def METHOD_NAME(self):
    mcmc = pints.AdaptiveCovarianceMCMC(self.real_parameters)
    self.assertIn('Haario-Bardenet', mcmc.name())
    # Perform short run
    mcmc.set_target_acceptance_rate(0.3)
    mcmc.set_initial_phase(True)
    rate = []
    chain = []
    for i in range(100):
        x = mcmc.ask()
        fx = self.log_posterior(x)
        y, fy, ac = mcmc.tell(fx)
        if i == 20:
            mcmc.set_initial_phase(False)
        if i >= 50:
            chain.append(x)
        rate.append(mcmc.acceptance_rate())
        self.assertTrue(isinstance(ac, bool))
        if ac:
            self.assertTrue(np.all(x == y))
            self.assertEqual(fx, fy)
    chain = np.array(chain)
    rate = np.array(rate)
    self.assertEqual(chain.shape[0], 50)
    self.assertEqual(chain.shape[1], len(self.real_parameters))
    self.assertEqual(rate.shape[0], 100)