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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. 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
- == 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
- =='''1.4 Discrete Random Variables'''== ...}_{2},\cdots</math> are i.i.d. Bernoulli random variables, then Binomial random variable is defined as <math class="inline">\mathbf{X}=\mathbf{Y}_{1}+\math5 KB (921 words) - 11:25, 30 November 2010
- ='''1.6 Continuous Random Variables'''= ...tribution, then <math class="inline">\mathbf{Y}=\ln\mathbf{X}</math> is a random variable with Gaussian distribution. This distribution is characterized wit5 KB (843 words) - 11:27, 30 November 2010
- ='''1.8 Some Measures on Random Variable'''=2 KB (305 words) - 11:15, 17 November 2010
- ='''1.10 Two Random Variables'''= ...bf{Y}</math> be two jointly-distributed, statistically independent random variables, having pdfs <math class="inline">f_{\mathbf{X}}\left(x\right)</math> and6 KB (952 words) - 11:31, 30 November 2010
- [[Category:random variables]] =Sequences of Random Variables=1 KB (194 words) - 11:35, 30 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. Addition of two jointly distributed Gaussian random variables= ...inline">\mathbf{Y}</math> is <math class="inline">r</math> . Define a new random variable <math class="inline">\mathbf{Z}=\mathbf{X}+\mathbf{Y}</math> .3 KB (504 words) - 12:00, 30 November 2010
- =Example. Two jointly distributed random variables= Two joinly distributed random variables <math class="inline">\mathbf{X}</math> and <math class="inline">\mathbf{Y}2 KB (416 words) - 11:47, 3 December 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
- =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) - 12:10, 30 November 2010
- =Example. Sequence of binomially distributed random variables= ...of binomially distributed random variables, with the <math>n_{th}</math> random variable <math>\mathbf{X}_{n}</math> having pmf3 KB (470 words) - 13:02, 23 November 2010
- =Example. Sequence of binomially distributed random variables= ...distributed random variables, with the <math class="inline">n_{th}</math> random variable <math class="inline">\mathbf{X}_{n}</math> having pmf3 KB (539 words) - 12:14, 30 November 2010
- =Example. Sequence of exponentially distributed random variables= ...X}_{n}</math> be a collection of i.i.d. exponentially distributed random variables, each having mean <math class="inline">\mu</math> . Define3 KB (486 words) - 07:13, 1 December 2010
- =Example. Sequence of uniformly distributed random variables= ...erval <math class="inline">\left[0,1\right]</math> . Define the new random variables <math class="inline">\mathbf{W}=\max\left\{ \mathbf{X}_{1},\mathbf{X}_{2},\3 KB (456 words) - 07:14, 1 December 2010
- =Example. Mean of i.i.d. random variables= ...ath> be <math class="inline">M</math> jointly distributed i.i.d. random variables with mean <math class="inline">\mu</math> and variance <math class="inline2 KB (420 words) - 11:25, 16 July 2012
Page text matches
- If the brightness values in the x and y directions are thought of as random variables then C is a scaled version of their covariance matrix.14 KB (2,253 words) - 12:21, 9 January 2009
- ...le="padding-right: 1em;" | Friday || 02/27/09 || Circular convolution, one random variable || 1.6.5., 3.1.1 ...ign="right" style="padding-right: 1em;" | Monday || 03/02/09 || two random variables || 3.1.26 KB (689 words) - 07:59, 2 August 2010
- ...an/ece438/lecture/module_1/1.1_signals/1.1.5_complex_variables.pdf complex variables] ==Random sequences ==8 KB (1,226 words) - 11:40, 1 May 2009
- *[[ECE600|ECE 600]]: "Random Variables and Stochastic Processes"4 KB (474 words) - 07:08, 4 November 2013
- Let <math>X</math> denote a binomial random variable with parameters <math>(N, p)</math>. *(a) Show that <math>Y = N - X</math> is a binomial random variable with parameters <math>(N,1-p)</math>6 KB (883 words) - 12:55, 22 November 2011
- '''Definition and basic concepts of random variables, PMFs''' Random Variable: a map/function from outcomes to real values3 KB (525 words) - 13:04, 22 November 2011
- This part deals with Binomial Random Variables.401 B (68 words) - 15:04, 23 September 2008
- ...figure out what the point of this question, Is W one of the common random variables we have seen in class?, is. Is any way that I can prove that W is one of the common random variables?532 B (101 words) - 05:43, 24 September 2008
- ...e coupons in it, with all being equally likely. Let <math>X</math> be the (random) number of candy bars you eat before you have all coupons. What are the mea ...t is the PDF of <math>Y</math>? Is <math>Y</math> one of the common random variables?4 KB (656 words) - 12:56, 22 November 2011
- <math>X</math> is an exponential random variable with paramter <math>\lambda</math>. <math>Y = \mathrm{ceil}(X)</ma What is the PMF of <math>Y</math>? Is it one of the common random variables? (Hint: for all <math>k</math>, find the quantity <math>P(Y > k)</math>. T3 KB (449 words) - 12:57, 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
- ...comes (1/2)*e^(-d/2) which is the pdf. And, D is one of the common random variables because our pdf's are exponential with parameter lambda = 1/2.297 B (54 words) - 12:54, 16 October 2008
- * For Continuous Random Variable: ==Theorem of Total Probability for Continuous Random Variables==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
- ...ding P[H2|H1], and H2 and H1 are both events rather than continuous random variables, we can do this. We don't have to worry about finding the conditional PDF333 B (64 words) - 10:26, 20 October 2008
- ...e former 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. Tha ...s said to be the conditional probability of the event A given the discrete random variable X:2 KB (332 words) - 16:52, 20 October 2008
- We create variables : Therefore, in c to produce a random variable with a gaussian distribution you simply do the following560 B (112 words) - 18:03, 20 October 2008
- OK, so what we have initially is a uniform random variable on the interval [0,1]. ...exponential random variable with λ=0.5 is made out of two gaussian random variables with the relationship '''<math>D=X^2+Y^2</math>'''1 KB (186 words) - 11:47, 21 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
