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adding a first attempt for adaptive Metropolis Hastings

time-shift
Michel Juillard 2011-07-24 19:50:56 +02:00
parent 509cc15191
commit a6281ac9f9
2 changed files with 135 additions and 3 deletions
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132
matlab/adaptive_metropolis_hastings.m Normal file
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@ -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

6
matlab/random_walk_metropolis_hastings.m
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@ -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.