def _bayes(self, X):
N = len(X)
mu_is_gaussian = isinstance(self.mu, Gaussian)
tau_is_gamma = isinstance(self.tau, Gamma)
if mu_is_gaussian and not tau_is_gamma:
mu = np.mean(X, 0)
tau = self.mu.tau + N * self.tau
self.mu = Gaussian(
mu=(self.mu.mu * self.mu.tau + N * mu * self.tau) / tau,
tau=tau)
elif not mu_is_gaussian and tau_is_gamma:
var = np.var(X, axis=0)
a = self.tau.a + 0.5 * N
b = self.tau.b + 0.5 * N * var
self.tau = Gamma(a, b)
elif mu_is_gaussian and tau_is_gamma:
raise NotImplementedError
else:
raise NotImplementedError
def _bayes_tau(self, X):
N = len(X)
var = np.var(X, axis=0)
a = self.tau.a + 0.5 * N
b = self.tau.b + 0.5 * N * var
self.tau = Gamma(a, b)