Search results for "Variance" - Rhea

Create the page "Variance" on this wiki! See also the search results found.

• ECE PhD QE CNSIP 2002 Problem1
  ...thbf{X}</math> has mean <math class="inline">\mu_{\mathbf{X}}</math> and variance <math class="inline">\sigma_{\mathbf{X}}^{2}</math> . The correlation coeff (c) Find the variance of <math class="inline">\mathbf{Z}</math> . Express your answer in terms of
  5 KB (766 words) - 00:16, 10 March 2015
• ECE PhD QE CNSIP 2003 Problem1
  ...{X}_{n}\right\}</math> , has mean <math class="inline">0</math> , and has variance <math class="inline">\sigma^{2}</math> . Define <math class="inline">\mathb
  5 KB (729 words) - 00:51, 10 March 2015
• ECE PhD QE CNSIP 2006 Problem1
  ...th> be a sequence of independent, identically distributed zero-mean, unit-variance Gaussian random variables. The sequence <math class="inline">\mathbf{X}_{n} Find the mean and variance of <math class="inline">\mathbf{X}_{n}</math> .
  5 KB (726 words) - 10:35, 10 March 2015
• ECE PhD QE CNSIP 2008 Problem1
  Find the mean and variance of <math class="inline">\mathbf{Y}_{n}</math> .
  4 KB (632 words) - 11:05, 10 March 2015
• ECE PhD QE CNSIP Jan 2001 Problem1
  Find the mean and variance of the output.
  4 KB (616 words) - 10:19, 13 September 2013
• ECE PhD QE CNSIP Jan 2006 Problem1
  Find the variance of <math class="inline">\mathbf{Z}</math> .
  4 KB (572 words) - 10:24, 10 March 2015
• ECE PhD QE CNSIP Jan 2001 Problem1.1
  ...math> be a random variable with mean <math class="inline">\mu</math> and variance <math class="inline">\sigma^{2}</math> . Then <math class="inline">\forall\
  3 KB (532 words) - 10:36, 13 September 2013
• ECE PhD QE CNSIP Jan 2001 Problem1.4
  Find the mean and variance of the output.
  2 KB (234 words) - 10:37, 13 September 2013
• ECE-QE CS1-2011 solusion-2
  ...e } Y(t) \text{ is WSS, its mean is constant and does not depend on . For variance}
  6 KB (932 words) - 10:30, 13 September 2013
• 2013 Spring ECE 302 Boutin
  ...s_point_3_ECE302_Spring2012_Boutin| Invent a problem on expectation and/or variance of a discrete random variable]] (0.5% course grade bonus)
  10 KB (1,422 words) - 20:14, 30 April 2013
• LectureScheduleECE302Spring13 Boutin
  *2.3 Moments of discrete random variable (expectation, variance) *3.2 Moments of a continuous random variables (expectation, variance)
  4 KB (498 words) - 10:18, 17 April 2013
• Lecture16 blog ECE302S13 Boutin
  ...ng the random variable by a constant "a" has the effect of multiplying the variance by <math>a^2</math>.
  2 KB (336 words) - 12:59, 18 February 2013
• Lecture21 blog ECE302S13 Boutin
  ...t we had already seen that relation when we looked at the expectation and variance of aX+b in general.
  3 KB (393 words) - 08:21, 27 February 2013
• Bonus point 3 ECE302 Spring2012 Boutin
  Invent a problem related to the expectation and/or variance of a discrete random variable and solve it. Then post your problem and solu [[Category:variance]]
  3 KB (467 words) - 18:17, 27 February 2013
• ThirdBonusDude91
  ...[[Category:discrete random variable]] [[Category:expectation]] [[Category:variance]] ...solve numerically.<br> b)What effect does increasing r to .99 have on the variance? Please note that round-off error can be somewhat significant when perform
  4 KB (757 words) - 06:59, 22 February 2013
• ECE302 EC2 Woo
  ...] [[Category:discrete random variable]][[Category:expectation]] [[Category:variance]] To find the variance value of Z We have to find values of E[Z^2] and E[Z].<br>
  2 KB (299 words) - 18:13, 27 February 2013
• Lecture30 blog ECE302S13 Boutin
  In Lecture 30, we defined various 1D and 2D signals (mean, variance, autocorrelation, autocovariance) that can be used to describe the statisti
  2 KB (265 words) - 12:15, 25 March 2013
• Discriminant Functions For The Normal(Gaussian) Density
  and where the expected squared deviation or ''variance'' is ...a;)'' which says that ''x'' is distributed normally with mean ''&mu;'' and variance ''&sigma;²''. Samples from normal distributions tend to cluster
  5 KB (844 words) - 05:43, 13 April 2013
• Lecture37 blog ECE302S13 Boutin
  ...on of sum processes. In particular, we obtained simple expressions for the variance and autocovariance of a sum process. We also described two important proper
  2 KB (299 words) - 06:44, 12 April 2013
• Lab2 OldKiwi
  ...that the autocorrelation function evaluated at the origin is equal to the variance of the sequence. I hope this helps. Good luck.
  2 KB (264 words) - 08:05, 9 April 2013
• Discriminant Functions For The Normal(Gaussian) Density - Part 2
  ...n the features are statistically independent and each feature has the same variance, &sigma;². Here, the covariance matrix is diagonal since its sim ...squared distance ||'''x - &mu;'''||² must be normalized by the variance &sigma;² and offset by adding ln ''P''(''w_i''); there
  11 KB (1,792 words) - 16:09, 19 April 2013
• CorrelationvsCovariance
  ...ou will obtain the definition of variance. Another popular way of defining variance is The standard deviation is simply the square root of the variance.
  7 KB (1,146 words) - 06:19, 5 May 2013
• MA375 discrete math lecture notes
  ** [[2008/10/02_MA375Fall2008walther]] - Recurrence Relations and sequences, variance and independence
  1 KB (167 words) - 09:33, 20 May 2013
• Lemma boutin mhossain 05 2013
  ...ations_of_independent_gaussian_RVs|(proof)]] characterized by a mean and a variance.
  6 KB (1,084 words) - 13:20, 13 June 2013
• Proofs mhossain
  *[[variance_of_LC_of_RVs|Variance of a Linear Combination of Random Variables]]
  2 KB (227 words) - 11:15, 6 October 2013
• Variance of LC of RVs
  By definition, we have that the variance of random variable <math>Z</math> is given by <br/>
  2 KB (333 words) - 14:17, 13 June 2013
• ECE600 F13 rv distribution mhossain
  ...andom variable that is Gaussian distributed with mean <math>\mu</math> and variance <math>\sigma^2</math>.]]</center>
  15 KB (2,637 words) - 12:11, 21 May 2014
• ECE600 F13 Expectation mhossain
  ...ote:''' <math>\qquad</math> E[(X - <math>\mu_X)^2</math>] is called the '''variance''' of X and is often denoted <math>\sigma_X</math><math>^2</math>. The posi Moments generalize mean and variance to nth order expectations.
  8 KB (1,474 words) - 12:12, 21 May 2014
• ECE600 F13 Stochastic Convergence mhossain
  Let X be a random variable with mean <math>\mu</math> and variance <math>\sigma^2</math>. Then, <br/> ...math> be a sequence of iid random variables with mean <math>\mu</math> and variance <math>\sigma^2</math>. Let <br/>
  15 KB (2,578 words) - 12:13, 21 May 2014
• ECE-QE CS1-2012
  ...or random variable <math>\mathbf{X}</math> with mean <math>\mu</math> and variance <math>\sigma^2</math>. In constructing your proof, keep in mind that <math>
  3 KB (449 words) - 21:36, 5 August 2018
• ECE-QE CS1-2012 solusion-1
  ...te and prove the Chebyshev inequality for random variable with mean μ and variance σ². In constructing your proof, keep in mind that may be either ...or discrete random variable with mean <span class="texhtml">μ</span> and variance <span class="texhtml">σ²</span>. Then, <math>\forall \varepsilo
  6 KB (995 words) - 09:21, 15 August 2014
• PCA
  ...erivation of PCA could be found in [4]). Following this idea of maximizing variance, let's derive the formula for PCA. ...mension <span class="texhtml">''P'' &lt; ''M''</span> while maximizing the variance of the projected data. For now we will assume that <span class="texhtml">''
  22 KB (3,459 words) - 10:40, 22 January 2015
• ECE662 Whitening and Coloring Transforms S14 MH
  ...s that the components of our new random variable are uncorrelated and have variance 1. This process is called whitening because the output resembles a white no ...ix. Finally, we must scale the different components so that they have unit variance. In order to do this, we need the eigendecomposition of the original covari
  17 KB (2,603 words) - 10:38, 22 January 2015
• Mle tutorial
  *Maximum Likelihood Estimate is efficient: (the estimates have the smallest variance).
  25 KB (4,187 words) - 10:49, 22 January 2015
• Curse of Dimensionality
  ...ossible (signal representation), then the components that have the largest variance in feature values within classes are kept, which is known as [[PCA|''Princi
  9 KB (1,419 words) - 10:41, 22 January 2015
• Bayersian Parameter Estimation
  In order to form a Gaussian distribution, the variance <math>\sigma_n^2</math> associated with <math>\mu_n</math> could also be ob
  8 KB (1,268 words) - 08:31, 29 April 2014
• Bayes rule in practice review
  ...le of using maximum likelihood estimation (MLE) to estimation the mean and variance of Gaussian data. Finally an experiment was performed to show Bayes classif
  2 KB (259 words) - 12:40, 2 May 2014
• Reviews on curse of dimensionality
  ...to choose the project axes are very important tasks. PCA uses the largest variance axes as a projection axes whereas LDA decides these axes based on the best
  4 KB (628 words) - 09:44, 30 April 2014
• Maximum Likelihood Estimation Analysis for various Probability Distributions
  ...> and <math>\hat{\sigma{}^{2}}</math> are the estimated Mean and estimated Variance.
  12 KB (1,986 words) - 10:49, 22 January 2015
• Estimation Using Nearest Neighbor
  ...leq 2P^*</math>). Obviously we can get better error bound by observing the variance where
  14 KB (2,313 words) - 10:55, 22 January 2015
• ParzenWindow
  == Convergence of the mean and variance == ...x}_n</math>, <math>p_n</math> has mean <math>E(p_n(\textbf{x}))</math> and variance <math>Var(p_n(\textbf{x}))</math>. Thus, <math>p_n(\textbf{x})</math> conve
  11 KB (1,824 words) - 10:53, 22 January 2015
• ParzenWindowReview
  ...density estimation. The report also covers the convergence of the mean and variance as sample size grows to the infinity. In last part, it explains about relat
  2 KB (333 words) - 09:32, 1 May 2014
• 662slecture tang
  ...istributed, and use maximum likelihood estimation to estimate the mean and variance of the training data. Then calculate the Bayesian probability and make the **Parameter estimation for mean,variance
  9 KB (1,382 words) - 10:47, 22 January 2015
• Deviation of Maximum Likelihood Estimators and Basic Properties of ML Method
  Similarly, the variance of the estimator is given by Since the bias, variance, and the MSE of the estimator will depend on the specific value of <math>\t
  14 KB (2,356 words) - 20:48, 30 April 2014
• Bayersian Parameter Estimation: Gaussian Case
  ...an Form and the two most decisive parameters in Gaussian R.V. are mean and variance) Please note that <math>\bar{x_n}</math> is the empirical mean in our known Given the posteriori density <math>p(\mu|D)</math> successfully derived (variance: <math>\sigma_n^2</math> and mean: <math>\mu_n</math> now known), the final
  10 KB (1,625 words) - 10:51, 22 January 2015
• Parzen Window Density Estimation
  ...arphi(\vec{X})</math> is a density function and the values of the mean and variance of the estimated p.d.f. converge. That is, <math>p_n(\vec{X})\xrightarrow{n
  16 KB (2,703 words) - 10:54, 22 January 2015
• Generation-of-normally-distributed-random-numbers-under-a-binary-prior-probability
  Often, a real-world data contains either high variance or high bias hence it is difficult to estimate true density and using such ...isson, its distribution is approximately normal with mean of $\lambda$ and variance of $\lambda$ by the central limit theorem.
  16 KB (2,400 words) - 23:34, 29 April 2014
• Bayes Parameter Estimation with examples
  ...each case, we account 30 trials, which will give us a reasonable mean and variance, where the ground truth of p is 2/3. <center>[[Image:ywfig5.png|600px|Figure 2:Variance of <math>\hat{p}</math> with different prior information]] </center>
  10 KB (1,600 words) - 10:52, 22 January 2015
• Generation of N-dimensional normally distributed random numbers from two categories with different priors
  ...rate a good quality of data. Often, a real-world data contains either high variance or high bias hence it is difficult to estimate true density and using such ...distribution is approximately normal with mean of <math>\lambda</math> and variance of <math>\lambda</math> by the central limit theorem. As a result, a normal
  18 KB (2,852 words) - 10:40, 22 January 2015
• Maximum Likelihood Estimators and Examples
  Similarly, the variance of the estimator is given by Since the bias, variance, and the MSE of the estimator will depend on the specific value of <math>\t
  19 KB (3,418 words) - 10:50, 22 January 2015