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  • 5.3 - AJ Hartnett ECE302Fall2008sanghavi
    ...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
  • 5.2b Divyanshu Kamboj ECE302Fall2008sanghavi
    From the memoryless property of''' Exponential Distribution''' function:
    571 B (71 words) - 18:50, 6 October 2008
  • ECE662:Glossary Old Kiwi
    ...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 be
    31 KB (4,832 words) - 18:13, 22 October 2010
  • Lecture 7 - MLE and BPE Old Kiwi
    ...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
  • Publications Old Kiwi
    ...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 Vision
    39 KB (5,715 words) - 10:52, 25 April 2008
  • MLE Examples: Exponential and Geometric Distributions Old Kiwi
    [[Category:exponential distribution]] [[Category:geometric distribution]]
    3 KB (498 words) - 10:13, 20 May 2013
  • MLE Examples: Binomial and Poisson Distributions Old Kiwi
    [[Category:binomial distribution]] [[Category:poisson distribution]]
    2 KB (366 words) - 10:14, 20 May 2013
  • Test
    ...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
  • ECE662 topic3 discussions
    ...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
  • Snikitha ListECE302
    ...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
  • Decision Theory Glossary ECE662Spring2008
    ...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 be
    31 KB (4,787 words) - 18:21, 22 October 2010
  • ECE600 Example Addition of multiple independent Exponential random variables
    == 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}</mat
    2 KB (268 words) - 04:18, 15 November 2010
  • ECE 600 Prerequisites Continuous Random Variables
    '''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
  • ECE 600 Central Limit Theorem
    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="te
    4 KB (657 words) - 11:42, 30 November 2010
  • ECE 600 Random Sum
    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}}\mat
    2 KB (310 words) - 11:44, 30 November 2010
  • ECE600 QE 2003 August
    ...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 o
    10 KB (1,608 words) - 08:31, 27 June 2012
  • ECE600 QE 2004 August
    ...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
  • ECE600 QE 2005 August
    ...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
  • ECE600 QE 2006 January
    ...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
  • ECE600 QE 2006 August
    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
  • Probability Distribution
    | Exponential <math> E(\lambda) </math> | <math> F- </math> distribution
    6 KB (851 words) - 15:34, 23 April 2013
  • Lecture 7 - MLE and BPE OldKiwi
    ...xamples: Exponential and Geometric Distributions_Old Kiwi|Examples of MLE: Exponential and Geometric Distributions ]] [[MLE Examples: Exponential and Geometric Distributions_OldKiwi|MLE Examples: Exponential and Geometric Distributions]]
    10 KB (1,472 words) - 11:16, 10 June 2013
  • Lecture13ECE662S12
    ...we talked about Maximum Likelihood Estimation (MLE) of the parameters of a distribution. *[[MLE_Examples:_Exponential_and_Geometric_Distributions_OldKiwi|MLE example: exponential and geometric distributions]]
    2 KB (196 words) - 09:54, 23 April 2012
  • MLE Examples: Binomial and Poisson Distributions OldKiwi
    =Maximum Likelihood Estimation (MLE) example: Bernouilli Distribution= ...examples: [[MLE_Examples:_Exponential_and_Geometric_Distributions_OldKiwi|Exponential and geometric distributions]]
    2 KB (310 words) - 09:58, 23 April 2012
  • MLE Examples: Exponential and Geometric Distributions OldKiwi
    =Maximum Likelihood Estimation (MLE) example: Exponential and Geometric Distributions= '''Exponential Distribution'''
    3 KB (446 words) - 10:00, 23 April 2012
  • ECE PhD QE CNSIP 2003 Problem1
    ...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 o
    5 KB (729 words) - 00:51, 10 March 2015
  • ECE PhD QE CNSIP 2006 Problem1
    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. ...class="inline">\Phi</math> be the standard normal distribution, i.e., the distribution function of a zero-mean, unit-variance Gaussian random variable. Let <math
    5 KB (726 words) - 10:35, 10 March 2015
  • ECE600 F13 rv distribution mhossain
    # 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
  • Walther375Spring2014 Markov chains: what and how?
    \end{bmatrix}</math> <br>General Rule for nth state distribution:<span class="texhtml">''x''<sub>''n''</sub> = ''x''<sub>''n'' − 1</sub>'' ...<span class="texhtml">''X''<sub>''n'' + 3</sub></span>. The initial state distribution can be written as a row vector&nbsp;: <math>\begin{pmatrix}1 & 0\end{pmatri
    19 KB (3,004 words) - 09:39, 23 April 2014
  • Maximum Likelihood Estimation (MLE) for various probability distributions
    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
  • Curse of Dimensionality
    ...r should still work in high dimension. But that requires you to collect an exponential number of training data to avoid ''sparsity''. In practice, the available t ...nce between if you draw just 10 points and if you draw 100 points from the distribution.
    9 KB (1,419 words) - 10:41, 22 January 2015
  • Generation of N-dimensional normally distributed random numbers from two categories with different priors review
    ...Ziggurat Algorithm. Finally, the author explains how to convert normalized distribution to another. ...x algorithm compared to the latter; however, those complexity is caused by exponential formula which is only used for special cases.
    2 KB (367 words) - 16:29, 14 May 2014
  • Maximum Likelihood Estimation Analysis for various Probability Distributions
    ...c Distribution, Binomial Distribution, Poisson Distribution, and Uniform Distribution ** Exponential Distribution
    12 KB (1,986 words) - 10:49, 22 January 2015
  • Generation-of-normally-distributed-random-numbers-under-a-binary-prior-probability
    In the circumstance, a naive assumption about the class distribution helps us synthesize data so that we can train models with a consistent data Among many distributions, Normal distribution is frequently used in many literatures.
    16 KB (2,400 words) - 23:34, 29 April 2014
  • Generation of N-dimensional normally distributed random numbers from two categories with different priors
    ...n train models with a consistent dataset. Among many distributions, Normal distribution is frequently used in many literatures. This tutorial will explain how to g # Normal distribution : How it work? Which is more efficient?
    18 KB (2,852 words) - 10:40, 22 January 2015
  • The principles for how to generate random samples from a Gaussian distribution
    ...h there are more general methods to generate random samples which have any distribution, we will focus on the simple method such as Box Muller transform to generat ...stributed on the interval [0, 1]. And let F be a continuous CDF(cumulative distribution function) of a random variable, X which we want to generate. Then, inverse
    8 KB (1,189 words) - 10:39, 22 January 2015
  • MLEforGMM
    ...ity distribution, MLE provides the estimates for the parameters of density distribution model. In real estimation, we search over all the possible sets of paramete ...o use MLE is to find the vector of parameters that is as close to the true distribution parameter value as possible.<br>
    13 KB (1,966 words) - 10:50, 22 January 2015
  • HW1ECE38F14 Solution
    ===A complex exponential=== ...ows from the [[Homework_3_ECE438F09| scaling property of the Dirac delta]] distribution.
    4 KB (563 words) - 05:31, 22 September 2014
  • ECE PhD QE CNSIP 2013 Problem1.4
    From the property of Normal distribution and exponential distribution,
    3 KB (548 words) - 07:33, 20 November 2014
  • ECE PhD QE CNSIP 2003 Problem1.2
    ...applied to <math class="inline">\mathbf{Y}</math> will yield the desired distribution for <math class="inline">\mathbf{X}</math> ? Prove your answer.
    2 KB (247 words) - 00:53, 10 March 2015
  • ECE PhD QE CNSIP 2004 Problem1.2
    ...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.
    2 KB (366 words) - 01:36, 10 March 2015
  • ECE PhD QE CNSIP 2005 Problem1.3
    – This is Erlang distribution. What kind of distribution does <math class="inline">\mathbf{T}_{1}</math> have?
    4 KB (679 words) - 01:58, 10 March 2015
  • ECE PhD QE CNSIP Jan 2006 Problem1.3
    ...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].
    4 KB (646 words) - 10:26, 10 March 2015
  • HW1ECE438F15 Soln
    ===A complex exponential=== ...ows from the [[Homework_3_ECE438F09| scaling property of the Dirac delta]] distribution.
    5 KB (812 words) - 13:08, 19 October 2015
  • ECE PhD QE CNSIP 2015 Problem1.3
    We then note that the characteristic function of an exponential random variable <math>Z</math> is written as where <math>\lambda</math> parameterizes the exponential distribution. As such, we can write the characteristic function of <math>X+Y</math> as
    2 KB (243 words) - 22:00, 7 March 2016
  • Background of Population
    ...ther sociological factors, such as government policy, technology, age, sex distribution, and economic growth. ...ncludes the carrying capacity of a specific region which is ignored in the exponential one.
    2 KB (269 words) - 22:17, 2 December 2018
  • QE2007 CS-4 ECE580
    Each packet needs time is an exponential distribution <math> \frac{\mu}{m} e^{-\frac{\mu}{n} t} </math><br/>
    5 KB (910 words) - 03:02, 24 February 2019

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