R"""
    Student's T log-likelihood.

    Describes a zero-mean normal variable
    whose precision is gamma distributed. Alternatively, describes the
    mean of several zero-mean normal random variables divided by their
    sample standard deviation.

    .. math::
        f(x \mid \nu) = \frac{\Gamma(\frac{\nu+1}{2})}{\Gamma(\frac{\nu}{2}) \sqrt{\nu\pi}} \left( 1 + \frac{x^2}{\nu} \right)^{-\frac{\nu+1}{2}}

    :Parameters:
      - `x` : Input data.
      - `nu` : Degrees of freedom.

def t_like(x, nu):
    R"""
    Student's T log-likelihood.

    Describes a zero-mean normal variable
    whose precision is gamma distributed. Alternatively, describes the
    mean of several zero-mean normal random variables divided by their
    sample standard deviation.

    .. math::
        f(x \mid \nu) = \frac{\Gamma(\frac{\nu+1}{2})}{\Gamma(\frac{\nu}{2}) \sqrt{\nu\pi}} \left( 1 + \frac{x^2}{\nu} \right)^{-\frac{\nu+1}{2}}

    :Parameters:
      - `x` : Input data.
      - `nu` : Degrees of freedom.

    """
    nu = np.asarray(nu)
    return flib.t(x, nu)