diff --git a/matlab/DsgeVarLikelihood.m b/matlab/DsgeVarLikelihood.m
index 62a331c5c..557dbcea6 100644
--- a/matlab/DsgeVarLikelihood.m
+++ b/matlab/DsgeVarLikelihood.m
@@ -1,5 +1,4 @@
-function [fval,cost_flag,info,PHI,SIGMAu,iXX] = DsgeVarLikelihood(xparam1,gend)
-
+function [fval,cost_flag,info,PHI,SIGMAu,iXX,prior] = DsgeVarLikelihood(xparam1,gend)
 % function [fval,cost_flag,info,PHI,SIGMAu,iXX] = DsgeVarLikelihood(xparam1,gend)
 % Evaluates the posterior kernel of the bvar-dsge model. 
 % 
@@ -14,14 +13,13 @@ function [fval,cost_flag,info,PHI,SIGMAu,iXX] = DsgeVarLikelihood(xparam1,gend)
 %   o PHI           [double]     Stacked BVAR-DSGE autoregressive matrices (at the mode associated to xparam1).
 %   o SIGMAu        [double]     Covariance matrix of the BVAR-DSGE (at the mode associated to xparam1).
 %   o iXX           [double]     inv(X'X).
+%   o prior         [double]     a matlab structure describing the dsge-var prior.  
 %
 % SPECIAL REQUIREMENTS
 %   None.
 %  
 % part of DYNARE, copyright Dynare Team (2006-2008)
 % Gnu Public License.
-
-
 global bayestopt_ estim_params_ M_ options_
 
 nvx = estim_params_.nvx;
@@ -211,4 +209,13 @@ if (nargout == 6)
     else
         iXX = tmp2;
     end
+end
+
+if (nargout==7)
+    iGXX = inv(GXX);
+    prior.SIGMAstar = GYY - GYX*iGXX*GYX';
+    prior.PHIstar = iGXX*transpose(GYX);
+    prior.ArtificialSampleSize = fix(dsge_prior_weight*T);
+    prior.DF = prior.ArtificialSampleSize - NumberOfParameters - NumberOfObservedVariables;
+    prior.iGXX = iGXX;
 end
\ No newline at end of file
diff --git a/matlab/dsgevar_posterior_density.m b/matlab/dsgevar_posterior_density.m
new file mode 100644
index 000000000..cd84cf15a
--- /dev/null
+++ b/matlab/dsgevar_posterior_density.m
@@ -0,0 +1,79 @@
+function bvar = dsgevar_posterior_density(deep)
+% This function characterizes the posterior distribution of a bvar with 
+% a dsge prior (as in Del Negro and Schorfheide 2003) for a given value 
+% of the deep parameters (structural parameters + the size of the 
+% shocks + dsge_prior_weight).
+%    
+% INPUTS
+%   deep:      [double] a vector with the deep parameters.
+%  
+% OUTPUTS
+%   bvar:      a matlab structure with prior and posterior densities. 
+%  
+% ALGORITHM
+%   ...
+% SPECIAL REQUIREMENTS
+%   none
+%  
+%  
+% part of DYNARE, copyright Dynare Team (1996-2007)
+% Gnu Public License.
+global options_ M_
+
+gend = options_.nobs;
+dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
+DSGE_PRIOR_WEIGHT = floor(gend*(1+dsge_prior_weight));
+
+bvar.NumberOfLags = options_.varlag;
+bvar.NumberOfVariables = size(options_.varobs,1);
+bvar.Constant = 'no';
+bvar.NumberOfEstimatedParameters = bvar.NumberOfLags*bvar.NumberOfVariables;   
+if 
+    bvar.Constant = 'yes';
+    bvar.NumberOfEstimatedParameters = bvar.NumberOfEstimatedParameters + ...
+        bvar.NumberOfVariables;        
+end
+
+[fval,cost_flag,info,PHI,SIGMAu,iXX,prior] =  DsgeVarLikelihood(deep',gend);
+
+% Conditionnal posterior density of the lagged matrices (given Sigma) ->
+% Matric-variate normal distribution.
+bvar.LaggedMatricesConditionalOnSigma.posterior.density = 'matric-variate normal';
+bvar.LaggedMatricesConditionalOnSigma.posterior.arg1 = PHI;
+bvar.LaggedMatricesConditionalOnSigma.posterior.arg2 = 'Sigma';
+bvar.LaggedMatricesConditionalOnSigma.posterior.arg3 = iXX;
+
+% Marginal posterior density of the covariance matrix -> Inverted Wishart. 
+bvar.Sigma.posterior.density = 'inverse wishart';
+bvar.Sigma.posterior.arg1 = SIGMAu*DSGE_PRIOR_WEIGHT;
+bvar.Sigma.posterior.arg2 = DSGE_PRIOR_WEIGHT-bvar.NumberOfEstimatedParameters;
+
+% Marginal posterior density of the lagged matrices -> Generalized 
+% Student distribution (See appendix B.5 in Zellner (1971)).
+bvar.LaggedMatrices.posterior.density = 'matric-variate student';
+bvar.LaggedMatrices.posterior.arg1 = inv(iXX);%P
+bvar.LaggedMatrices.posterior.arg2 = SIGMAu*DSGE_PRIOR_WEIGHT;%Q
+bvar.LaggedMatrices.posterior.arg3 = PHI;%M (posterior mean)
+bvar.LaggedMatrices.posterior.arg4 = DSGE_PRIOR_WEIGHT;%(sample size)
+
+
+
+% Conditionnal posterior density of the lagged matrices (given Sigma) ->
+% Matric-variate normal distribution.
+bvar.LaggedMatricesConditionalOnSigma.prior.density = 'matric-variate normal';
+bvar.LaggedMatricesConditionalOnSigma.prior.arg1 = prior.PHIstar;
+bvar.LaggedMatricesConditionalOnSigma.prior.arg2 = 'Sigma';
+bvar.LaggedMatricesConditionalOnSigma.prior.arg3 = prior.iGXX;
+
+% Marginal posterior density of the covariance matrix -> Inverted Wishart. 
+bvar.Sigma.prior.density = 'inverse wishart';
+bvar.Sigma.prior.arg1 = prior.SIGMAstar*prior.ArtificialSampleSize;
+bvar.Sigma.prior.arg2 = prior.DF;
+
+% Marginal posterior density of the lagged matrices -> Generalized 
+% Student distribution (See appendix B.5 in Zellner (1971)).
+bvar.LaggedMatrices.prior.density = 'matric-variate student';
+bvar.LaggedMatrices.prior.arg1 = inv(iGXX);%P
+bvar.LaggedMatrices.prior.arg2 = prior.SIGMAstar*prior.ArtificialSampleSize;%Q
+bvar.LaggedMatrices.prior.arg3 = prior.PHIstar;%M (posterior mean)
+bvar.LaggedMatrices.prior.arg4 = prior.ArtificialSampleSize;%(sample size)
\ No newline at end of file