distributions.py 文件源码

def get_vlb(self):
        # E[ln p(mu) / q(mu)] part
        h_0, J_0, h_mf, J_mf = self.h_0, self.J_0, self.h_mf, self.J_mf
        Emu, Emu2 = self._E_mu
        p_mu_avgengy = -1./2*J_0*Emu2 + h_0*Emu \
                - 1./2*(h_0**2/J_0) + 1./2*np.log(J_0) - 1./2*np.log(2*np.pi)
        q_mu_entropy = 1./2*np.log(2*np.pi*np.e/J_mf)

        # E[ln p(sigmasq) / q(sigmasq)] part
        alpha_0, beta_0, alpha_mf, beta_mf = \
                self.alpha_0, self.beta_0, self.alpha_mf, self.beta_mf
        (Esigmasqinv, Elnsigmasq) = self._E_sigmasq
        p_sigmasq_avgengy = (-alpha_0-1)*Elnsigmasq + (-beta_0)*Esigmasqinv \
                - (special.gammaln(alpha_0) - alpha_0*np.log(beta_0))
        q_sigmasq_entropy = alpha_mf + np.log(beta_mf) + special.gammaln(alpha_mf) \
                - (1+alpha_mf)*special.digamma(alpha_mf)

        return p_mu_avgengy + q_mu_entropy + p_sigmasq_avgengy + q_sigmasq_entropy