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  "body": "\n## Summary\n\nThe Wishart distribution is a probability distribution on symmetric positive semidefinite matrices.\nIt is the distribution of the scatter matrix $\\sum_i X_i X_i^T$ of independent centred multivariate normal vectors.\nIt is the matrix version of the chi-squared distribution, and it is the sampling distribution of the sample covariance matrix of normal data.\nIn Bayesian statistics it is the conjugate prior of the precision matrix of a multivariate normal distribution.\n\n## Explanations\n\n### L1 human\n\nDraw $n$ random points in $p$-dimensional space from a normal distribution centred at the origin.\nAdd up the matrices $X_i X_i^T$; the result records how the cloud of points spreads in every direction.\nThis random matrix has the Wishart distribution.\nIn dimension $p = 1$ the matrix is the number $\\sum_i X_i^2$, so the Wishart distribution is a scaled chi-squared distribution.\n",
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