diff --git a/matlab/prior_draw.m b/matlab/prior_draw.m
index 9e49f8d3685d140c73d57ff6444c188ef6e92fa9..8cebcdfe479ab59dbdb08dc2806808820362237e 100644
--- a/matlab/prior_draw.m
+++ b/matlab/prior_draw.m
@@ -175,368 +175,100 @@ if weibull_draws
 end
 
 %@test:1
-%$ %% Initialize required structures
-%$ options_.prior_trunc=0;
-%$ options_.initialize_estimated_parameters_with_the_prior_mode=0;
-%$ 
-%$ M_.dname='test';
-%$ M_.param_names = 'alp';
-%$ ndraws=100000;
-%$ global estim_params_
-%$ estim_params_.var_exo = [];
-%$ estim_params_.var_endo = [];
-%$ estim_params_.corrx = [];
-%$ estim_params_.corrn = [];
-%$ estim_params_.param_vals = [];
-%$ estim_params_.param_vals = [1, NaN, (-Inf), Inf, 1, 0.356, 0.02, NaN, NaN, NaN ];
-%$ 
-%$ %% beta
-%$ estim_params_.param_vals(1,3)= -Inf; % LB
-%$ estim_params_.param_vals(1,4)= +Inf; % UB
-%$ estim_params_.param_vals(1,5)= 1;    % Shape
-%$ estim_params_.param_vals(1,6)=0.5;
-%$ estim_params_.param_vals(1,7)=0.01;
-%$ estim_params_.param_vals(1,8)=NaN;
-%$ estim_params_.param_vals(1,9)=NaN;
-%$ 
-%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_)
-%$  
-%$ pdraw = prior_draw(1,0); pdraw
-%$ pdraw_vec=NaN(ndraws,1);
-%$ for ii=1:ndraws
-%$     pdraw_vec(ii)=prior_draw(0,0);
-%$ end
-%$ 
-%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<0) || any(pdraw_vec>1)
-%$     error('Beta prior wrong')
-%$ end
-%$ 
-%$ 
-%$ %% Gamma
-%$ estim_params_.param_vals(1,3)= -Inf;%LB
-%$ estim_params_.param_vals(1,4)= +Inf;%UB
-%$ estim_params_.param_vals(1,5)= 2;%Shape 
-%$ estim_params_.param_vals(1,6)=0.5;
-%$ estim_params_.param_vals(1,7)=0.01;
-%$ estim_params_.param_vals(1,8)=NaN;
-%$ estim_params_.param_vals(1,9)=NaN;
-%$ 
-%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
-%$ 
-%$ pdraw = prior_draw(1,0);
-%$ pdraw_vec=NaN(ndraws,1);
-%$ for ii=1:ndraws
-%$     pdraw_vec(ii)=prior_draw(0,0);
-%$ end
-%$ 
-%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<0)
-%$     error('Gamma prior wrong')
-%$ end
-%$ 
-%$ %% Normal
-%$ estim_params_.param_vals(1,3)= -Inf;%LB
-%$ estim_params_.param_vals(1,4)= +Inf;%UB
-%$ estim_params_.param_vals(1,5)= 3;%Shape 
-%$ estim_params_.param_vals(1,6)=0.5;
-%$ estim_params_.param_vals(1,7)=0.01;
-%$ estim_params_.param_vals(1,8)=NaN;
-%$ estim_params_.param_vals(1,9)=NaN;
-%$ 
-%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
-%$ 
-%$ pdraw = prior_draw(1,0);
-%$ pdraw_vec=NaN(ndraws,1);
-%$ for ii=1:ndraws
-%$     pdraw_vec(ii)=prior_draw(0,0);
-%$ end
-%$ 
-%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4
-%$     error('Normal prior wrong')
-%$ end
-%$ 
-%$ %% inverse gamma distribution (type 1)
-%$ estim_params_.param_vals(1,3)= -Inf;%LB
-%$ estim_params_.param_vals(1,4)= +Inf;%UB
-%$ estim_params_.param_vals(1,5)= 4;%Shape 
-%$ estim_params_.param_vals(1,6)=0.5;
-%$ estim_params_.param_vals(1,7)=0.01;
-%$ estim_params_.param_vals(1,8)=NaN;
-%$ estim_params_.param_vals(1,9)=NaN;
-%$ 
-%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
-%$ 
-%$ pdraw = prior_draw(1,0);
-%$ pdraw_vec=NaN(ndraws,1);
-%$ for ii=1:ndraws
-%$     pdraw_vec(ii)=prior_draw(0,0);
-%$ end
-%$ 
-%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<0)
-%$     error('inverse gamma distribution (type 1) prior wrong')
-%$ end
-%$ 
-%$ %% Uniform
-%$ estim_params_.param_vals(1,3)= -Inf;%LB
-%$ estim_params_.param_vals(1,4)= +Inf;%UB
-%$ estim_params_.param_vals(1,5)= 5;%Shape 
-%$ estim_params_.param_vals(1,6)=0.5;
-%$ estim_params_.param_vals(1,7)=sqrt(12)^(-1)*(1-0);
-%$ estim_params_.param_vals(1,8)=NaN;
-%$ estim_params_.param_vals(1,9)=NaN;
-%$ 
-%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
-%$ 
-%$ pdraw = prior_draw(1,0);
-%$ pdraw_vec=NaN(ndraws,1);
-%$ for ii=1:ndraws
-%$     pdraw_vec(ii)=prior_draw(0,0);
-%$ end
-%$ 
-%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-2 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-3 || any(pdraw_vec<0) || any(pdraw_vec>1)
-%$     error('Uniform prior wrong')
-%$ end
-%$ 
-%$ %% inverse gamma distribution (type 2)
-%$ estim_params_.param_vals(1,3)= -Inf;%LB
-%$ estim_params_.param_vals(1,4)= +Inf;%UB
-%$ estim_params_.param_vals(1,5)= 6;%Shape 
-%$ estim_params_.param_vals(1,6)=0.5;
-%$ estim_params_.param_vals(1,7)=0.01;
-%$ estim_params_.param_vals(1,8)=NaN;
-%$ estim_params_.param_vals(1,9)=NaN;
-%$ 
-%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
-%$ 
-%$ pdraw = prior_draw(1,0);
-%$ pdraw_vec=NaN(ndraws,1);
-%$ for ii=1:ndraws
-%$     pdraw_vec(ii)=prior_draw(0,0);
-%$ end
-%$ 
-%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<0)
-%$     error('inverse gamma distribution (type 2) prior wrong')
-%$ end
-%$ 
-%$ 
-%$ %%%%%%%%%%%%%%%%%%%%%% Generalized distributions %%%%%%%%%%%%%%%%%%%%%
-%$ 
-%$ %% beta
-%$ estim_params_.param_vals(1,3)= -Inf;%LB
-%$ estim_params_.param_vals(1,4)= +Inf;%UB
-%$ estim_params_.param_vals(1,5)= 1;%Shape
-%$ estim_params_.param_vals(1,6)=1.5;
-%$ estim_params_.param_vals(1,7)=0.01;
-%$ estim_params_.param_vals(1,8)=1;
-%$ estim_params_.param_vals(1,9)=3;
-%$ 
-%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
-%$ 
-%$ pdraw = prior_draw(1,0);
-%$ pdraw_vec=NaN(ndraws,1);
-%$ for ii=1:ndraws
-%$     pdraw_vec(ii)=prior_draw(0,0);
-%$ end
-%$ 
-%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<estim_params_.param_vals(1,3)) || any(pdraw_vec>estim_params_.param_vals(1,4))
-%$     error('Beta prior wrong')
-%$ end
-%$ 
-%$ %% Gamma
-%$ estim_params_.param_vals(1,3)= -Inf;%LB
-%$ estim_params_.param_vals(1,4)= +Inf;%UB
-%$ estim_params_.param_vals(1,5)= 2;%Shape 
-%$ estim_params_.param_vals(1,6)=1.5;
-%$ estim_params_.param_vals(1,7)=0.01;
-%$ estim_params_.param_vals(1,8)=1;
-%$ estim_params_.param_vals(1,9)=NaN;
-%$ 
-%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
-%$ 
-%$ pdraw = prior_draw(1,0);
-%$ pdraw_vec=NaN(ndraws,1);
-%$ for ii=1:ndraws
-%$     pdraw_vec(ii)=prior_draw(0,0);
-%$ end
-%$ 
-%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<estim_params_.param_vals(1,8))
-%$     error('Gamma prior wrong')
-%$ end
-%$ 
-%$ %% inverse gamma distribution (type 1)
-%$ estim_params_.param_vals(1,3)= -Inf;%LB
-%$ estim_params_.param_vals(1,4)= +Inf;%UB
-%$ estim_params_.param_vals(1,5)= 4;%Shape 
-%$ estim_params_.param_vals(1,6)=1.5;
-%$ estim_params_.param_vals(1,7)=0.01;
-%$ estim_params_.param_vals(1,8)=1;
-%$ estim_params_.param_vals(1,9)=NaN;
-%$ 
-%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
-%$ 
-%$ pdraw = prior_draw(1,0);
-%$ pdraw_vec=NaN(ndraws,1);
-%$ for ii=1:ndraws
-%$     pdraw_vec(ii)=prior_draw(0,0);
-%$ end
-%$ 
-%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<estim_params_.param_vals(1,8)) 
-%$     error('inverse gamma distribution (type 1) prior wrong')
-%$ end
-%$ 
-%$ %% Uniform
-%$ estim_params_.param_vals(1,3)= -Inf;%LB
-%$ estim_params_.param_vals(1,4)= +Inf;%UB
-%$ estim_params_.param_vals(1,5)= 5;%Shape 
-%$ estim_params_.param_vals(1,6)=1.5;
-%$ estim_params_.param_vals(1,7)=0.01;
-%$ estim_params_.param_vals(1,8)=NaN;
-%$ estim_params_.param_vals(1,9)=NaN;
-%$ 
-%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
-%$ 
-%$ pdraw = prior_draw(1,0);
-%$ pdraw_vec=NaN(ndraws,1);
-%$ for ii=1:ndraws
-%$     pdraw_vec(ii)=prior_draw(0,0);
-%$ end
-%$ 
-%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<estim_params_.param_vals(1,3)) || any(pdraw_vec>estim_params_.param_vals(1,4))
-%$     error('Uniform prior wrong')
-%$ end
-%$ 
-%$ %% inverse gamma distribution (type 2)
-%$ estim_params_.param_vals(1,3)= -Inf;%LB
-%$ estim_params_.param_vals(1,4)= +Inf;%UB
-%$ estim_params_.param_vals(1,5)= 6;%Shape 
-%$ estim_params_.param_vals(1,6)=1.5;
-%$ estim_params_.param_vals(1,7)=0.01;
-%$ estim_params_.param_vals(1,8)=1;
-%$ estim_params_.param_vals(1,9)=NaN;
-%$ 
-%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
-%$ 
-%$ pdraw = prior_draw(1,0);
-%$ pdraw_vec=NaN(ndraws,1);
-%$ for ii=1:ndraws
-%$     pdraw_vec(ii)=prior_draw(0,0);
-%$ end
-%$ 
-%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<estim_params_.param_vals(1,8)) 
-%$     error('inverse gamma distribution (type 2) prior wrong')
-%$ end
-%$ 
-%$ %%%%%%%%%%%% With prior truncation
-%$ options_.prior_trunc=.4;
-%$ 
-%$ %% beta
-%$ estim_params_.param_vals(1,3)= -Inf;%LB
-%$ estim_params_.param_vals(1,4)= +Inf;%UB
-%$ estim_params_.param_vals(1,5)= 1;%Shape
-%$ estim_params_.param_vals(1,6)=1.5;
-%$ estim_params_.param_vals(1,7)=0.01;
-%$ estim_params_.param_vals(1,8)=1;
-%$ estim_params_.param_vals(1,9)=3;
-%$ 
-%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
-%$ bounds = prior_bounds(bayestopt_,options_.prior_trunc)';
-%$ 
-%$ pdraw = prior_draw(1,0);
-%$ pdraw_vec=NaN(ndraws,1);
-%$ for ii=1:ndraws
-%$     pdraw_vec(ii)=prior_draw(0,0);
-%$ end
-%$ 
-%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>5e-3 || any(pdraw_vec<bounds.lb) || any(pdraw_vec>bounds.ub)
-%$     error('Beta prior wrong')
-%$ end
-%$ 
-%$ %% Gamma
-%$ estim_params_.param_vals(1,3)= -Inf;%LB
-%$ estim_params_.param_vals(1,4)= +Inf;%UB
-%$ estim_params_.param_vals(1,5)= 2;%Shape 
-%$ estim_params_.param_vals(1,6)=1.5;
-%$ estim_params_.param_vals(1,7)=0.01;
-%$ estim_params_.param_vals(1,8)=1;
-%$ estim_params_.param_vals(1,9)=NaN;
-%$ 
-%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
-%$ bounds = prior_bounds(bayestopt_,options_.prior_trunc)';
-%$ 
-%$ pdraw = prior_draw(1,0);
-%$ pdraw_vec=NaN(ndraws,1);
-%$ for ii=1:ndraws
-%$     pdraw_vec(ii)=prior_draw(0,0);
-%$ end
-%$ 
-%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>5e-3 || any(pdraw_vec<bounds.lb) || any(pdraw_vec>bounds.ub)
-%$     error('Gamma prior wrong')
-%$ end
-%$ 
-%$ %% inverse gamma distribution (type 1)
-%$ estim_params_.param_vals(1,3)= -Inf;%LB
-%$ estim_params_.param_vals(1,4)= +Inf;%UB
-%$ estim_params_.param_vals(1,5)= 4;%Shape 
-%$ estim_params_.param_vals(1,6)=1.5;
-%$ estim_params_.param_vals(1,7)=0.01;
-%$ estim_params_.param_vals(1,8)=1;
-%$ estim_params_.param_vals(1,9)=NaN;
-%$ 
-%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
-%$ bounds = prior_bounds(bayestopt_,options_.prior_trunc)';
-%$ 
-%$ pdraw = prior_draw(1,0);
-%$ pdraw_vec=NaN(ndraws,1);
-%$ for ii=1:ndraws
-%$     pdraw_vec(ii)=prior_draw(0,0);
-%$ end
-%$ 
-%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>5e-3 || any(pdraw_vec<bounds.lb) || any(pdraw_vec>bounds.ub)
-%$     error('inverse gamma distribution (type 1) prior wrong')
-%$ end
-%$ 
-%$ %% Uniform
-%$ estim_params_.param_vals(1,3)= -Inf;%LB
-%$ estim_params_.param_vals(1,4)= +Inf;%UB
-%$ estim_params_.param_vals(1,5)= 5;%Shape 
-%$ estim_params_.param_vals(1,6)=1.5;
-%$ estim_params_.param_vals(1,7)=0.01;
-%$ estim_params_.param_vals(1,8)=NaN;
-%$ estim_params_.param_vals(1,9)=NaN;
-%$ 
-%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
-%$ bounds = prior_bounds(bayestopt_,options_.prior_trunc)';
-%$ 
-%$ pdraw = prior_draw(1,0);
-%$ pdraw_vec=NaN(ndraws,1);
-%$ for ii=1:ndraws
-%$     pdraw_vec(ii)=prior_draw(0,0);
-%$ end
-%$ 
-%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>5e-3 || any(pdraw_vec<bounds.lb) || any(pdraw_vec>bounds.ub)
-%$     error('Uniform prior wrong')
-%$ end
-%$ 
-%$ 
-%$ %% inverse gamma distribution (type 2)
-%$ estim_params_.param_vals(1,3)= -Inf;%LB
-%$ estim_params_.param_vals(1,4)= +Inf;%UB
-%$ estim_params_.param_vals(1,5)= 6;%Shape 
-%$ estim_params_.param_vals(1,6)=1.5;
-%$ estim_params_.param_vals(1,7)=0.01;
-%$ estim_params_.param_vals(1,8)=1;
-%$ estim_params_.param_vals(1,9)=NaN;
-%$ 
-%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
-%$ bounds = prior_bounds(bayestopt_,options_.prior_trunc)';
-%$ 
-%$ pdraw = prior_draw(1,0);
-%$ pdraw_vec=NaN(ndraws,1);
-%$ for ii=1:ndraws
-%$     pdraw_vec(ii)=prior_draw(0,0);
-%$ end
-%$ 
-%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>5e-3 || any(pdraw_vec<bounds.lb) || any(pdraw_vec>bounds.ub)
-%$     error('inverse gamma distribution (type 2) prior wrong')
-%$ end
-%$ 
-%@eof:1
+%$ % Fill global structures with required fields...
+%$ prior_trunc = 1e-10;
+%$ p0 = repmat([1; 2; 3; 4; 5; 6; 8], 2, 1);    % Prior shape
+%$ p1 = .4*ones(14,1);                          % Prior mean
+%$ p2 = .2*ones(14,1);                          % Prior std.
+%$ p3 = NaN(14,1);
+%$ p4 = NaN(14,1);
+%$ p5 = NaN(14,1);
+%$ p6 = NaN(14,1);
+%$ p7 = NaN(14,1);
+%$
+%$ for i=1:14
+%$     switch p0(i)
+%$       case 1
+%$         % Beta distribution
+%$         p3(i) = 0;
+%$         p4(i) = 1;
+%$         [p6(i), p7(i)] = beta_specification(p1(i), p2(i)^2, p3(i), p4(i));
+%$         p5(i) = compute_prior_mode([p6(i) p7(i)], 1);
+%$       case 2
+%$         % Gamma distribution
+%$         p3(i) = 0;
+%$         p4(i) = Inf;
+%$         [p6(i), p7(i)] = gamma_specification(p1(i), p2(i)^2, p3(i), p4(i));
+%$         p5(i) = compute_prior_mode([p6(i) p7(i)], 2);
+%$       case 3
+%$         % Normal distribution
+%$         p3(i) = -Inf;
+%$         p4(i) = Inf;
+%$         p6(i) = p1(i);
+%$         p7(i) = p2(i);
+%$         p5(i) = p1(i);
+%$       case 4
+%$         % Inverse Gamma (type I) distribution
+%$         p3(i) = 0;
+%$         p4(i) = Inf;
+%$         [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 1, false);
+%$         p5(i) = compute_prior_mode([p6(i) p7(i)], 4);
+%$       case 5
+%$         % Uniform distribution
+%$         [p1(i), p2(i), p6(i), p7(i)] = uniform_specification(p1(i), p2(i), p3(i), p4(i));
+%$         p3(i) = p6(i);
+%$         p4(i) = p7(i);
+%$         p5(i) = compute_prior_mode([p6(i) p7(i)], 5);
+%$       case 6
+%$         % Inverse Gamma (type II) distribution
+%$         p3(i) = 0;
+%$         p4(i) = Inf;
+%$         [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 2, false);
+%$         p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
+%$       case 8
+%$         % Weibull distribution
+%$         p3(i) = 0;
+%$         p4(i) = Inf;
+%$         [p6(i), p7(i)] = weibull_specification(p1(i), p2(i)^2, p3(i));
+%$         p5(i) = compute_prior_mode([p6(i) p7(i)], 8);
+%$       otherwise
+%$         error('This density is not implemented!')
+%$     end
+%$ end
+%$
+%$ BayesInfo.pshape = p0;
+%$ BayesInfo.p1 = p1;
+%$ BayesInfo.p2 = p2;
+%$ BayesInfo.p3 = p3;
+%$ BayesInfo.p4 = p4;
+%$ BayesInfo.p5 = p5;
+%$ BayesInfo.p6 = p6;
+%$ BayesInfo.p7 = p7;
+%$
+%$ ndraws = 1e5;
+%$ m0 = BayesInfo.p1; %zeros(14,1);
+%$ v0 = diag(BayesInfo.p2.^2); %zeros(14);
+%$
+%$ % Call the tested routine
+%$ try
+%$     % Initialization of prior_draws.
+%$     prior_draw(BayesInfo, prior_trunc, false);
+%$     % Do simulations in a loop and estimate recursively the mean and teh variance.
+%$     for i = 1:ndraws
+%$          draw = transpose(prior_draw());
+%$          m1 = m0 + (draw-m0)/i;
+%$          m2 = m1*m1';
+%$          v0 = v0 + ((draw*draw'-m2-v0) + (i-1)*(m0*m0'-m2'))/i;
+%$          m0 = m1;
+%$     end
+%$     t(1) = true;
+%$ catch
+%$     t(1) = false;
+%$ end
+%$
+%$ if t(1)
+%$     t(2) = all(abs(m0-BayesInfo.p1)<3e-3);
+%$     t(3) = all(all(abs(v0-diag(BayesInfo.p2.^2))<2e-3));
+%$ end
+%$ T = all(t);
+%@eof:1
\ No newline at end of file