def log_likelihood_biased(self, count_matrix, transition_matrix):
""" Evaluate AMM likelihood. """
ll_unbiased = msmest.log_likelihood(count_matrix, transition_matrix)
ll_bias = -np.sum(self.experimental_measurement_weights * (self.m_hat - self.experimental_measurements) ** 2.)
return ll_unbiased + ll_bias
def _log_likelihood_biased(C, T, E, mhat, ws):
""" Evaluate AMM likelihood. """
ll_unbiased = log_likelihood(C, T)
ll_bias = -_np.sum(ws * (mhat - E)**2.)
return ll_unbiased + ll_bias
def test_count_matrix(self):
"""Small test cases"""
log = log_likelihood(self.C1, self.T1)
assert_allclose(log, np.log(0.8 * 0.2**3 * 0.9**3 * 0.1))