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diff --git a/gsl-1.9/randist/exppow.c b/gsl-1.9/randist/exppow.c
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+/* randist/exppow.c
+ * 
+ * Copyright (C) 1996, 1997, 1998, 1999, 2000, 2006 James Theiler, Brian Gough
+ * Copyright (C) 2006 Giulio Bottazzi
+ * 
+ * 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 2 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, write to the Free Software
+ * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
+ */
+
+#include <config.h>
+#include <math.h>
+#include <gsl/gsl_math.h>
+#include <gsl/gsl_sf_gamma.h>
+#include <gsl/gsl_rng.h>
+#include <gsl/gsl_randist.h>
+
+/* The exponential power probability distribution is  
+
+   p(x) dx = (1/(2 a Gamma(1+1/b))) * exp(-|x/a|^b) dx
+
+   for -infty < x < infty. For b = 1 it reduces to the Laplace
+   distribution. 
+
+   The exponential power distribution is related to the gamma
+   distribution by E = a * pow(G(1/b),1/b), where E is an exponential
+   power variate and G is a gamma variate.
+
+   We use this relation for b < 1. For b >=1 we use rejection methods
+   based on the laplace and gaussian distributions which should be
+   faster.  For b>4 we revert to the gamma method.
+
+   See P. R. Tadikamalla, "Random Sampling from the Exponential Power
+   Distribution", Journal of the American Statistical Association,
+   September 1980, Volume 75, Number 371, pages 683-686.
+   
+*/
+
+double
+gsl_ran_exppow (const gsl_rng * r, const double a, const double b)
+{
+  if (b < 1 || b > 4)
+    {
+      double u = gsl_rng_uniform (r);
+      double v = gsl_ran_gamma (r, 1 / b, 1.0);
+      double z = a * pow (v, 1 / b);
+
+      if (u > 0.5)
+        {
+          return z;
+        }
+      else
+        {
+          return -z;
+        }
+    }
+  else if (b == 1)
+    {
+      /* Laplace distribution */
+      return gsl_ran_laplace (r, a);
+    }
+  else if (b < 2)
+    {
+      /* Use laplace distribution for rejection method, from Tadikamalla */
+
+      double x, h, u;
+
+      double B = pow (1 / b, 1 / b);
+
+      do
+        {
+          x = gsl_ran_laplace (r, B);
+          u = gsl_rng_uniform_pos (r);
+          h = -pow (fabs (x), b) + fabs (x) / B - 1 + (1 / b);
+        }
+      while (log (u) > h);
+
+      return a * x;
+    }
+  else if (b == 2)
+    {
+      /* Gaussian distribution */
+      return gsl_ran_gaussian (r, a / sqrt (2.0));
+    }
+  else
+    {
+      /* Use gaussian for rejection method, from Tadikamalla */
+
+      double x, h, u;
+
+      double B = pow (1 / b, 1 / b);
+
+      do
+        {
+          x = gsl_ran_gaussian (r, B);
+          u = gsl_rng_uniform_pos (r);
+          h = -pow (fabs (x), b) + (x * x) / (2 * B * B) + (1 / b) - 0.5;
+        }
+      while (log (u) > h);
+
+      return a * x;
+    }
+}
+
+double
+gsl_ran_exppow_pdf (const double x, const double a, const double b)
+{
+  double p;
+  double lngamma = gsl_sf_lngamma (1 + 1 / b);
+  p = (1 / (2 * a)) * exp (-pow (fabs (x / a), b) - lngamma);
+  return p;
+}