Search results for "Continuous random variable" - Rhea

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• 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