posterior / distributions

    return dot(dot(dot(ch, z), z.T), ch.T)

def sample_wishart_v2(nu, Lambda):

    """

    From Sawyer, et. al. 'Wishart Distributions and Inverse-Wishart Sampling'

    Runs in constant time

    Untested

    """

    d = Lambda.shape[0]

    ch = cholesky(Lambda)

    T = numpy.zeros((d, d))

    for i in xrange(d):

        if i != 0:

            T[i, :i] = numpy.random.normal(size=(i,))

        T[i, i] = sqrt(chi2.rvs(nu - i + 1))

    return dot(dot(dot(ch, T), T.T), ch.T)

def sample_inverse_wishart(nu, S):

    return S

def sample_wishart(nu, Lambda):

    ch = cholesky(Lambda)

    d = Lambda.shape[0]

    z = numpy.zeros((d, d))

    for i in xrange(d):

        if i != 0:

            z[i, :i] = numpy.random.normal(size=(i,))

        z[i, i] = sqrt(numpy.random.gamma(0.5 * nu - d + 1, 2.0))

    return dot(dot(dot(ch, z), z.T), ch.T)

def sample_wishart_v2(nu, Lambda):