diff --git a/java/unittests/infodynamics/measures/continuous/ConditionalMutualInfoMultiVariateAbstractTester.java b/java/unittests/infodynamics/measures/continuous/ConditionalMutualInfoMultiVariateAbstractTester.java
new file mode 100755
index 0000000..04c3739
--- /dev/null
+++ b/java/unittests/infodynamics/measures/continuous/ConditionalMutualInfoMultiVariateAbstractTester.java
@@ -0,0 +1,99 @@
+package infodynamics.measures.continuous;
+
+import infodynamics.utils.MatrixUtils;
+import infodynamics.utils.RandomGenerator;
+import junit.framework.TestCase;
+
+public abstract class ConditionalMutualInfoMultiVariateAbstractTester
+	extends TestCase {
+
+	/**
+	 * Confirm that the local values average correctly back to the average value
+	 * 
+	 * @param condMiCalc a pre-constructed ConditionalMutualInfoCalculatorMultiVariate object
+	 * @param dimensions number of dimensions for the source and dest data to use
+	 * @param timeSteps number of time steps for the random data
+	 */
+	public void testLocalsAverageCorrectly(ConditionalMutualInfoCalculatorMultiVariate condMiCalc,
+			int dimensions, int timeSteps)
+			throws Exception {
+		
+		condMiCalc.initialise(dimensions, dimensions, dimensions);
+		
+		// generate some random data
+		RandomGenerator rg = new RandomGenerator();
+		double[][] sourceData = rg.generateNormalData(timeSteps, dimensions,
+				0, 1);
+		double[][] destData = rg.generateNormalData(timeSteps, dimensions,
+				0, 1);
+		double[][] condData = rg.generateNormalData(timeSteps, dimensions,
+				0, 1);
+		
+		condMiCalc.setObservations(sourceData, destData, condData);
+		
+		//teCalc.setDebug(true);
+		double condmi = condMiCalc.computeAverageLocalOfObservations();
+		//miCalc.setDebug(false);
+		double[] condMiLocal = condMiCalc.computeLocalOfPreviousObservations();
+		
+		System.out.printf("Average was %.5f\n", condmi);
+
+		assertEquals(condmi, MatrixUtils.mean(condMiLocal), 0.00001);
+	}
+	
+	/**
+	 * Confirm that significance testing doesn't alter the average that
+	 * would be returned.
+	 * 
+	 * @param condMiCalc a pre-constructed ConditionalMutualInfoCalculatorMultiVariate object
+	 * @param dimensions number of dimensions for the source and dest data to use
+	 * @param timeSteps number of time steps for the random data
+	 * @throws Exception
+	 */
+	public void testComputeSignificanceDoesntAlterAverage(ConditionalMutualInfoCalculatorMultiVariate condMiCalc,
+			int dimensions, int timeSteps) throws Exception {
+		
+		condMiCalc.initialise(dimensions, dimensions, dimensions);
+		
+		// generate some random data
+		RandomGenerator rg = new RandomGenerator();
+		double[][] sourceData = rg.generateNormalData(timeSteps, dimensions,
+				0, 1);
+		double[][] destData = rg.generateNormalData(timeSteps, dimensions,
+				0, 1);
+		double[][] condData = rg.generateNormalData(timeSteps, dimensions,
+				0, 1);
+		
+		condMiCalc.setObservations(sourceData, destData, condData);
+		
+		//condMiCalc.setDebug(true);
+		double condMi = condMiCalc.computeAverageLocalOfObservations();
+		//condMiCalc.setDebug(false);
+		//double[] condMiLocal = miCalc.computeLocalOfPreviousObservations();
+		
+		System.out.printf("Average was %.5f\n", condMi);
+		
+		// Now look at statistical significance tests
+		int[][] newOrderings = rg.generateDistinctRandomPerturbations(
+				timeSteps, 100);
+		
+		// Compute significance for permuting first variable 
+		condMiCalc.computeSignificance(1, newOrderings);
+		
+		// And compute the average value again to check that it's consistent:
+		for (int i = 0; i < 10; i++) {
+			double averageCheck1 = condMiCalc.computeAverageLocalOfObservations();
+			assertEquals(condMi, averageCheck1);
+		}
+
+		// Compute significance for permuting second variable 
+		condMiCalc.computeSignificance(2, newOrderings);
+		
+		// And compute the average value again to check that it's consistent:
+		for (int i = 0; i < 10; i++) {
+			double averageCheck1 = condMiCalc.computeAverageLocalOfObservations();
+			assertEquals(condMi, averageCheck1);
+		}
+	}
+
+}
diff --git a/java/unittests/infodynamics/measures/continuous/gaussian/ConditionalMutualInfoMultiVariateTester.java b/java/unittests/infodynamics/measures/continuous/gaussian/ConditionalMutualInfoMultiVariateTester.java
new file mode 100755
index 0000000..4910c03
--- /dev/null
+++ b/java/unittests/infodynamics/measures/continuous/gaussian/ConditionalMutualInfoMultiVariateTester.java
@@ -0,0 +1,20 @@
+package infodynamics.measures.continuous.gaussian;
+
+import infodynamics.measures.continuous.ConditionalMutualInfoMultiVariateAbstractTester;
+
+public class ConditionalMutualInfoMultiVariateTester extends
+		ConditionalMutualInfoMultiVariateAbstractTester {
+
+	public void testLocalsAverageCorrectly() throws Exception {
+		ConditionalMutualInfoCalculatorMultiVariateGaussian condMiCalc =
+				new ConditionalMutualInfoCalculatorMultiVariateGaussian();
+		super.testLocalsAverageCorrectly(condMiCalc, 2, 100);
+	}
+
+	public void testComputeSignificanceDoesntAlterAverage() throws Exception {
+		ConditionalMutualInfoCalculatorMultiVariateGaussian condMiCalc =
+				new ConditionalMutualInfoCalculatorMultiVariateGaussian();
+		super.testComputeSignificanceDoesntAlterAverage(condMiCalc, 2, 100);
+	}
+	
+}
diff --git a/java/unittests/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoMultiVariateTester.java b/java/unittests/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoMultiVariateTester.java
new file mode 100755
index 0000000..827fb93
--- /dev/null
+++ b/java/unittests/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoMultiVariateTester.java
@@ -0,0 +1,66 @@
+package infodynamics.measures.continuous.kraskov;
+
+public class ConditionalMutualInfoMultiVariateTester
+	extends infodynamics.measures.continuous.ConditionalMutualInfoMultiVariateAbstractTester {
+
+	/**
+	 * Utility function to create a calculator for the given algorithm number
+	 * 
+	 * @param algNumber
+	 * @return
+	 */
+	public ConditionalMutualInfoCalculatorMultiVariateKraskov getNewCalc(int algNumber) {
+		ConditionalMutualInfoCalculatorMultiVariateKraskov condMiCalc = null;
+		if (algNumber == 1) {
+			condMiCalc = new ConditionalMutualInfoCalculatorMultiVariateKraskov1();
+		} else if (algNumber == 2) {
+			condMiCalc = new ConditionalMutualInfoCalculatorMultiVariateKraskov2();
+		}
+		return condMiCalc;
+	}
+	
+	/**
+	 * Confirm that the local values average correctly back to the average value
+	 * 
+	 */
+	public void checkLocalsAverageCorrectly(int algNumber) throws Exception {
+		
+		ConditionalMutualInfoCalculatorMultiVariateKraskov miCalc = getNewCalc(algNumber);
+		
+		String kraskov_K = "4";
+		
+		miCalc.setProperty(
+				MutualInfoCalculatorMultiVariateKraskov.PROP_K,
+				kraskov_K);
+
+		super.testLocalsAverageCorrectly(miCalc, 2, 100);
+	}
+	public void testLocalsAverageCorrectly() throws Exception {
+		checkLocalsAverageCorrectly(1);
+		checkLocalsAverageCorrectly(2);
+	}
+	
+	/**
+	 * Confirm that significance testing doesn't alter the average that
+	 * would be returned.
+	 * 
+	 * @throws Exception
+	 */
+	public void checkComputeSignificanceDoesntAlterAverage(int algNumber) throws Exception {
+		
+		ConditionalMutualInfoCalculatorMultiVariateKraskov condMiCalc = getNewCalc(algNumber);
+		
+		String kraskov_K = "4";
+		
+		condMiCalc.setProperty(
+				MutualInfoCalculatorMultiVariateKraskov.PROP_K,
+				kraskov_K);
+
+		super.testComputeSignificanceDoesntAlterAverage(condMiCalc, 2, 100);
+	}
+	public void testComputeSignificanceDoesntAlterAverage() throws Exception {
+		checkComputeSignificanceDoesntAlterAverage(1);
+		checkComputeSignificanceDoesntAlterAverage(2);
+	}
+	
+}