Continuous Multivariate distributions

Multivariate Gaussian, a.k.a. Multivariate Normal, distribution

  • Story. This is a generalization of the univariate Gaussian.
  • Example. Finch beaks are measured for beak depth and beak length. The resulting distribution of depths and length is Gaussian distributed. In this case, the Gaussian is bivariate, with μ=(μ_d,μ_l) and \begin{align}\mathsf{\Sigma} = \begin{pmatrix}\sigma_\mathrm{d}^2 & \sigma_\mathrm{dl} \\\sigma_\mathrm{dl} & \sigma_\mathrm{l}^2\end{pmatrix}\end{align}.
  • Parameters. There is one vector-valued parameter, μ, and a matrix-valued parameter, Σ, referred to respectively as the mean and covariance matrix. The covariance matrix is symmetric and strictly positive definite.
  • Support. The K-variate Gaussian distribution is supported on \mathbb{R}^K.
  • Probability density function.

    \begin{align}f(\mathbf{y};\boldsymbol{\mu}, \mathsf{\Sigma}) = \frac{1}{\sqrt{(2\pi)^K \det \mathsf{\Sigma}}}\,\mathrm{exp}\left[-\frac{1}{2}(\mathbf{y} - \boldsymbol{\mu})^T \cdot \mathsf{\Sigma}^{-1}\cdot (\mathbf{y} - \boldsymbol{\mu})\right].\end{align}

  • Usage The usage below assumes that mu is a length K array, Sigma is a K×K symmetric positive definite matrix, and L is a K×K lower-triangular matrix with strictly positive values on teh diagonal that is a Cholesky factor.

    • Package Syntax
    NumPy np.random.multivariate_normal(mu, Sigma)
    SciPy scipy.stats.multivariate_normal(mu, Sigma)
    Stan multi_normal(mu, Sigma)
    NumPy Cholesky np.random.multivariate_normal(mu, np.dot(L, L.T))
    SciPy Cholesky scipy.stats.multivariate_normal(mu, np.dot(L, L.T))
    Stan Cholesky multi_normal_cholesky(mu, L)


  • Related distributions.
    • The Multivariate Gaussian is a generalization of the univariate Gaussian.
  • Notes.
  • The covariance matrix may also be written as Σ=S⋅C⋅S, where
    S=\sqrt{diag(Σ)},
    and entry i, j in the correlation matrix C is

    C_{ij}=σ_{ij}/σ_iσ_j.

    Furthermore, because Σ is symmetric and strictly positive definite, it can be uniquely defined in terms of its Cholesky decomposition, L, which satisfies Σ=L ⋅ LT. In practice, you will almost always use the Cholesky representation of the Multivariate Gaussian distribution in Stan.