elif isinstance(self.gp_link, link_functions.Heaviside):
a = sign*v_i/np.sqrt(tau_i)
Z_hat = np.max(1e-13, std_norm_cdf(z))
N = std_norm_pdf(a)
mu_hat = v_i/tau_i + sign*N/Z_hat/np.sqrt(tau_i)
sigma2_hat = (1. - a*N/Z_hat - np.square(N/Z_hat))/tau_i
else:
//TODO: do we want to revert to numerical quadrature here?
After Change
raise ValueError("Exact moment matching not available for link {}".format(self.gp_link.__name__))
// TODO: Output log_Z_hat instead of Z_hat (needs to be change in all others likelihoods)
return np.exp(log_Z_hat), mu_hat, sigma2_hat
def variational_expectations(self, Y, m, v, gh_points=None, Y_metadata=None):
if isinstance(self.gp_link, link_functions.Probit):
