• ...F and from there you can determine the type of distribution (eg. Binomial, Geometric, etc.)
    440 B (83 words) - 09:04, 23 September 2008
  • ...tial and Geometric Distributions_Old Kiwi|Examples of MLE: Exponential and Geometric Distributions ]] ...tial and Geometric Distributions_Old Kiwi|Examples of MLE: Exponential and Geometric Distributions ]]
    10 KB (1,488 words) - 10:16, 20 May 2013
  • [[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
  • Hint: it's a geometric distribution. See pages 433-434 - specifically, Theorem 4 on page 434.
    5 KB (966 words) - 23:24, 3 March 2010
  • ...e frequency, you see the amplitude of the signal at that frequency (such a distribution is called a frequency spectrum).<br>It is thus a technique that can be used ...ot. <br> For <math>r\neq 1</math>, the sum of the first ''n''+1 terms of a geometric series is:
    13 KB (2,348 words) - 13:25, 2 December 2011
  • ...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}</mat The probability generating function of Geometric random variable is
    2 KB (268 words) - 04:18, 15 November 2010
  • ='''1.4.1 Bernoulli distribution'''= ='''1.4.2 Binomial distribution'''=
    5 KB (921 words) - 11:25, 30 November 2010
  • ...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}}\mat The probability generating function of Geometric random variable is
    2 KB (310 words) - 11:44, 30 November 2010
  • =Example. Geometric random variable= This is a geometric random variable with success probability <math class="inline">\alpha</math>
    5 KB (793 words) - 12:10, 30 November 2010
  • | <math> F- </math> distribution | Geometric
    6 KB (851 words) - 15:34, 23 April 2013
  • ...tial and Geometric Distributions_Old Kiwi|Examples of MLE: Exponential and Geometric Distributions ]] ...onential and Geometric Distributions_OldKiwi|MLE Examples: Exponential and Geometric Distributions]]
    10 KB (1,472 words) - 11:16, 10 June 2013
  • ...we talked about Maximum Likelihood Estimation (MLE) of the parameters of a distribution. ...ponential_and_Geometric_Distributions_OldKiwi|MLE example: exponential and geometric distributions]]
    2 KB (196 words) - 09:54, 23 April 2012
  • =Maximum Likelihood Estimation (MLE) example: Bernouilli Distribution= ..._Examples:_Exponential_and_Geometric_Distributions_OldKiwi|Exponential and geometric distributions]]
    2 KB (310 words) - 09:58, 23 April 2012
  • =Maximum Likelihood Estimation (MLE) example: Exponential and Geometric Distributions= '''Exponential Distribution'''
    3 KB (446 words) - 10:00, 23 April 2012
  • In 1837, the Poisson Distribution was introduced by Siméon Denis Poisson[http://www.aabri.com/SA12Manuscript This distribution models the probablility that a number of events, x, will occur within a giv
    5 KB (708 words) - 07:22, 22 April 2013
  • # the cumulative distribution function (cdf) '''Definition''' <math>\quad</math> The '''cumulative distribution function (cdf)''' of X is defined as <br/>
    15 KB (2,637 words) - 12:11, 21 May 2014
  • In this slecture, the author details the method of MLE on different specific distribution and conclude the final expression on how to estimate each of them. ...sented which helps student to understand how to apply general MLE on a new distribution. This slecture also summerizes the final useful expression of estimation fo
    2 KB (235 words) - 10:25, 5 May 2014
  • ...c Distribution, Binomial Distribution, Poisson Distribution, and Uniform Distribution ** Exponential Distribution
    12 KB (1,986 words) - 10:49, 22 January 2015
  • ...(i.e., from 10 to 2), so it is much easier to visualize the shape of data distribution. PCA is also useful in the modeling of robust classifier where considerably ...X\in\mathbb{R}^{2\times100}</math> on 1-D space. Figure 1 shows elliptical distribution of X with principal component directions <math>\vec{u}_{1}</math> and <math
    13 KB (1,990 words) - 11:42, 21 May 2014

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Basic linear algebra uncovers and clarifies very important geometry and algebra.

Dr. Paul Garrett