diff --git a/java/unittests/infodynamics/measures/continuous/gaussian/ActiveInfoStorageMultiVariateGaussianTester.java b/java/unittests/infodynamics/measures/continuous/gaussian/ActiveInfoStorageMultiVariateGaussianTester.java
new file mode 100644
index 0000000..ae872c1
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+++ b/java/unittests/infodynamics/measures/continuous/gaussian/ActiveInfoStorageMultiVariateGaussianTester.java
@@ -0,0 +1,123 @@
+package infodynamics.measures.continuous.gaussian;
+
+import infodynamics.measures.continuous.ActiveInfoStorageCalculatorViaMutualInfo;
+import infodynamics.utils.ArrayFileReader;
+import infodynamics.utils.MatrixUtils;
+import infodynamics.utils.RandomGenerator;
+import junit.framework.TestCase;
+
+public class ActiveInfoStorageMultiVariateGaussianTester extends TestCase {
+
+	public void testBasicFunctionality() throws Exception {
+		ArrayFileReader afr = new ArrayFileReader("demos/data/2coupledRandomCols-1.txt");
+		double[][] data = afr.getDouble2DMatrix();
+
+		int[] ks = {1,2,3,4};
+		int[] taus = {1,2,3,4};
+		
+		for (int kIndex = 0; kIndex < ks.length; kIndex++) {
+			int k = ks[kIndex];
+			for (int tauIndex = 0; tauIndex < taus.length; tauIndex++) {
+				int tau = taus[tauIndex];
+		
+				ActiveInfoStorageCalculatorMultiVariateGaussian ais =
+						new ActiveInfoStorageCalculatorMultiVariateGaussian();
+				ais.initialise(data[0].length, k, tau);
+				ais.setObservations(data);
+
+				assertEquals(data.length -  (k-1)*tau - 1,
+						ais.getNumObservations());
+				
+				@SuppressWarnings("unused")
+				double result = ais.computeAverageLocalOfObservations();
+			}
+		}
+	}
+
+	public void testHandlesOneDimension() throws Exception {
+		ArrayFileReader afr = new ArrayFileReader("demos/data/2coupledRandomCols-1.txt");
+		double[][] data = afr.getDouble2DMatrix();
+		double[] column1 = MatrixUtils.selectColumn(data, 0);
+		double[][] column1In2D = MatrixUtils.selectColumns(data, new int[] {0});
+
+		int[] ks = {1,2,3,4};
+		int[] taus = {1,2,3,4};
+		
+		for (int kIndex = 0; kIndex < ks.length; kIndex++) {
+			int k = ks[kIndex];
+			for (int tauIndex = 0; tauIndex < taus.length; tauIndex++) {
+				int tau = taus[tauIndex];
+		
+				ActiveInfoStorageCalculatorMultiVariateGaussian ais =
+						new ActiveInfoStorageCalculatorMultiVariateGaussian();
+				ais.initialise(1, k, tau);
+				ais.setObservations(column1);
+				assertEquals(data.length -  (k-1)*tau - 1,
+						ais.getNumObservations());
+				double resultFromColumn1 = ais.computeAverageLocalOfObservations();
+				
+				ais.initialise(1, k, tau);
+				ais.setObservations(column1In2D);
+				assertEquals(data.length -  (k-1)*tau - 1,
+						ais.getNumObservations());
+				double resultFrom2D = ais.computeAverageLocalOfObservations();
+				
+				assertEquals(resultFromColumn1, resultFrom2D, 0.00000001);
+			}
+		}
+	}
+
+	public void testAutoEmbeddingAIS() throws Exception {
+		System.out.println("Start AIS multivariate autoembedding test (Gaussian).");
+		
+		checkAutoEmbeddingLength(false, false);
+		checkAutoEmbeddingLength(false, true);
+		checkAutoEmbeddingLength(true, false);
+		checkAutoEmbeddingLength(true, true);
+	}
+	
+	public void checkAutoEmbeddingLength(boolean source1Has3, boolean source2Has3) throws Exception {
+		
+		// Generate multivariate data
+		int dataLength = 5000;
+		RandomGenerator rg = new RandomGenerator();
+		double[][] data = rg.generateNormalData(dataLength, 2, 0, 1);
+		
+		for (int i=3; i < dataLength; i++) {
+			data[i][0] = 0.2*data[i-2][0] + 0.2*data[i-1][0] + data[i][0];
+			if (source1Has3) {
+				data[i][0] += 0.2*data[i-3][0];
+			}
+			data[i][1] = 0.2*data[i-2][1] + 0.2*data[i-1][1] + data[i][1];
+			if (source2Has3) {
+				data[i][1] += 0.2*data[i-3][1];
+			}
+		}
+		
+		int correctK = (source1Has3 || source2Has3) ? 3 : 2;
+		
+		// Instantiate calculator and set search bounds
+		ActiveInfoStorageCalculatorMultiVariateGaussian aisCalc = 
+					new ActiveInfoStorageCalculatorMultiVariateGaussian();
+		// Can't search larger than the max we expect because fluctuations can make the selection larger
+		aisCalc.setProperty(ActiveInfoStorageCalculatorViaMutualInfo.PROP_AUTO_EMBED_METHOD,
+				ActiveInfoStorageCalculatorViaMutualInfo.AUTO_EMBED_METHOD_MAX_CORR_AIS);
+		// We only check up to the correct k -- selections beyond this are stochastic, so the algorithm
+		//  could choose a larger value if we let it, and that's not wrong.
+		aisCalc.setProperty(ActiveInfoStorageCalculatorViaMutualInfo.PROP_K_SEARCH_MAX, Integer.toString(correctK));
+		aisCalc.setProperty(ActiveInfoStorageCalculatorViaMutualInfo.PROP_TAU_SEARCH_MAX, "1");
+		aisCalc.setDebug(true);
+		
+		// Run optimisation
+		aisCalc.initialise(2);
+		aisCalc.setObservations(data);
+		int optimisedK = Integer.parseInt(aisCalc.getProperty(ActiveInfoStorageCalculatorViaMutualInfo.K_PROP_NAME));
+		
+		// Test that answer was correct
+		assertEquals(correctK, optimisedK);
+		
+		double estimate = aisCalc.computeAverageLocalOfObservations();
+		System.out.printf("Computed average was %.5f from %d samples (embedding length optimisedK = %d\n",
+				estimate, aisCalc.getNumObservations(), optimisedK);
+	}
+}