--- a/mle_obj.m
+++ b/mle_obj.m
@@ -1,33 +1,67 @@
-## Copyright (C) 2003,2004,2005  Michael Creel michael.creel@uab.es
-##  
-## 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., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+# Copyright (C) 2003,2004,2005  Michael Creel michael.creel@uab.es
+# under the terms of the GNU General Public License.
+# 
+# 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., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 
-## usage: [obj_value, score] = mle_obj(theta, data, model, modelargs)
+## usage: [obj_value, score] = mle_obj(theta, data, model, modelargs, nslaves)
 ##
 ## Returns the average log-likelihood for a specified model
 ## This is for internal use by mle_estimate
 
-# this takes a general model and calculates average log likelihood
-function [obj_value, score] = mle_obj(theta, data, model, modelargs)
-	[obj_value, score] = feval(model, theta, data, modelargs);
-	obj_value = - mean(obj_value);
+
+function [obj_value, score] = mle_obj(theta, data, model, modelargs, nslaves)
+
+	n = rows(data);   
+	
+	if nargin < 5 nslaves = 0; endif
+	if nslaves > 0
+	  global NSLAVES PARALLEL NEWORLD NSLAVES TAG;
+
+		nn = floor(n/(NSLAVES + 1)); # number of obsns per slave
+
+		# The command that the slave nodes will execute
+    cmd=['contrib = mle_obj_nodes(theta, data, model, modelargs, nn); ',...	
+         'MPI_Send(contrib,0,TAG,NEWORLD);'];	
+
+		# send items to slaves
+		NumCmds_Send({"theta", "nn", "cmd"}, {theta, nn, cmd});
+
+		# evaluate last block on master while slaves are busy
+  	obj_value = mle_obj_nodes(theta, data, model, modelargs, nn);
+
+		# collect slaves' results
+		contrib = 0.0; # must be initialized to use MPI_Recv
+  	for i = 1:NSLAVES
+	    MPI_Recv(contrib,i,TAG,NEWORLD);
+			obj_value = obj_value + contrib;
+		endfor
+
+		# compute the average
+  	obj_value = - obj_value / n;
+  	score = "na"; # fix this later to allow analytic score in parallel
+		
+  else # serial version
+    [contribs, score] = feval(model, theta, data, modelargs);
+		obj_value = - mean(contribs);
+    if isnumeric(score) score = - mean(score)'; endif # model passes "na" when score not available
+  endif
+
 
 	# let's bullet-proof this in case the model goes nuts
-	if (((abs(obj_value) == Inf)) || (isnan(obj_value)))
-		obj_value = realmax;
-	endif
+  if (((abs(obj_value) == Inf)) || (isnan(obj_value)))
+    obj_value = realmax/10;
+  endif	    
 
-	if isnumeric(score) score = - mean(score)'; endif
-endfunction	
+endfunction