Skip to main content
`Ctrl`+`K`
scipy.special.

# multigammaln

scipy.special.multigammaln(a, d)

  Returns the log of multivariate gamma, also sometimes called the generalized gamma.

  Parameters:
    andarray

      The multivariate gamma is computed for each item of a.

    dint

      The dimension of the space of integration.

  Returns:
    resndarray

      The values of the log multivariate gamma at the given points a.

  Notes

  The formal definition of the multivariate gamma of dimension d for a real a is

  \[\Gamma_d(a) = \int_{A>0} e^{-tr(A)} |A|^{a - (d+1)/2} dA\]

  with the condition \(a > (d-1)/2\), and \(A > 0\) being the set of all the positive definite matrices of dimension d. Note that a is a scalar: the integrand only is multivariate, the argument is not (the function is defined over a subset of the real set).

  This can be proven to be equal to the much friendlier equation

  \[\Gamma_d(a) = \pi^{d(d-1)/4} \prod_{i=1}^{d} \Gamma(a - (i-1)/2).\]

  References

  R. J. Muirhead, Aspects of multivariate statistical theory (Wiley Series in probability and mathematical statistics).

  Examples

      >>> import numpy as np
      >>> from scipy.special import multigammaln, gammaln
      >>> a = 23.5
      >>> d = 10
      >>> multigammaln(a, d)
      454.1488605074416

  Verify that the result agrees with the logarithm of the equation shown above:

      >>> d*(d-1)/4*np.log(np.pi) + gammaln(a - 0.5*np.arange(0, d)).sum()
      454.1488605074416