Search results for "Continuous random variable" - Rhea

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• Reading guide (ECE438BoutinSpring2009)
  .../vise.www.ecn.purdue.edu/VISE/ee438L/lab1/pdf/lab1.pdf Lab on discrete and continuous signals] ==Random sequences ==
  8 KB (1,226 words) - 11:40, 1 May 2009
• Cheat2 ECE302Fall2008sanghavi
  * For Continuous Random Variable: ==Theorem of Total Probability for Continuous Random Variables==
  4 KB (722 words) - 13:05, 22 November 2011
• Divyanshu Kamboj 7.3 ECE302Fall2008sanghavi
  ...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:
  2 KB (332 words) - 16:52, 20 October 2008
• Map Estimation ECE302Fall2008sanghavi
  <math>f_x(X; \theta)</math> (continuous) <math>P_\theta(\theta)</math> (continuous)
  4 KB (671 words) - 09:23, 10 May 2013
• 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
  ...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
• My use for the DFT!
  ...-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
• Decision Theory Glossary ECE662Spring2008
  ...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
• ECE 600 Prerequisites Continuous Random Variables
  ='''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
  5 KB (843 words) - 11:27, 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">\ma
  10 KB (1,667 words) - 11:37, 30 November 2010
• ECE 600 General Concepts of Stochastic Processes Definitions
  ...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
• ECE 600 Finals MRB 1994 Final
  ...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
• ECE 600 Finals MRB 2004 Final
  ...<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
• ECE600 QE 2002 January
  ...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
• ECE600 QE 2004 August
  ...>\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
• ECE600 QE 2006 January
  ...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
• ECE 600 Exams Secquence of binomially distributed random variables
  =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
  3 KB (470 words) - 13:02, 23 November 2010
• ECE 600 Exams Sequence of binomially distributed random variables
  =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
• ECE301inBME
  ...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>
  17 KB (2,368 words) - 10:53, 6 May 2012
• Probability practice problems list
  *Discrete Random Variables ...02S13Boutin|Normalizing the probability mass function of a discrete random variable]]
  7 KB (960 words) - 18:17, 23 February 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 2006 Problem1
  [[Category:random variables]] Question 1: Probability and Random Processes
  4 KB (572 words) - 10:24, 10 March 2015
• ECE-QE CS1-2011
  [[Category:random variables]] Question 1: Probability and Random Processes
  4 KB (547 words) - 16:40, 30 March 2015
• 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
• Lecture17 blog ECE302S13 Boutin
  ...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
• Practice Question probability normalization ECE302S13Boutin
  [[Category:discrete random variable]] ...Problem]]: normalizing the probability mass function of a discrete random variable=
  2 KB (355 words) - 13:50, 13 February 2013
• Lecture18 blog ECE302S13 Boutin
  ...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. ...nvent 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
  ...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
• Practice Question gaussian normalization ECE302S13Boutin
  [[Category:continuous random variable]] ...roblem]]: normalizing the probability mass function of a continuous random variable=
  3 KB (519 words) - 08:11, 25 February 2013
• Practice Question uniform random variable mean ECE302S13Boutin
  [[Category:continuous random variable]] ...roblem]]: normalizing the probability mass function of a continuous random variable=
  2 KB (288 words) - 15:49, 22 March 2013
• Practice Question uniform random variable CDF ECE302S13Boutin
  [[Category:continuous random variable]] ...roblem]]: normalizing the probability mass function of a continuous random variable=
  2 KB (401 words) - 04:52, 4 March 2013
• Practice Question linear random variable probability ECE302S13Boutin
  [[Category:continuous random variable]] ...roblem]]: normalizing the probability mass function of a continuous random variable=
  2 KB (269 words) - 04:58, 4 March 2013
• Practice Question moment order zero Gaussian ECE302S13Boutin
  [[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
• Practice Question moment order two Gaussian ECE302S13Boutin
  [[Category:continuous random variable]] ...|Practice Problem]]: compute the zero-th order moment of a Gaussian random variable=
  902 B (129 words) - 08:14, 27 February 2013
• Practice Question comparing probabilities Gaussians ECE302S13Boutin
  [[Category:continuous random variable]] [[Category:gaussian random variable
  2 KB (216 words) - 05:48, 24 March 2013
• Practice Question probability meeting occurs ECE302S13Boutin
  [[Category:continuous random variable]] [[Category:uniform random variable]]
  2 KB (284 words) - 11:49, 26 March 2013
• Practice Question find conditional pdf ECE302S13Boutin
  [[Category:continuous random variable]] [[Category:uniform random variable]]
  1 KB (157 words) - 11:59, 26 March 2013
• Practice Question find conditional ellipse ECE302S13Boutin
  [[Category:continuous random variable]] [[Category:uniform random variable]]
  1,022 B (148 words) - 12:00, 26 March 2013
• Practice Question find conditional event ECE302S13Boutin
  [[Category:continuous random variable]] Let X be a continuous random variable with probability density function
  2 KB (299 words) - 09:17, 27 March 2013
• Lecture26 blog ECE302S13 Boutin
  ...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
• Lecture27 blog ECE302S13 Boutin
  ...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
• Practice Question characteristic linear function random variable ECE302S13Boutin
  ...: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
• Temporary file ECE302S12Boutin
  ...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
• Lecture38 blog ECE302S13 Boutin
  ...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
• Quiz7 expectation continuousRV ECE302 S13 Boutin
  Topic: Expectation of continuous RV *A random variable X has the following probability density function:
  784 B (104 words) - 14:41, 25 April 2013
• ECE600 F13 rv definition mhossain
  [[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 distribution mhossain
  [[ECE600_F13_rv_definition_mhossain|Previous Topic: Random Variables: Definitions]]<br/> [[ECE600_F13_notes_mhossain|'''The Comer Lectures on Random Variables and Signals''']]
  15 KB (2,637 words) - 12:11, 21 May 2014
• ECE600 F13 rv conditional distribution mhossain
  [[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
• ECE600 F13 rv Functions of random variable mhossain
  [[ECE600_F13_notes_mhossain|'''The Comer Lectures on Random Variables and Signals''']] <font size= 3> Topic 8: Functions of Random Variables</font size>
  9 KB (1,723 words) - 12:11, 21 May 2014
• ECE600 F13 Expectation mhossain
  ...unctions_of_random_variable_mhossain|Previous Topic: Functions of a Random Variable]]<br/> [[ECE600_F13_notes_mhossain|'''The Comer Lectures on Random Variables and Signals''']]
  8 KB (1,474 words) - 12:12, 21 May 2014
• ECE600 F13 Characteristic Functions mhossain
  [[ECE600_F13_notes_mhossain|'''The Comer Lectures on Random Variables and Signals''']] ...pdf f<math>_X</math> of a random variable X is a function of a real valued variable x. It is sometimes useful to work with a "frequency domain" representation
  5 KB (804 words) - 12:12, 21 May 2014
• ECE600 F13 Joint Distributions mhossain
  [[ECE600_F13_notes_mhossain|'''The Comer Lectures on Random Variables and Signals''']] <font size= 3> Topic 11: Two Random Variables: Joint Distribution</font size>
  8 KB (1,524 words) - 12:12, 21 May 2014
• ECE600 F13 Joint Expectation mhossain
  [[ECE600_F13_notes_mhossain|'''The Comer Lectures on Random Variables and Signals''']] Given random variables X and Y, let Z = g(X,Y) for some g:'''R'''<math>_2</math>→R. Th
  7 KB (1,307 words) - 12:12, 21 May 2014
• ECE600 F13 Conditional Distributions for Two Random Variables mhossain
  [[ECE600_F13_notes_mhossain|'''The Comer Lectures on Random Variables and Signals''']] <font size= 3> Topic 15: Conditional Distributions for Two Random Variables</font size>
  6 KB (1,139 words) - 12:12, 21 May 2014
• ECE600 F13 Stochastic Processes mhossain
  [[ECE600_F13_notes_mhossain|'''The Comer Lectures on Random Variables and Signals''']] ...formalize the concept of random process, including both discrete-time and continuous time.
  10 KB (1,690 words) - 12:13, 21 May 2014
• ECE-QE CS1-2012
  [[Category:random variables]] Question 1: Probability and Random Processes
  3 KB (449 words) - 21:36, 5 August 2018
• ECE-QE CS1-2012 solusion-1
  <font size="4">Question 1: Probability and Random Processes </font> ...our proof, keep in mind that may be either a discrete or continuous random variable.
  6 KB (995 words) - 09:21, 15 August 2014
• Walther375Spring2014 Markov chains: what and how?
  ...cess. 
 A stochastic process { X(t), t∈T } is an ordered collection of random variables, T where T is the index set and if t is a time in the set, X(t) i ...h models that use X1,…,Xn as independently identically distributed (iid) random variables. However, note that states do not necessarily have to be independ
  19 KB (3,004 words) - 09:39, 23 April 2014
• Bayes Parameter Estimation
  ...ven value of θ is denoted by p(x|θ ). It should be noted that the random variable X and the parameter θ can be vector-valued. Now we obtain a set of indepen ...s parameter estimation, the parameter θ is viewed as a random variable or random vector following the distribution p(θ ). Then the probability density func
  15 KB (2,273 words) - 10:51, 22 January 2015
• Bayersian Parameter Estimation
  ...e in Bayersian estimation <math>\theta</math> is considered to be a random variable. ...ew <math>\theta</math> as a random variable. Consider more specifically in continuous case:
  8 KB (1,268 words) - 08:31, 29 April 2014
• Maximum Likelihood Estimation Analysis for various Probability Distributions
  ...tinuous random variables and probability mass function in case of discrete random variables and 'θ' is the parameter being estimated. ...variables) or the probability of the probability mass (in case of discrete random variables)'''
  12 KB (1,986 words) - 10:49, 22 January 2015
• Comments of slecture: Bayes Parameter Estimation (BPE) tutorial
  *The author gives an example of continuous case using Gaussian random variable.
  2 KB (291 words) - 06:39, 5 May 2014
• The principles for how to generate random samples from a Gaussian distribution
  The principle of how to generate a Gaussian random variable ...od for pseudo random number sampling first. Then, we will explain Gaussian random sample generation method based on Box Muller transform. Finally, we will in
  8 KB (1,189 words) - 10:39, 22 January 2015
• ECE PhD QE CNSIP Jan 2002 Problem1.4
  [[Category:random variables]] Question 1: Probability and Random Processes
  2 KB (302 words) - 17:38, 13 March 2015
• ECE PhD QE CNSIP Jan 2002 Problem1.5
  [[Category:random variables]] Question 1: Probability and Random Processes
  2 KB (284 words) - 17:39, 13 March 2015
• ECE PhD QE CNSIP 2004 Problem1.2
  [[Category:random variables]] Question 1: Probability and Random Processes
  2 KB (366 words) - 01:36, 10 March 2015
• ECE PhD QE CNSIP Jan 2006 Problem1.2
  [[Category:random variables]] Question 1: Probability and Random Processes
  3 KB (454 words) - 10:25, 10 March 2015
• QE2007 CS-4 ECE580
  ...rate <math>\lambda</math>. Assume that <math>K</math> is a Poisson random variable independent of <math>N_{i}(t)</math> (for all i) and has mean a. Let <math> ...ent: For a given t and T, <math> (N(t+T)-N(t)) </math> is a Poisson random variable with mean <math> a \lambda T </math>. <br/>
  5 KB (910 words) - 03:02, 24 February 2019
• Definitions and statistical formulas
  ...ertain value, usually denoted as <math>P(X=A)</math> where X is the random variable and A is the outcome we are looking for. The integrals of all probability d ...Is used in statistics to represent any unknown parameter of interest. In a continuous probability function, it can be used as the likelihood that event X occurs.
  2 KB (358 words) - 22:58, 6 December 2020