Index: /trunk/EventBenchCore/src/de/ugoe/cs/eventbench/models/PredictionByPartialMatch.java
===================================================================
--- /trunk/EventBenchCore/src/de/ugoe/cs/eventbench/models/PredictionByPartialMatch.java	(revision 108)
+++ /trunk/EventBenchCore/src/de/ugoe/cs/eventbench/models/PredictionByPartialMatch.java	(revision 109)
@@ -28,4 +28,11 @@
 	 */
 	private static final long serialVersionUID = 1L;
+
+	/**
+	 * <p>
+	 * Minimum order of the Markov model.
+	 * </p>
+	 */
+	private int minOrder;
 
 	/**
@@ -67,6 +74,29 @@
 	 */
 	public PredictionByPartialMatch(int markovOrder, Random r, double probEscape) {
+		this(markovOrder, 0, r, probEscape);
+	}
+
+	/**
+	 * <p>
+	 * Creates a new PredictionByPartialMatch model with a given Markov order
+	 * and escape probability.
+	 * </p>
+	 * 
+	 * @param markovOrder
+	 *            Markov order of the model
+	 * @param minOrder
+	 *            minimum order of the model; if this order is reached, there is
+	 *            no escape
+	 * @param r
+	 *            random number generator used by probabilistic methods of the
+	 *            class
+	 * @param probEscape
+	 *            escape probability used by the model
+	 */
+	public PredictionByPartialMatch(int markovOrder, int minOrder, Random r,
+			double probEscape) {
 		super(markovOrder, r);
 		this.probEscape = probEscape;
+		this.minOrder = minOrder;
 	}
 
@@ -97,6 +127,8 @@
 	 * <p>
 	 * Calculates the probability of the next event based on the formula:<br>
-	 * P_{PPM}(X_n|X_{n-1},...,X_{n-k}) = \sum_{i=k}^1 escape^{i-1}
-	 * P_{MM^i}(X_n|X_{n-1},...,X_{n-i})(1-escape)<br>
+	 * P_{PPM}(X_n|X_{n-1},...,X_{n-k}) = \sum_{i=k}^min escape^{k-i}
+	 * P_{MM^i}(X_n
+	 * |X_{n-1},...,X_{n-i})(1-escape)+escape^(k-min)P(X_n|X_{n-i},...
+	 * ,X_{n-min})<br>
 	 * P_{MM^i} denotes the probability in an i-th order Markov model.
 	 * </p>
@@ -124,19 +156,20 @@
 
 		int countSymbol = trie.getCount(contextCopy, symbol); // N(s\sigma)
-		if (contextCopy.size() == 0) {
-			resultCurrentContex = ((double) countSymbol) / sumCountFollowers;
+		if (sumCountFollowers == 0) {
+			resultCurrentContex = 0.0;
 		} else {
-			if (sumCountFollowers == 0) {
-				resultCurrentContex = 0.0;
-			} else {
-				resultCurrentContex = ((double) countSymbol / sumCountFollowers)
-						* (1 - probEscape);
-			}
+			resultCurrentContex = (double) countSymbol / sumCountFollowers;
+		}
+		if (contextCopy.size() != minOrder) {
+			resultCurrentContex *= (1 - probEscape);
 			contextCopy.remove(0);
-			double probSuffix = getProbability(contextCopy, symbol);
-			if (followers.size() == 0) {
-				resultShorterContex = probSuffix;
-			} else {
-				resultShorterContex = probEscape * probSuffix;
+			if (contextCopy.size() >= minOrder) {
+				double probSuffix = getProbability(contextCopy, symbol);
+
+				if (followers.size() == 0) {
+					resultShorterContex = probSuffix;
+				} else {
+					resultShorterContex = probEscape * probSuffix;
+				}
 			}
 		}