diff --git a/demos/octave/example10ConditionalTeKraskov.m b/demos/octave/example10ConditionalTeKraskov.m
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+%%
+%%  Java Information Dynamics Toolkit (JIDT)
+%%  Copyright (C) 2016 Joseph T. Lizier
+%%  
+%%  This program is free software: you can redistribute it and/or modify
+%%  it under the terms of the GNU General Public License as published by
+%%  the Free Software Foundation, either version 3 of the License, or
+%%  (at your option) any later version.
+%%  
+%%  This program is distributed in the hope that it will be useful,
+%%  but WITHOUT ANY WARRANTY; without even the implied warranty of
+%%  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+%%  GNU General Public License for more details.
+%%  
+%%  You should have received a copy of the GNU General Public License
+%%  along with this program.  If not, see <http://www.gnu.org/licenses/>.
+%%
+
+% = Example 10 - Conditional Transfer entropy on continuous multivariate data using Kraskov estimators =
+
+% Conditional Transfer entropy (TE) calculation on multivariate continuous-valued data using the Kraskov-estimator TE calculator.
+
+% Change location of jar to match yours:
+javaaddpath('../../infodynamics.jar')
+
+% Generate some random normalised data.
+numObservations = 100000;
+% Keep the sum of squares of these covariances below 1 to allow proper calculation of noise term.
+covarianceToSource = 0.4;
+covarianceToConds = [0.3,0.3];
+if (sumsq([covarianceToSource, covarianceToConds]) >= 1)
+	error('Sum of squares of the covariances must be < 1 here');
+end
+noiseCovar = sqrt(1 - sumsq([covarianceToSource, covarianceToConds]));
+
+% Generate the random variables
+sourceArray = randn(numObservations, 1);
+condArray = randn(numObservations, length(covarianceToConds));
+destArray = [0; covarianceToSource*sourceArray(1:numObservations-1,:) + covarianceToConds(1)*condArray(1:numObservations-1,1) + covarianceToConds(2)*condArray(1:numObservations-1,2) + noiseCovar*randn(numObservations-1, 1)];
+
+% Expected results:
+expectedConditional = -0.5 * log(noiseCovar .* noiseCovar ./ (1 - sumsq(covarianceToConds)));
+expectedPairwise = -0.5 * log(1-covarianceToSource.^2);
+
+% Create a conditional TE calculator and run it:
+teCalc=javaObject('infodynamics.measures.continuous.kraskov.ConditionalTransferEntropyCalculatorKraskov');
+teCalc.initialise(1,1, ... % Destination embedding length (Schreiber k=1) and delays
+		1,1, ...   % Source embedding length (Schreiber l=1) and delays
+		1, ...     % Source-destination delay of 1 (default)
+		octaveToJavaDoubleArray([1,1]), ... % Embedding lengths for each conditional variable
+		octaveToJavaDoubleArray([1,1]), ... % Embedding delays for each conditional variable
+		octaveToJavaDoubleArray([1,1]) ... % conditional-destination delays for each conditional variable
+);
+teCalc.setObservations(octaveToJavaDoubleArray(sourceArray), ...
+	octaveToJavaDoubleArray(destArray), ...
+	octaveToJavaDoubleMatrix(condArray));
+% Perform calculation with correlated source, but no conditioning on other sources:
+conditionalResult = teCalc.computeAverageLocalOfObservations();
+
+% Create a pairwise TE calculator and run it:
+teCalc=javaObject('infodynamics.measures.continuous.kraskov.TransferEntropyCalculatorKraskov');
+teCalc.initialise(); % Use default embeddings of 1 (e.g. Schreiber k=1 and l=1)
+teCalc.setObservations(octaveToJavaDoubleArray(sourceArray), octaveToJavaDoubleArray(destArray));
+% Perform calculation with correlated source, but no conditioning on other sources:
+pairwiseResult = teCalc.computeAverageLocalOfObservations();
+
+% Note that the calculation is a random variable (because the generated
+%  data is a set of random variables) - the result will be of the order
+%  of what we expect, but not exactly equal to it; in fact, there will
+%  be some variance around it. It will probably be biased down here
+%  due to small correlations between the supposedly uncorrelated variables.
+fprintf('From %d samples:\nTE result conditional result = %.4f nats, pairwise = %.4f nats;\nexpected around %.4f nats (conditional) and %.4f nats (pairwise) for the correlated Gaussians\n', ...
+	numObservations, conditionalResult, pairwiseResult, expectedConditional, expectedPairwise);
+
+clear teCalc
+