Exponential family distributions | Conjugate prior distributions | Random matrices | Continuous distributions | Covariance and correlation | Multivariate continuous distributions

Wishart distribution

In statistics, the Wishart distribution is a generalization to multiple dimensions of the gamma distribution. It is named in honor of John Wishart, who first formulated the distribution in 1928. It is a family of probability distributions defined over symmetric, nonnegative-definite random matrices (i.e. matrix-valued random variables). In random matrix theory, the space of Wishart matrices is called the Wishart ensemble. These distributions are of great importance in the estimation of covariance matrices in multivariate statistics. In Bayesian statistics, the Wishart distribution is the conjugate prior of the inverse covariance-matrix of a multivariate-normal random-vector. (Wikipedia).

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Related pages

Complex Wishart distribution | Inverse-Wishart distribution | Multivariate gamma function | Scatter matrix | Precision (statistics) | Conjugate prior | Gamma distribution | Spectral theorem | Trace (linear algebra) | F-distribution | Statistics | Chi-squared distribution | Probability density function | Covariance matrix | Multivariate normal distribution | Bayesian statistics | Hotelling's T-squared distribution | Determinant | Student's t-distribution | Kullback–Leibler divergence | Riemann surface | Likelihood-ratio test | MIMO | Cholesky decomposition | Multivariate statistics | Random matrix | Normal-Wishart distribution | Probability distribution | Cross entropy | Analytic continuation | Characterization (mathematics) | Random variable | Degrees of freedom (statistics) | Estimation of covariance matrices | Normalizing constant | Variance-gamma distribution | Moment-generating function | Sampling distribution | Normal variance-mean mixture | Matrix (mathematics) | Bayesian inference | Characteristic function (probability theory)