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- =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
- == 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
- == 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>\mat2 KB (268 words) - 04:18, 15 November 2010
- =Example. Addition of two independent Poisson random variables= ...and <math class="inline">\mathbf{Y}</math> are independent Poisson random variables with means <math class="inline">\lambda</math> and <math class="inline">\m3 KB (532 words) - 11:58, 30 November 2010
- =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?7 KB (1,015 words) - 11:59, 30 November 2010
- =Example. Two jointly distributed independent random variables= ..."inline">\mathbf{Y}</math> be two jointly distributed, independent random variables. The pdf of <math class="inline">\mathbf{X}</math> is5 KB (803 words) - 12:08, 30 November 2010
- [[ECE600_F13_notes_mhossain|'''The Comer Lectures on Random Variables and Signals''']] <font size= 3> Topic 12: Independent Random Variables</font size>2 KB (449 words) - 12:12, 21 May 2014
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- A & B are independent if <math>P(A\cap B)=P(A)P(B)</math> side note: if A&B are independent then P(A|B)=P(A)3 KB (525 words) - 13:04, 22 November 2011
- == 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
- *The sum of many, small independent things For 2 independent Gaussians:4 KB (722 words) - 13:05, 22 November 2011
- The PDF of the sum of two independent random variables is the convolution of the two PDFs. The lecture notes from 10/10 are helpf133 B (23 words) - 19:13, 19 October 2008
- == 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
- ...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 of31 KB (4,832 words) - 18:13, 22 October 2010
- ...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 h10 KB (1,594 words) - 11:41, 24 March 2008
- which datasets with tens or hundreds of thousands of variables are available. These areas include ...tion of the nearest of a set of previously classified points. This rule is independent of the underlying joint distribution on the sample points and their classif39 KB (5,715 words) - 10:52, 25 April 2008
- ...ion case, there will be very large set of feature vectors and classes, and independent of the probability distributions of features, the sum of the distributions The following histograms of N uniformly distributed random variables for different values of N can be given to visualize the [http://en.wikipedi2 KB (247 words) - 08:32, 10 April 2008
- ...iable" being observed should be the sum or mean of many independent random variables.213 B (35 words) - 10:01, 31 March 2008
- | align="right" style="padding-right: 1em;" | The intersection of two independent events A and B ...e-policy: -moz-initial;" colspan="2" | Expectation and Variance of Random Variables3 KB (491 words) - 12:54, 3 March 2015
- 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}}</ma5 KB (806 words) - 09:08, 11 May 2010
- ...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. Y4 KB (756 words) - 04:25, 8 September 2010
- ...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 of31 KB (4,787 words) - 18:21, 22 October 2010
- = [[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
- [[Category:random variables]] Question 1: Probability and Random Processes1 KB (191 words) - 17:42, 13 March 2015
- [[Category:random variables]] Question 1: Probability and Random Processes5 KB (928 words) - 17:46, 13 March 2015
- =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
- == Example. Two jointly distributed random variables == Two joinly distributed random variables <math>\mathbf{X}</math> and <math>\mathbf{Y}</math> have joint pdf7 KB (1,103 words) - 05:27, 15 November 2010
- == 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
