variable rather than the Poisson random variable.
    14 KB (2,358 words) - 08:31, 27 June 2012
  • ...th> and <math class="inline">\mathbf{Y}</math> be two joinly distributed random variables having joint pdf Let <math class="inline">\mathbf{Z}</math> be a new random variable defined as <math class="inline">\mathbf{Z}=\mathbf{X}+\mathbf{Y}</math> . F
    9 KB (1,560 words) - 08:30, 27 June 2012
  • =Example. Sequence of binomially distributed random variables= ...omially distributed random variables, with the <math>n_{th}</math> random variable <math>\mathbf{X}_{n}</math> having pmf
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  • =Example. Sequence of binomially distributed random variables= ...uted random variables, with the <math class="inline">n_{th}</math> random variable <math class="inline">\mathbf{X}_{n}</math> having pmf
    3 KB (539 words) - 12:14, 30 November 2010
  • ...stem [[Video Tutorial on How to Cascade Transformations of the Independent Variable|cascade]]: <br>x(t) = the amount of protein released by damaged endothelium ...ls require filtering of background noise and, often times, conversion from continuous time (CT) to discrete time (DT).<br>
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  • *Discrete Random Variables ...02S13Boutin|Normalizing the probability mass function of a discrete random variable]]
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  • [[Category:random variables]] Question 1: Probability and Random Processes
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  • [[Category:random variables]] Question 1: Probability and Random Processes
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  • [[Category:random variables]] Question 1: Probability and Random Processes
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  • Probability, Statistics, and Random Processes for Electrical Engineering, 3rd Edition, by Alberto Leon-Garcia, *Discrete Random Variables
    10 KB (1,422 words) - 20:14, 30 April 2013
  • ==Part 2: Discrete Random Variables (To be tested in the second intra-semestrial exam)== *2.2 Functions of a discrete random variable
    4 KB (498 words) - 10:18, 17 April 2013
  • ...III of the material with a definition of the concept of "continuous random variable" along with two examples.
    2 KB (321 words) - 11:12, 15 February 2013
  • [[Category:discrete random variable]] ...Problem]]: normalizing the probability mass function of a discrete random variable=
    2 KB (355 words) - 13:50, 13 February 2013
  • ...at an example of continuous random variable, namely the exponential random variable.
    2 KB (329 words) - 08:16, 20 February 2013
  • In Lecture 19, we continued our discussion of continuous random variables. ...nvent a problem about the expectation and/or variable of a discrete random variable]]
    2 KB (252 words) - 08:20, 20 February 2013
  • ...us and discrete) and we began discussing normally distributed (continuous) random variables. ...02S13Boutin|Normalizing the probability mass function of a Gaussian random variable]]
    2 KB (304 words) - 07:43, 23 February 2013
  • [[Category:continuous random variable]] ...roblem]]: normalizing the probability mass function of a continuous random variable=
    3 KB (519 words) - 08:11, 25 February 2013
  • [[Category:continuous random variable]] ...roblem]]: normalizing the probability mass function of a continuous random variable=
    2 KB (288 words) - 15:49, 22 March 2013
  • [[Category:continuous random variable]] ...roblem]]: normalizing the probability mass function of a continuous random variable=
    2 KB (401 words) - 04:52, 4 March 2013
  • [[Category:continuous random variable]] ...roblem]]: normalizing the probability mass function of a continuous random variable=
    2 KB (269 words) - 04:58, 4 March 2013
  • [[Category:continuous random variable]] ...|Practice Problem]]: compute the zero-th order moment of a Gaussian random variable=
    1 KB (214 words) - 04:47, 4 March 2013
  • [[Category:continuous random variable]] ...|Practice Problem]]: compute the zero-th order moment of a Gaussian random variable=
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  • [[Category:continuous random variable]] [[Category:gaussian random variable
    2 KB (216 words) - 05:48, 24 March 2013
  • [[Category:continuous random variable]] [[Category:uniform random variable]]
    2 KB (284 words) - 11:49, 26 March 2013
  • [[Category:continuous random variable]] [[Category:uniform random variable]]
    1 KB (157 words) - 11:59, 26 March 2013
  • [[Category:continuous random variable]] [[Category:uniform random variable]]
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  • [[Category:continuous random variable]] Let X be a continuous random variable with probability density function
    2 KB (299 words) - 09:17, 27 March 2013
  • ...pdf of a random variable Y defined as a function Y=g(X) of another random variable X.
    2 KB (328 words) - 04:58, 9 March 2013
  • ...iable. We also discussed the problem of recovering the pdf/pmf of a random variable from its moment generating function. ...CE302S13Boutin|Obtain the characteristic function of an exponential random variable]]
    2 KB (336 words) - 09:39, 18 March 2013
  • ...:Problem solving|Practice Problem]]: PDF for a linear function of a random variable = ...ed constants a,b, with <math>a\neq 0</math>. What is the pdf of the random variable Y?
    2 KB (249 words) - 19:36, 27 March 2013
  • ...and let Y be the arrival time of the professor. Assume that the 2D random variable (X,Y) is uniformly distributed in the square [2 , 3]x[2,3]. '''2.''' Let (X,Y) be a 2D random variable that is uniformly distributed in the rectangle [1,3]x[5,10].
    3 KB (559 words) - 07:02, 22 March 2013
  • ...the next lecture to fully understand the relationship between the Poisson random process and the binomial counting process.
    3 KB (395 words) - 06:31, 15 April 2013
  • Topic: Expectation of continuous RV *A random variable X has the following probability density function:
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  • [[ECE600_F13_rv_distribution_mhossain|Next Topic: Random Variables: Distributions]] [[ECE600_F13_notes_mhossain|'''The Comer Lectures on Random Variables and Signals''']]
    7 KB (1,194 words) - 12:11, 21 May 2014
  • [[ECE600_F13_rv_definition_mhossain|Previous Topic: Random Variables: Definitions]]<br/> [[ECE600_F13_notes_mhossain|'''The Comer Lectures on Random Variables and Signals''']]
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  • [[ECE600_F13_rv_distribution_mhossain|Previous Topic: Random Variables: Distributions]]<br/> ...rv_Functions_of_random_variable_mhossain|Next Topic: Functions of a Random Variable]]
    6 KB (1,109 words) - 12:11, 21 May 2014

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