diff --git a/demos/java/example3TeContinuousDataKernel.sh b/demos/java/example3TeContinuousDataKernel.sh
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+++ b/demos/java/example3TeContinuousDataKernel.sh
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+#!/bin/bash
+
+# Make sure the latest example source file is compiled.
+javac -classpath "../../infodynamics.jar" "infodynamics/demos/Example3TeContinuousDataKernel.java"
+
+# Run the example:
+java -classpath ".:../../infodynamics.jar" infodynamics.demos.Example3TeContinuousDataKernel
+
diff --git a/demos/java/infodynamics/demos/Example3TeContinuousDataKernel.java b/demos/java/infodynamics/demos/Example3TeContinuousDataKernel.java
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+++ b/demos/java/infodynamics/demos/Example3TeContinuousDataKernel.java
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+package infodynamics.demos;
+
+import infodynamics.utils.RandomGenerator;
+import infodynamics.measures.continuous.kernel.TransferEntropyCalculatorKernel;
+
+/**
+ * 
+ * = Example 3 - Transfer entropy on continuous data using kernel estimators =
+ * 
+ * Simple transfer entropy (TE) calculation on continuous-valued data using the (box) kernel-estimator TE calculator.
+ * 
+ * @author Joseph Lizier
+ *
+ */
+public class Example3TeContinuousDataKernel {
+
+	/**
+	 * @param args
+	 */
+	public static void main(String[] args) throws Exception {
+		
+		// Generate some random normalised data.
+		int numObservations = 1000;
+		double covariance = 0.4;
+
+		// Create destArray correlated to previous value of sourceArray:
+		RandomGenerator rg = new RandomGenerator();
+		double[] sourceArray = rg.generateNormalData(numObservations, 0, 1);
+		double[] destArray = rg.generateNormalData(numObservations, 0, 1-covariance);
+		for (int t = 1; t < numObservations; t++) {
+			destArray[t] += covariance * sourceArray[t-1];
+		}
+		// And an uncorrelated second source
+		double[] sourceArray2 = rg.generateNormalData(numObservations, 0, 1);
+
+		// Create a TE calculator and run it:
+		TransferEntropyCalculatorKernel teCalc =
+				new TransferEntropyCalculatorKernel();
+		teCalc.setProperty("NORMALISE", "true"); // Normalise the individual variables (default)
+		teCalc.initialise(1, 0.5); // Use history length 1 (Schreiber k=1), kernel width of 0.5 normalised units
+		teCalc.setObservations(sourceArray, destArray);
+		// For copied source, should give something close to 1 bit:
+		double result = teCalc.computeAverageLocalOfObservations();
+		System.out.printf("TE result %.4f bits; expected to be close to " +
+				"%.4f bits for these correlated Gaussians but biased upwards\n",
+				result, Math.log(1.0/(1-Math.pow(covariance,2)))/Math.log(2));
+
+		teCalc.initialise(); // Initialise leaving the parameters the same
+		teCalc.setObservations(sourceArray2, destArray);
+		// For random source, it should give something close to 0 bits
+		double result2 = teCalc.computeAverageLocalOfObservations();
+		System.out.printf("TE result %.4f bits; expected to be close to " +
+				"0 bits for uncorrelated Gaussians but will be biased upwards\n",
+				result2);
+	}
+}