multivariate normal distribution वाक्य
उदाहरण वाक्य
मोबाइल
- Where each \ xi _ { k } is an independent draw from a multivariate normal distribution on \ mathbb { R } ^ { d } with mean 0 and covariance matrix equal to the d \ times d identity matrix.
- By properties of multivariate normal distribution, this means that " P? " and " M? " are independent, and therefore estimators \ hat \ beta and \ hat \ sigma ^ 2 will be independent as well.
- Later work for the multivariate normal distribution allowed the classifier to be nonlinear : several classification rules can be derived based on slight different adjustments of the Mahalanobis distance, with a new observation being assigned to the group whose centre has the lowest adjusted distance from the observation.
- Where we used the fact that \ hat { \ beta }-\ beta is just an affine transformation of \ varepsilon by the matrix ( X'X ) ^ {-1 } X'( see article on the multivariate normal distribution under the affine transformation section ).
- One particular use of the precision matrix is in the context of Bayesian analysis of the multivariate normal distribution : for example, Bernardo & Smith prefer to parameterise the multivariate normal distribution in terms of the precision matrix, rather than the covariance matrix, because of certain simplifications that then arise.
- One particular use of the precision matrix is in the context of Bayesian analysis of the multivariate normal distribution : for example, Bernardo & Smith prefer to parameterise the multivariate normal distribution in terms of the precision matrix, rather than the covariance matrix, because of certain simplifications that then arise.
- For example, in attempting to find the maximum likelihood estimate of a multivariate normal distribution using matrix calculus, if the domain is a " k " x1 column vector, then the result using the numerator layout will be in the form of a 1x " k " row vector.
- The main difference between EDAs and most conventional evolutionary algorithms is that evolutionary algorithms generate new candidate solutions using an " implicit " distribution defined by one or more variation operators, whereas EDAs use an " explicit " probability distribution encoded by a Bayesian network, a multivariate normal distribution, or another model class.
- In that case, the joint distribution of " w " is a multivariate normal distribution; the independence between the variables then implies that the distribution has Hartley, the transform " W " of " w " will be a Gaussian white noise vector, too; that is, the " n"
- If all parameters are scalar values, then this means that there will be one more hyperparameter than parameter; but this also applies to vector-valued and matrix-valued parameters . ( See the general article on the exponential family, and consider also the Wishart distribution, conjugate prior of the covariance matrix of a multivariate normal distribution, for an example where a large dimensionality is involved .)
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