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- [[Category:Multivariate Normal Density]] '''Discussion about Discriminant Functions for the Multivariate Normal Density''' <br />14 KB (2,287 words) - 09:46, 22 January 2015
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- ...t the distributions of the feature vectors for the two classes are (known) normal distributions and that the priors for the classes P(w1) and P(w2) are also ...llustrating the centeral limit theorem when the underlying distribution is normal or chi-square or uniform, bowtie, right wedge, left wedge, and triangular10 KB (1,594 words) - 10:41, 24 March 2008
- [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]],9 KB (1,586 words) - 07:47, 17 January 2013
- [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]],13 KB (2,073 words) - 07:39, 17 January 2013
- ...nd is the function used to generate random numbers. In order to generate a multivariate normally distributed sequence of *n* vectors with mean *mu* and covariance ...declaration:''' example that computes the multivariate normal probability density:3 KB (376 words) - 19:45, 26 March 2008
- construction, feature ranking, multivariate feature selection, efficient search methods, and feature ...ticle from the Journal of Multivariate Analysis on Bayesian Estimators for Normal Discriminant Functions===39 KB (5,715 words) - 09:52, 25 April 2008
- The '''Gausian distribution''', or '''Normal Distribution''' is an important and very widely used probability distributi ...al distribution is zero and the variance is one then it is called standard normal distribution.2 KB (247 words) - 07:32, 10 April 2008
- ...'' is the function used to generate random numbers. In order to generate a multivariate normally distributed sequence of ''n'' vectors with mean ''mu'' and covaria example that computes the multivariate normal probability density:3 KB (379 words) - 09:20, 20 March 2008
- [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|10 KB (1,604 words) - 10:17, 10 June 2013
- [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|13 KB (2,098 words) - 10:21, 10 June 2013
- = Discriminant Functions For The Normal Density - Part 1 = '''Introduction to Normal or Gaussian Distribution'''5 KB (844 words) - 04:43, 13 April 2013
- = Discriminant Functions For The Normal Density - Part 2 = ...from where we left of in [[Discriminant Functions For The Normal(Gaussian) Density|Part 1]], in a problem with feature vector '''y''' and state of nature var11 KB (1,792 words) - 15:09, 19 April 2013
- ***[[Discussion about Discriminant Functions for the Multivariate Normal Density|Text slecture in English]] by Yanzhe Cui *Slectures on Density Estimation10 KB (1,450 words) - 19:50, 2 May 2016
- ...uous values. That is, <math>\rho(x|\omega_i)</math> is a class-conditional density, <math>P(\omega_i)</math> is a prior probability, <math>\rho(x)</math> is a ...on <math>x_i \sim N(\mu_i, \sigma_i^2)</math>. Then, the class-conditional density is given by19 KB (3,255 words) - 09:47, 22 January 2015
- [[Category:Multivariate Normal Density]] '''Discussion about Discriminant Functions for the Multivariate Normal Density''' <br />14 KB (2,287 words) - 09:46, 22 January 2015
- **[[Discussion about Discriminant Functions for the Multivariate Normal Density|Text slecture in English]] by Yanzhe Cui ==3. Global (parametric) Density Estimation Methods==8 KB (1,123 words) - 09:38, 22 January 2015
