multivariate normal distribution वाक्य
उदाहरण वाक्य
मोबाइल
- The equidensity contours of a non-singular multivariate normal distribution are ellipsoids ( i . e . linear transformations of hyperspheres ) centered at the mean.
- A different type of generalization is the normal-inverse-Wishart distribution, essentially the product of a multivariate normal distribution with an inverse Wishart distribution.
- In fact, the DoG as the difference of two Multivariate normal distribution has always a total null sum and convolving it with a uniform signal generates no response.
- Here, p _ \ mathcal { N } ( x | C ) denotes the likelihood of x from a multivariate normal distribution with zero mean and covariance matrix C.
- In statistics, the "'matrix normal distribution "'is a probability distribution that is a generalization of the multivariate normal distribution to matrix-valued random variables.
- Both of these expectations are needed when deriving the variational Bayes update equations in a Bayes network involving a Wishart distribution ( which is the conjugate prior of the multivariate normal distribution ).
- In cases where the contribution of random noise is additive and has a multivariate normal distribution, the problem of maximum likelihood sequence estimation can be reduced to that of a least squares minimization.
- The "'logistic normal distribution "'is a generalization of the logit normal distribution to D-dimensional probability vectors by taking a logistic transformation of a multivariate normal distribution.
- The matrix " t "-distribution shares the same relationship with the multivariate " t "-distribution that the matrix normal distribution shares with the multivariate normal distribution.
- In order to produce a sample, typically the generator is seeded with a randomized input that is sampled from a predefined latent space ( e . g ., a multivariate normal distribution ).
- In particular, his books have extended classical multivariate analysis beyond the multivariate normal distribution to a generalized multivariate analysis using more general " elliptical distributions ", which have elliptically contoured distributions.
- He also framed the Anderson Bahadur algorithm along with Theodore Wilbur Anderson which is used in statistics and engineering for solving binary classification problems when the underlying data have multivariate normal distributions with different covariance matrices.
- He also framed the Anderson Bahadur algorithm along with Raghu Raj Bahadur which is used in statistics and engineering for solving binary classification problems when the underlying data have multivariate normal distributions with different covariance matrices.
- However, only few financial distributions such as the multivariate normal distribution and the multivariate student-t distribution are special cases of elliptical distributions, for which the linear correlation measure can be meaningfully interpreted.
- Named joint distributions that arise frequently in statistics include the multivariate normal distribution, the multivariate stable distribution, the multinomial distribution, the negative multinomial distribution, the multivariate hypergeometric distribution, and the elliptical distribution.
- In multivariate statistics and probability theory, the "'scatter matrix "'is a statistic that is used to make estimates of the covariance matrix, for instance of the multivariate normal distribution.
- It is a more general version of the Wishart distribution, and is used similarly, e . g . as the conjugate prior of the precision matrix of a multivariate normal distribution and matrix normal distribution.
- Two random variables that are normally distributed may fail to be " jointly " normally distributed, i . e ., the vector whose components they are may fail to have a multivariate normal distribution.
- It is a more general version of the inverse Wishart distribution, and is used similarly, e . g . as the conjugate prior of the covariance matrix of a multivariate normal distribution or matrix normal distribution.
- This choice is claimed to have advantages in numerical computations when " ? " is very close to zero and simplify formulas in some contexts, such as in the Bayesian inference of variables with multivariate normal distribution.
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