enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Normalization (statistics) - Wikipedia

    en.wikipedia.org/wiki/Normalization_(statistics)

    In the simplest cases, normalization of ratings means adjusting values measured on different scales to a notionally common scale, often prior to averaging. In more complicated cases, normalization may refer to more sophisticated adjustments where the intention is to bring the entire probability distributions of adjusted values into alignment.

  3. Singular value decomposition - Wikipedia

    en.wikipedia.org/wiki/Singular_value_decomposition

    After the algorithm has converged, the singular value decomposition = is recovered as follows: the matrix is the accumulation of Jacobi rotation matrices, the matrix is given by normalising the columns of the transformed matrix , and the singular values are given as the norms of the columns of the transformed matrix .

  4. Matrix normal distribution - Wikipedia

    en.wikipedia.org/wiki/Matrix_normal_distribution

    The probability density function for the random matrix X (n × p) that follows the matrix normal distribution , (,,) has the form: (,,) = ⁡ ([() ()]) / | | / | | /where denotes trace and M is n × p, U is n × n and V is p × p, and the density is understood as the probability density function with respect to the standard Lebesgue measure in , i.e.: the measure corresponding to integration ...

  5. Category:Matrix normal forms - Wikipedia

    en.wikipedia.org/wiki/Category:Matrix_normal_forms

    A matrix normal form or matrix canonical form describes the transformation of a matrix to another with special properties. Pages in category "Matrix normal forms" The following 10 pages are in this category, out of 10 total.

  6. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    The simplest case of a normal distribution is known as the standard normal distribution or unit normal distribution. This is a special case when μ = 0 {\textstyle \mu =0} and σ 2 = 1 {\textstyle \sigma ^{2}=1} , and it is described by this probability density function (or density): φ ( z ) = e − z 2 2 2 π . {\displaystyle \varphi (z ...

  7. Normal matrix - Wikipedia

    en.wikipedia.org/wiki/Normal_matrix

    Phrased differently: a matrix is normal if and only if its eigenspaces span C n and are pairwise orthogonal with respect to the standard inner product of C n. The spectral theorem for normal matrices is a special case of the more general Schur decomposition which holds for all square matrices. Let A be a square matrix.

  8. Normal-inverse-Wishart distribution - Wikipedia

    en.wikipedia.org/wiki/Normal-inverse-Wishart...

    In probability theory and statistics, the normal-inverse-Wishart distribution (or Gaussian-inverse-Wishart distribution) is a multivariate four-parameter family of continuous probability distributions. It is the conjugate prior of a multivariate normal distribution with unknown mean and covariance matrix (the inverse of the precision matrix). [1]

  9. Noncentral t-distribution - Wikipedia

    en.wikipedia.org/wiki/Noncentral_t-distribution

    where ¯ is the sample mean and ^ is the unbiased sample variance. Since the right hand side of the second equality exactly matches the characterization of a noncentral t -distribution as described above, T has a noncentral t -distribution with n −1 degrees of freedom and noncentrality parameter n θ / σ {\displaystyle {\sqrt {n}}\theta ...