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- ...hs L (which is clearly just [0,2r], that the PDF of it should be a uniform distribution, as each distance has an infinite amount of chords tangent to a circle of r ...e less than or equal to the length of r. I am thinking the distribution is exponential...2 KB (315 words) - 11:57, 6 October 2008
- From the memoryless property of''' Exponential Distribution''' function:571 B (71 words) - 18:50, 6 October 2008
- ...the probability of error (misclassification). This method assumes a known distribution for the feature vectors given each class. The CLT explains why many distributions tend to be close to the normal distribution. The important point is that the "random variable" being observed should be31 KB (4,832 words) - 18:13, 22 October 2010
- ...xamples: Exponential and Geometric Distributions_Old Kiwi|Examples of MLE: Exponential and Geometric Distributions ]] ...xamples: Exponential and Geometric Distributions_Old Kiwi|Examples of MLE: Exponential and Geometric Distributions ]]10 KB (1,488 words) - 10:16, 20 May 2013
- ...iously classified points. This rule is independent of the underlying joint distribution on the sample points and their classifications, and hence the probability o *'''N.Johnson, and D. Hogg, "Learning the Distribution of object Trajectories for Event Recognition", Journal of Image and Vision39 KB (5,715 words) - 10:52, 25 April 2008
- [[Category:exponential distribution]] [[Category:geometric distribution]]3 KB (498 words) - 10:13, 20 May 2013
- [[Category:binomial distribution]] [[Category:poisson distribution]]2 KB (366 words) - 10:14, 20 May 2013
- ...d <math> \rho \big(x \mid \omega _{2}\big) </math> are Gaussians with the distribution Since Gaussian distribution is one of the exponential families, eq.(3.1) can be expressed as a following form [1] [3].17 KB (2,590 words) - 10:45, 22 January 2015
- ...he distribution of the sample means is normal regardless of the population distribution. ...0) i.i.d random values which can be sampled from one of the following five distribution:7 KB (1,104 words) - 07:44, 23 February 2010
- ...ensity functions, moments and random variables. Applications of normal and exponential distributions. Estimation of means, variances. Correlation and spectral den <br/><br/>3. Independence, Cumulative Distribution Function (used in ECE 438), Probability Density Function (used in ECE 438),2 KB (231 words) - 07:20, 4 May 2010
- ...the probability of error (misclassification). This method assumes a known distribution for the feature vectors given each class. The CLT explains why many distributions tend to be close to the normal distribution. The important point is that the "random variable" being observed should be31 KB (4,787 words) - 18:21, 22 October 2010
- == Example. Addition of multiple independent Exponential random variables == ...math> is Geometric random variable with parameter <math>p</math>. Find the distribution of <math>\mathbf{S}_{\mathbf{N}}=\sum_{i=1}^{\mathbf{N}}\mathbf{X}_{i}</mat2 KB (268 words) - 04:18, 15 November 2010
- '''1.6.1 Gaussian distribution (normal distribution)''' <math class="inline">\mathcal{N}\left(\mu,\sigma^{2}\right)</math> '''1.6.2 Log-normal distribution <math class="inline">\ln\mathcal{N}\left(\mu,\sigma^{2}\right)</math>'''5 KB (843 words) - 11:27, 30 November 2010
- then <math class="inline">\mathbf{Z}_{n}</math> converges in distribution to a random variable <math class="inline">\mathbf{Z}</math> that is Gaussia We can expand the exponential as a power series (in <span class="texhtml">ω</span> about <span class="te4 KB (657 words) - 11:42, 30 November 2010
- Example. Addition of multiple independent Exponential random variables ...ic random variable with parameter <math class="inline">p</math> . Find the distribution of <math class="inline">\mathbf{S}_{\mathbf{N}}=\sum_{i=1}^{\mathbf{N}}\mat2 KB (310 words) - 11:44, 30 November 2010
- ...applied to <math class="inline">\mathbf{Y}</math> will yield the desired distribution for <math class="inline">\mathbf{X}</math> ? Prove your answer. Given that a node is in the circle C , determine the density or distribution function of its distance <math class="inline">\mathbf{D}</math> from the o10 KB (1,608 words) - 08:31, 27 June 2012
- ...ass="inline">\mathbf{Y}</math> be two independent identically distributed exponential random variables having mean <math class="inline">\mu</math> . Let <math cl ...dependent Poisson random variables|example]] except that it deals with the exponential random variable rather than the Poisson random variable.14 KB (2,358 words) - 08:31, 27 June 2012
- ...bf{X}_{1},\mathbf{X}_{2},\cdots,\mathbf{X}_{n},\cdots</math> converges in distribution to a Poisson random variable having mean <math class="inline">\lambda</math ...ow\infty</math> , <math class="inline">\mathbf{X}_{n}</math> converges in distribution to a Poisson random variable with mean <math class="inline">\lambda</math>10 KB (1,754 words) - 08:30, 27 June 2012
- ...ht\} \right)</math><math class="inline">=1-e^{-\lambda\pi r}\text{: CDF of exponential random variable}.</math> ref. You can see the expressions about exponentail distribution [CS1ExponentialDistribution].9 KB (1,560 words) - 08:30, 27 June 2012
- Does the sequence <math class="inline">\mathbf{X}_{n}</math> converge in distribution? A simple yes or no answer is not sufficient. You must justify your answer. ...ath> The squance <math class="inline">\mathbf{X}_{n}</math> converges in distribution.14 KB (2,439 words) - 08:29, 27 June 2012
