diff --git a/pints/_mcmc/_mala.py b/pints/_mcmc/_mala.py
index 784a3c487..fabcfc4ff 100644
--- a/pints/_mcmc/_mala.py
+++ b/pints/_mcmc/_mala.py
@@ -84,7 +84,6 @@ def __init__(self, x0, sigma0=None):
 
         # Set initial state
         self._running = False
-        self._ready_for_tell = False
 
         # Current point and proposed point
         self._current = None
@@ -225,9 +224,8 @@ def tell(self, reply):
         """ See :meth:`pints.SingleChainMCMC.tell()`. """
 
         # Check if we had a proposal
-        if not self._ready_for_tell:
+        if self._proposed is None:
             raise RuntimeError('Tell called before proposal was set.')
-        self._ready_for_tell = False
 
         # Unpack reply
         fx, log_gradient = reply
diff --git a/pints/tests/test_mcmc_mala.py b/pints/tests/test_mcmc_mala.py
index 6b13a690b..a33d33dc8 100755
--- a/pints/tests/test_mcmc_mala.py
+++ b/pints/tests/test_mcmc_mala.py
@@ -20,7 +20,8 @@ class TestMALAMCMC(unittest.TestCase):
     Tests the basic methods of the MALA MCMC routine.
     """
 
-    def test_method(self):
+    def test_short_run(self):
+        # Test a short run with MALA
 
         # Create log pdf
         log_pdf = pints.toy.GaussianLogPDF([5, 5], [[4, 1], [1, 3]])
@@ -30,9 +31,6 @@ def test_method(self):
         sigma = [[3, 0], [0, 3]]
         mcmc = pints.MALAMCMC(x0, sigma)
 
-        # This method needs sensitivities
-        self.assertTrue(mcmc.needs_sensitivities())
-
         # Perform short run
         chain = []
         for i in range(100):
@@ -47,11 +45,13 @@ def test_method(self):
         chain = np.array(chain)
         self.assertEqual(chain.shape[0], 50)
         self.assertEqual(chain.shape[1], len(x0))
-        self.assertTrue(mcmc.acceptance_rate() >= 0.0 and
-                        mcmc.acceptance_rate() <= 1.0)
+        self.assertTrue(0 <= mcmc.acceptance_rate() <= 1.0)
+
+    def test_needs_sensitivities(self):
+        # This method needs sensitivities
 
-        mcmc._proposed = [1, 3]
-        self.assertRaises(RuntimeError, mcmc.tell, (fx, gr))
+        mcmc = pints.MALAMCMC(np.array([2, 2]))
+        self.assertTrue(mcmc.needs_sensitivities())
 
     def test_logging(self):
         # Test logging includes name and custom fields.
@@ -59,29 +59,35 @@ def test_logging(self):
         log_pdf = pints.toy.GaussianLogPDF([5, 5], [[4, 1], [1, 3]])
         x0 = [np.array([2, 2]), np.array([8, 8])]
 
-        mcmc = pints.MCMCSampling(log_pdf, 2, x0, method=pints.MALAMCMC)
+        mcmc = pints.MCMCController(log_pdf, 2, x0, method=pints.MALAMCMC)
         mcmc.set_max_iterations(5)
         with StreamCapture() as c:
             mcmc.run()
         text = c.text()
 
-        self.assertIn('Metropolis-Adjusted Langevin Algorithm (MALA)',
-                      text)
+        self.assertIn('Metropolis-Adjusted Langevin Algorithm (MALA)', text)
         self.assertIn(' Accept.', text)
 
     def test_flow(self):
+        # Test the ask-and-tell flow
 
         log_pdf = pints.toy.GaussianLogPDF([5, 5], [[4, 1], [1, 3]])
         x0 = np.array([2, 2])
 
         # Test initial proposal is first point
         mcmc = pints.MALAMCMC(x0)
-        self.assertTrue(np.all(mcmc.ask() == mcmc._x0))
-
-        # Repeated asks
-        self.assertRaises(RuntimeError, mcmc.ask)
-
-        # Tell without ask
+        self.assertTrue(np.all(mcmc.ask() == x0))
+
+        # Repeated asks return same point
+        self.assertTrue(np.all(mcmc.ask() == x0))
+        self.assertTrue(np.all(mcmc.ask() == x0))
+        self.assertTrue(np.all(mcmc.ask() == x0))
+        for i in range(5):
+            mcmc.tell(log_pdf.evaluateS1(mcmc.ask()))
+        x1 = mcmc.ask()
+        self.assertTrue(np.all(mcmc.ask() == x1))
+
+        # Tell without ask should fail
         mcmc = pints.MALAMCMC(x0)
         self.assertRaises(RuntimeError, mcmc.tell, 0)
 
@@ -101,8 +107,8 @@ def test_flow(self):
         mcmc._running = True
         self.assertRaises(RuntimeError, mcmc._initialise)
 
-    def test_set_hyper_parameters(self):
-        # Tests the parameter interface for this sampler.
+    def test_hyper_parameters(self):
+        # Tests the hyper parameter interface for this sampler.
 
         x0 = np.array([2, 2])
         mcmc = pints.MALAMCMC(x0)
@@ -113,17 +119,24 @@ def test_set_hyper_parameters(self):
         self.assertTrue(np.array_equal(mcmc.epsilon(),
                         0.2 * np.diag(mcmc._sigma0)))
 
+        mcmc = pints.MALAMCMC(np.array([2, 2]))
         self.assertEqual(mcmc.n_hyper_parameters(), 1)
         mcmc.set_hyper_parameters([[3, 2]])
         self.assertTrue(np.array_equal(mcmc.epsilon(), [3, 2]))
+        mcmc.set_hyper_parameters([[5, 5]])
+        self.assertTrue(np.array_equal(mcmc.epsilon(), [5, 5]))
 
-        mcmc._step_size = 5
-        mcmc._scale_vector = np.array([3, 7])
-        mcmc._epsilon = None
+    def test_epsilon(self):
+        # Test the epsilon methods
+
+        mcmc = pints.MALAMCMC(np.array([2, 2]), np.array([3, 3]))
         mcmc.set_epsilon()
-        self.assertTrue(np.array_equal(mcmc.epsilon(), [15, 35]))
+        x = mcmc.epsilon()
+        self.assertAlmostEqual(x[0], 0.6)
+        self.assertAlmostEqual(x[1], 0.6)
         mcmc.set_epsilon([0.4, 0.5])
-        self.assertTrue(np.array_equal(mcmc.epsilon(), [0.4, 0.5]))
+        self.assertTrue(np.all(mcmc.epsilon() == [0.4, 0.5]))
+
         self.assertRaises(ValueError, mcmc.set_epsilon, 3.0)
         self.assertRaises(ValueError, mcmc.set_epsilon, [-2.0, 1])