def sample(self, model: Model, sigma: Sigma) -> float:
posterior_alpha = sigma.alpha + (model.data.n_obsv / 2.)
posterior_beta = sigma.beta + (0.5 *
(np.sum(np.square(model.residuals()))))
draw = np.power(np.random.gamma(posterior_alpha, 1. / posterior_beta),
-0.5)
return draw
def sample(model: Model, sigma: Sigma) -> float:
posterior_alpha = sigma.alpha + (model.data.n_obsv / 2.)
posterior_beta = sigma.beta + (0.5 * (np.sum(np.square(model.residuals()))))
draw = np.power(invgamma.rvs(posterior_alpha, scale=posterior_beta), 0.5)
return draw