Create the page "Random variables" on this wiki! See also the search results found.

  • ECE PhD QE CNSIP 2002 Problem1
    [[Category:random variables]] Question 1: Probability and Random Processes
    5 KB (766 words) - 00:16, 10 March 2015
  • ECE PhD QE CNSIP 2003 Problem1
    [[Category:random variables]] Question 1: Probability and Random Processes
    5 KB (729 words) - 00:51, 10 March 2015
  • ECE PhD QE CNSIP 2004 Problem1
    [[Category:random variables]] Question 1: Probability and Random Processes
    5 KB (735 words) - 01:17, 10 March 2015
  • ECE PhD QE CNSIP 2005 Problem1
    [[Category:random variables]] Question 1: Probability and Random Processes
    4 KB (609 words) - 01:54, 10 March 2015
  • ECE PhD QE CNSIP 2006 Problem1
    [[Category:random variables]] Question 1: Probability and Random Processes
    5 KB (726 words) - 10:35, 10 March 2015
  • ECE PhD QE CNSIP 2008 Problem1
    [[Category:random variables]] Question 1: Probability and Random Processes
    4 KB (632 words) - 11:05, 10 March 2015
  • ECE PhD QE CNSIP Jan 2002 Problem1
    [[Category:random variables]] Question 1: Probability and Random Processes
    4 KB (643 words) - 11:16, 10 March 2015
  • ECE PhD QE CNSIP Jan 2001 Problem1
    [[Category:random variables]] Question 1: Probability and Random Processes
    4 KB (616 words) - 10:19, 13 September 2013
  • ECE PhD QE CNSIP Jan 2006 Problem1
    [[Category:random variables]] Question 1: Probability and Random Processes
    4 KB (572 words) - 10:24, 10 March 2015
  • ECE PhD QE CNSIP Jan 2004 Problem1
    [[Category:random variables]] Question 1: Probability and Random Processes
    5 KB (748 words) - 01:01, 10 March 2015
  • ECE PhD QE CNSIP 2000 Problem1.1
    [[Category:random variables]] Question 1: Probability and Random Processes
    2 KB (358 words) - 10:33, 13 September 2013
  • ECE PhD QE CNSIP 2000 Problem1.2
    [[Category:random variables]] Question 1: Probability and Random Processes
    4 KB (638 words) - 10:34, 13 September 2013
  • ECE PhD QE CNSIP 2000 Problem1.3
    [[Category:random variables]] Question 1: Probability and Random Processes
    2 KB (248 words) - 10:34, 13 September 2013
  • ECE PhD QE CNSIP 2000 Problem1.4
    [[Category:random variables]] Question 1: Probability and Random Processes
    2 KB (282 words) - 10:34, 13 September 2013
  • ECE PhD QE CNSIP Jan 2001 Problem1.1
    [[Category:random variables]] Question 1: Probability and Random Processes
    3 KB (532 words) - 10:36, 13 September 2013
  • ECE PhD QE CNSIP Jan 2001 Problem1.2
    [[Category:random variables]] Question 1: Probability and Random Processes
    4 KB (655 words) - 10:36, 13 September 2013
  • ECE PhD QE CNSIP Jan 2001 Problem1.3
    [[Category:random variables]] Question 1: Probability and Random Processes
    2 KB (357 words) - 10:36, 13 September 2013
  • ECE PhD QE CNSIP Jan 2001 Problem1.4
    [[Category:random variables]] Question 1: Probability and Random Processes
    2 KB (234 words) - 10:37, 13 September 2013
  • ECE PhD QE CNSIP Jan 2001 Problem1.5
    [[Category:random variables]] Question 1: Probability and Random Processes
    3 KB (496 words) - 10:37, 13 September 2013
  • ECE-QE CS1-2011
    [[Category:random variables]] Question 1: Probability and Random Processes
    4 KB (547 words) - 16:40, 30 March 2015
  • Https://www.projectrhea.org/rhea/index.php/ECE-QE CS1-2011 solusion-1
    ...25 pts} \right) \text{ Let X, Y, and Z be three jointly distributed random variables with joint pdf} f_{XYZ}\left ( x,y,z \right )= \frac{3z^{2}}{7\sqrt[]{2\pi}
    5 KB (711 words) - 09:05, 27 July 2012
  • ECE-QE CS1-2011 solusion-1
    [[Category:random variables]] Question 1: Probability and Random Processes
    8 KB (1,247 words) - 10:29, 13 September 2013
  • ECE-QE CS1-2011 solusion-2
    [[Category:random variables]] Question 1: Probability and Random Processes
    6 KB (932 words) - 10:30, 13 September 2013
  • 2013 Spring ECE 302 Boutin
    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
  • LectureScheduleECE302Spring13 Boutin
    ==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
  • Lab 2 Additional Information ECE637 Spring2013 Bouman
    ...e having trouble using the image() command in Matlab for displaying their 'random image' in part 2? Mine comes up as just a black square every time. I set th ...ifferent numbers of arguments. I'd like to not have to create 25 different variables for each window. Can anyone help me with this?
    5 KB (957 words) - 08:11, 9 April 2013
  • Lecture11 blog ECE302S13 Boutin
    ...The formula for obtaining the probability mass function of a function of a random variable was given, and we illustrated it with two simple examples. We fini
    2 KB (307 words) - 10:26, 4 February 2013
  • Lecture10 blog ECE302S13 Boutin
    In Lecture 10, defined the concept of a discrete random variables and gave several examples. The concept that caused the most confusion seems
    2 KB (289 words) - 11:08, 30 January 2013
  • Lecture16 blog ECE302S13 Boutin
    ...the random variable does not change the variance, and that multiplying the random variable by a constant "a" has the effect of multiplying the variance by <m
    2 KB (336 words) - 12:59, 18 February 2013
  • Lecture17 blog ECE302S13 Boutin
    ...n Part 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
  • Markov chain green26
    ...tative robot spiraling 'inward' or 'outward'. Normally distributed random variables are used to modify the magnitude (M) of the complex vector and rotate the v % generate random initial state with complex magnitude 1
    2 KB (289 words) - 15:14, 1 May 2016
  • Bayesian Decision Theory
    ...t it is spades, and both probabilities sum up to 1 (since we only have two variables). We can therefore use the following decision rule; that if ''P(x<sub>1</su ...r), we can describe this as a variable ''y'' and we consider ''y'' to be a random variable whose distribution depends on the state of the card and is express
    5 KB (844 words) - 23:32, 28 February 2013
  • Lecture18 blog ECE302S13 Boutin
    ...looked at an example of continuous random variable, namely the exponential random variable.
    2 KB (329 words) - 08:16, 20 February 2013
  • Lecture19 blog ECE302S13 Boutin
    In Lecture 19, we continued our discussion of continuous random variables. ...outin|Invent a problem about the expectation and/or variable of a discrete random variable]]
    2 KB (252 words) - 08:20, 20 February 2013
  • Lecture20 blog ECE302S13 Boutin
    ...discrete) and we began discussing normally distributed (continuous) random variables. ...on_ECE302S13Boutin|Normalizing the probability mass function of a Gaussian random variable]]
    2 KB (304 words) - 07:43, 23 February 2013
  • Lecture21 blog ECE302S13 Boutin
    ...a normally distributed random variable: it was observed that the resulting random variable Y=aX+b is also normally distributed. The relation between the mean
    3 KB (393 words) - 08:21, 27 February 2013
  • Lecture22 blog ECE302S13 Boutin
    ...lso had a little bit of time to start talking about two dimensional random variables.
    3 KB (387 words) - 07:09, 28 February 2013
  • Practice Question joint from marginals independent variables ECE302S13Boutin
    [[Category:independent random variables]] ...e Problem]]: obtaining the joint pdf from the marginals of two independent variables =
    2 KB (394 words) - 12:03, 26 March 2013
  • HW5 ECE302 S13 Boutin
    ...on_ECE302S13Boutin|Normalizing the probability mass function of a Gaussian random variable]] ...13Boutin|Obtaining the joint pdf from the marginal pdfs of two independent variables]]
    2 KB (337 words) - 06:28, 1 March 2013
  • Lecture24 blog ECE302S13 Boutin
    ...of a 2D random variable. In particular, we looked at the covariance of two variables. We finished the lecture by giving the definition of conditional probabili
    2 KB (324 words) - 13:11, 5 March 2013
  • Lecture26 blog ECE302S13 Boutin
    ...ind the 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
  • Lecture32 blog ECE302S13 Boutin
    ...particular, we obtain a formula for the pdf of a sum of independent random variables (namely, the convolution of their respective pdf's).
    2 KB (286 words) - 09:11, 29 March 2013
  • Practice Question independence ECE302S13Boutin
    [[Category:independent random variables]] Two continuous random variables X and Y have the following joint probability density function:
    2 KB (290 words) - 10:17, 27 March 2013
  • Practice Question characteristic function ECE302S13Boutin
    A discrete random variables X has a moment generating (characteristic) function <math>M_X(s)</math> suc
    1 KB (211 words) - 03:47, 27 March 2013
  • Bayes theorem S13
    ...vation of the conditional distributions for continuous and discrete random variables, you may wish to go over Professor Mary Comer's [[ECE600_F13_rv_conditional * Alberto Leon-Garcia, ''Probability, Statistics, and Random Processes for Electrical Engineering,'' Third Edition
    4 KB (649 words) - 13:08, 25 November 2013
  • Practice Question 2D Gaussian ECE302S13Boutin
    [[Category:normal random variable]] be a two-dimensional Gaussian random variable with mean <math>\mu</math> and standard deviation matrix <math>\Si
    2 KB (273 words) - 03:22, 26 March 2013
  • Temporary file ECE302S12Boutin
    ...tudent, 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
  • Lecture35 blog ECE302S13 Boutin
    ...also a quiz where we re-emphasized how easy it is to compute the mean of a random variable with a symmetric pmf/pdf. (The trick is to guess the answer m, and *Read Sections 2.1.1-2.1.6 of Prof. Pollak's notes on random variables [https://engineering.purdue.edu/~ipollak/ee438/FALL04/notes/Section2.1.pdf
    2 KB (330 words) - 06:16, 9 April 2013
  • Practice Question what is stationary increment property sum process ECE302S13
    [[Category:random process]] ...ariable with the same distribution as the random variable contained in the random process at the time found by differencing the two distinct times mentioned
    9 KB (1,507 words) - 16:23, 23 April 2013
  • Lecture40 blog ECE302S13 Boutin
    ...short introduction to the topic, we covered the definition of a stationary random process.
    3 KB (376 words) - 10:23, 17 April 2013

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