diff --git a/matlab/adaptive_metropolis_hastings.m b/matlab/adaptive_metropolis_hastings.m
new file mode 100644
index 0000000000000000000000000000000000000000..1875dac9ea190e10efd0f41647f319a0681df478
--- /dev/null
+++ b/matlab/adaptive_metropolis_hastings.m
@@ -0,0 +1,132 @@
+function adaptive_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin)
+%function adaptive_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin)
+% Random walk Metropolis-Hastings algorithm.
+%
+% INPUTS
+% o TargetFun [char] string specifying the name of the objective
+% function (posterior kernel).
+% o xparam1 [double] (p*1) vector of parameters to be estimated (initial values).
+% o vv [double] (p*p) matrix, posterior covariance matrix (at the mode).
+% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
+% o varargin list of argument following mh_bounds
+%
+% OUTPUTS
+% None
+%
+% ALGORITHM
+% Metropolis-Hastings.
+%
+% SPECIAL REQUIREMENTS
+% None.
+%
+% PARALLEL CONTEXT
+% The most computationally intensive part of this function may be executed
+% in parallel. The code sutable to be executed in
+% parallel on multi core or cluster machine (in general a 'for' cycle),
+% is removed from this function and placed in random_walk_metropolis_hastings_core.m funtion.
+% Then the DYNARE parallel package contain a set of pairs matlab functions that can be executed in
+% parallel and called name_function.m and name_function_core.m.
+% In addition in parallel package we have second set of functions used
+% to manage the parallel computation.
+%
+% This function was the first function to be parallelized, later other
+% functions have been parallelized using the same methodology.
+% Then the comments write here can be used for all the other pairs of
+% parallel functions and also for management funtions.
+
+% Copyright (C) 2006-2011 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare 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 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare 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 Dynare. If not, see <http://www.gnu.org/licenses/>.
+
+global M_ options_ bayestopt_ estim_params_ oo_
+
+
+old_options = options_;
+
+accept_target = options_.amh.accept_target;
+
+options_.mh_jscale = tune_scale_parameter(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin{:});
+
+for i=1:options_.amh.cova_steps
+ options_.mh_replic = options_.amh.cova_replic;
+ random_walk_metropolis_hastings(TargetFun,ProposalFun, ...
+ xparam1,vv,mh_bounds,varargin{:});
+ CutSample(M_,options_,estim_params_);
+ [junk,vv] = compute_mh_covariance_matrix();
+ bayestopt_.jscale = tune_scale_parameter(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin{:});
+end
+
+options_.mh_replic = old_options.mh_replic;
+record = random_walk_metropolis_hastings(TargetFun,ProposalFun, ...
+ xparam1,vv,mh_bounds,varargin{:});
+
+
+
+function selected_scale = tune_scale_parameter(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin)
+global options_ bayestopt_
+
+selected_scale = [];
+
+maxit = options_.amh.scale_tuning_maxit;
+accept_target = options_.amh.accept_target;
+test_runs = options_.amh.scale_tuning_test_runs;
+tolerance = options_.amh.scale_tuning_tolerance;
+Scales = zeros(maxit,1);
+AvRates = zeros(maxit,1);
+Scales(1) = options_.mh_jscale;
+
+for i=1:maxit
+ options_.mh_replic = options_.amh.scale_tuning_blocksize;
+ bayestopt_.jscale = Scales(i);
+ record = random_walk_metropolis_hastings(TargetFun,ProposalFun, ...
+ xparam1,vv, ...
+ mh_bounds,varargin{:});
+ AvRates(i) = mean(record.AcceptationRates);
+ disp(AvRates(1:i)')
+
+ if i >= test_runs
+ a_mean = mean(AvRates((i-test_runs+1):i));
+ if (a_mean > (1-tolerance)*accept_target) && ...
+ (a_mean < (1+tolerance)*accept_target)
+ selected_scale = mean(Scales((i-test_runs+1):i));
+ disp(['Selected scale: ' num2str(selected_scale)])
+ return
+ end
+ end
+
+ if i == 1
+ if AvRates(1) > accept_target
+ Scales(i+1) = 2*Scales(i);
+ else
+ Scales(i+1) = Scales(i)/2;
+ end
+ elseif i < maxit
+ X = [ones(i,1) Scales(1:i)];
+ b = X\(AvRates(1:i)-accept_target);
+ Scales(i+1) = -b(1)/b(2);
+ if Scales(i+1) < 0.05
+ Scales(i+1) = 0.05;
+ elseif Scales(i+1) > 2
+ Scales(i+1) = 2;
+ end
+ else
+ error('AMH scale tuning: tuning didn''t converge')
+ end
+
+ options_.load_mh_file = 1;
+
+ disp(Scales(1:i)')
+end
\ No newline at end of file
diff --git a/matlab/random_walk_metropolis_hastings.m b/matlab/random_walk_metropolis_hastings.m
index cf55d0e6535b93de18f3b64739a11723bfe4c99c..48c20fa947aa9eb1c158859b980ffc93a32de0d3 100644
--- a/matlab/random_walk_metropolis_hastings.m
+++ b/matlab/random_walk_metropolis_hastings.m
@@ -1,5 +1,5 @@
-function random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin)
-%function random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin)
+function record=random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin)
+%function record=random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin)
% Random walk Metropolis-Hastings algorithm.
%
% INPUTS
@@ -11,7 +11,7 @@ function random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bou
% o varargin list of argument following mh_bounds
%
% OUTPUTS
-% None
+% o record [struct] structure describing the iterations
%
% ALGORITHM
% Metropolis-Hastings.