Index: /trunk/autoquest-core-usageprofiles/src/main/java/de/ugoe/cs/autoquest/usageprofiles/InvertedHighOrderMarkovModel.java
===================================================================
--- /trunk/autoquest-core-usageprofiles/src/main/java/de/ugoe/cs/autoquest/usageprofiles/InvertedHighOrderMarkovModel.java	(revision 2049)
+++ /trunk/autoquest-core-usageprofiles/src/main/java/de/ugoe/cs/autoquest/usageprofiles/InvertedHighOrderMarkovModel.java	(revision 2049)
@@ -0,0 +1,100 @@
+//   Copyright 2012 Georg-August-Universität Göttingen, Germany
+//
+//   Licensed under the Apache License, Version 2.0 (the "License");
+//   you may not use this file except in compliance with the License.
+//   You may obtain a copy of the License at
+//
+//       http://www.apache.org/licenses/LICENSE-2.0
+//
+//   Unless required by applicable law or agreed to in writing, software
+//   distributed under the License is distributed on an "AS IS" BASIS,
+//   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+//   See the License for the specific language governing permissions and
+//   limitations under the License.
+
+package de.ugoe.cs.autoquest.usageprofiles;
+
+import java.util.Collection;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Random;
+
+import de.ugoe.cs.autoquest.eventcore.Event;
+
+/**
+ * <p>
+ * Implements an inverted high-order Markov models. The idea behind this is that the most likely events, become the most unlikely events and the most unlikely events become the most likely events.
+ * </p>
+ * <p>
+ * The empirical probability of an observation is N-n/(k-1)N with N the total number of observations, n the number a specific observation was seen and k the number of different observations. 
+ * 
+ * @author Steffen Herbold
+ * @version 1.0
+ */
+public class InvertedHighOrderMarkovModel extends TrieBasedModel {
+
+    /**
+     * <p>
+     * Id for object serialization.
+     * </p>
+     */
+    private static final long serialVersionUID = 1L;
+
+    /**
+     * <p>
+     * Constructor. Creates a new HighOrderMarkovModel with a defined Markov order.
+     * </p>
+     * 
+     * @param maxOrder
+     *            Markov order of the model
+     * @param r
+     *            random number generator used by probabilistic methods of the class
+     */
+    public InvertedHighOrderMarkovModel(int maxOrder, Random r) {
+        super(maxOrder, r);
+    }
+
+    /**
+     * <p>
+     * Calculates the probability of the next Event being symbol based on the order of the Markov
+     * model. The order is defined in the constructor {@link #HighOrderMarkovModel(int, Random)}.
+     * </p>
+     * 
+     * @see de.ugoe.cs.autoquest.usageprofiles.IStochasticProcess#getProbability(java.util.List,
+     *      de.ugoe.cs.autoquest.eventcore.Event)
+     */
+    @Override
+    public double getProbability(List<Event> context, Event symbol) {
+        if (context == null) {
+            throw new IllegalArgumentException("context must not be null");
+        }
+        if (symbol == null) {
+            throw new IllegalArgumentException("symbol must not be null");
+        }
+        double result = 0.0d;
+
+        List<Event> contextCopy;
+        if (context.size() >= trieOrder) {
+            contextCopy =
+                new LinkedList<Event>(context.subList(context.size() - trieOrder + 1,
+                                                      context.size()));
+        }
+        else {
+            contextCopy = new LinkedList<Event>(context);
+        }
+
+        Collection<Event> followers = trie.getFollowingSymbols(contextCopy);
+        int sumCountFollowers = 0; // N(s\sigma')
+        for (Event follower : followers) {
+            sumCountFollowers += trie.getCount(contextCopy, follower);
+        }
+
+        int countSymbol = trie.getCount(contextCopy, symbol);
+        if (sumCountFollowers != 0) {
+            result = ((double) (sumCountFollowers-countSymbol)) / ((followers.size()-1)*sumCountFollowers);
+        }
+
+        return result;
+    }
+
+}