293 lines
15 KiB
Matlab
293 lines
15 KiB
Matlab
function myoutput = TaRB_metropolis_hastings_core(myinputs,fblck,nblck,whoiam, ThisMatlab)
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% function myoutput = TaRB_metropolis_hastings_core(myinputs,fblck,nblck,whoiam, ThisMatlab)
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% Contains the most computationally intensive portion of code in
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% random_walk_metropolis_hastings (the 'for xxx = fblck:nblck' loop) using the TaRB algorithm.
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% The branches in that 'for'
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% cycle are completely independent to be suitable for parallel execution.
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%
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% INPUTS
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% o myimput [struc] The mandatory variables for local/remote
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% parallel computing obtained from random_walk_metropolis_hastings.m
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% function.
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% o fblck and nblck [integer] The Metropolis-Hastings chains.
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% o whoiam [integer] In concurrent programming a modality to refer to the different threads running in parallel is needed.
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% The integer whoaim is the integer that
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% allows us to distinguish between them. Then it is the index number of this CPU among all CPUs in the
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% cluster.
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% o ThisMatlab [integer] Allows us to distinguish between the
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% 'main' Matlab, the slave Matlab worker, local Matlab, remote Matlab,
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% ... Then it is the index number of this slave machine in the cluster.
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% OUTPUTS
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% o myoutput [struc]
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% If executed without parallel, this is the original output of 'for b =
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% fblck:nblck'. Otherwise, it's a portion of it computed on a specific core or
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% remote machine. In this case:
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% record;
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% irun;
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% NewFile;
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% OutputFileName
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%
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% ALGORITHM
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% Portion of Tailored Randomized Block Metropolis-Hastings proposed in
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% Chib/Ramamurthy (2010): Tailored randomized block MCMC methods with
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% application to DSGE models, Journal of Econometrics 155, pp. 19-38
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%
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% This implementation differs from the originally proposed one in the
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% treatment of non-positive definite Hessians. Here we
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% - use the Jordan decomposition
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%
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% SPECIAL REQUIREMENTS.
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% None.
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%
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% PARALLEL CONTEXT
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% See the comments in the random_walk_metropolis_hastings.m funtion.
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% Copyright (C) 2006-2015 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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global objective_function_penalty_base;
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if nargin<4,
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whoiam=0;
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end
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% reshape 'myinputs' for local computation.
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% In order to avoid confusion in the name space, the instruction struct2local(myinputs) is replaced by:
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TargetFun=myinputs.TargetFun;
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ProposalFun=myinputs.ProposalFun;
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xparam1=myinputs.xparam1;
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mh_bounds=myinputs.mh_bounds;
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last_draw=myinputs.ix2;
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last_posterior=myinputs.ilogpo2;
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fline=myinputs.fline;
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npar=myinputs.npar;
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nruns=myinputs.nruns;
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NewFile=myinputs.NewFile;
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MAX_nruns=myinputs.MAX_nruns;
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d=myinputs.d;
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InitSizeArray=myinputs.InitSizeArray;
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record=myinputs.record;
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dataset_ = myinputs.dataset_;
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dataset_info = myinputs.dataset_info;
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bayestopt_ = myinputs.bayestopt_;
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estim_params_ = myinputs.estim_params_;
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options_ = myinputs.options_;
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M_ = myinputs.M_;
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oo_ = myinputs.oo_;
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% Necessary only for remote computing!
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if whoiam
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% initialize persistent variables in priordens()
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priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7, bayestopt_.p3,bayestopt_.p4,1);
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end
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MetropolisFolder = CheckPath('metropolis',M_.dname);
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ModelName = M_.fname;
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BaseName = [MetropolisFolder filesep ModelName];
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options_.lik_algo = 1;
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OpenOldFile = ones(nblck,1);
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%
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% Now I run the (nblck-fblck+1) Metropolis-Hastings chains
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%
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block_iter=0;
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for curr_chain = fblck:nblck,
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block_iter=block_iter+1;
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try
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% This will not work if the master uses a random number generator not
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% available in the slave (different Matlab version or
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% Matlab/Octave cluster). Therefore the trap.
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%
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% Set the random number generator type (the seed is useless but needed by the function)
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set_dynare_seed(options_.DynareRandomStreams.algo, options_.DynareRandomStreams.seed);
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% Set the state of the RNG
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set_dynare_random_generator_state(record.InitialSeeds(curr_chain).Unifor, record.InitialSeeds(curr_chain).Normal);
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catch
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% If the state set by master is incompatible with the slave, we only reseed
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set_dynare_seed(options_.DynareRandomStreams.seed+curr_chain);
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end
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if (options_.load_mh_file~=0) && (fline(curr_chain)>1) && OpenOldFile(curr_chain) %load previous draws and likelihood
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load([BaseName '_mh' int2str(NewFile(curr_chain)) '_blck' int2str(curr_chain) '.mat'])
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x2 = [x2;zeros(InitSizeArray(curr_chain)-fline(curr_chain)+1,npar)];
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logpo2 = [logpo2;zeros(InitSizeArray(curr_chain)-fline(curr_chain)+1,1)];
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OpenOldFile(curr_chain) = 0;
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else
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x2 = zeros(InitSizeArray(curr_chain),npar);
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logpo2 = zeros(InitSizeArray(curr_chain),1);
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end
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%Prepare waiting bars
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if whoiam
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prc0=(curr_chain-fblck)/(nblck-fblck+1)*(isoctave || options_.console_mode);
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hh = dyn_waitbar({prc0,whoiam,options_.parallel(ThisMatlab)},['MH (' int2str(curr_chain) '/' int2str(options_.mh_nblck) ')...']);
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else
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hh = dyn_waitbar(0,['Metropolis-Hastings (' int2str(curr_chain) '/' int2str(options_.mh_nblck) ')...']);
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set(hh,'Name','Metropolis-Hastings');
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end
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accepted_draws_this_chain = 0;
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accepted_draws_this_file = 0;
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blocked_draws_counter_this_chain=0;
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blocked_draws_counter_this_chain_this_file=0;
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draw_index_current_file = fline(curr_chain); %get location of first draw in current block
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draw_iter = 1;
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while draw_iter <= nruns(curr_chain)
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%% randomize indices for blocking in this iteration
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indices=randperm(npar)';
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blocks=[1; (1+cumsum((rand(length(indices)-1,1)>(1-options_.TaRB.new_block_probability))))];
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nblocks=blocks(end,1); %get number of blocks this iteration
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current_draw=last_draw(curr_chain,:)'; %get starting point for current draw for updating
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for block_iter=1:nblocks
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blocked_draws_counter_this_chain=blocked_draws_counter_this_chain+1;
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blocked_draws_counter_this_chain_this_file=blocked_draws_counter_this_chain_this_file+1;
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nxopt=length(indices(blocks==block_iter,1)); %get size of current block
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par_start_current_block=current_draw(indices(blocks==block_iter,1));
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[xopt_current_block, fval, exitflag, hess_mat_optimizer, options_, Scale] = dynare_minimize_objective(@TaRB_optimizer_wrapper,par_start_current_block,options_.TaRB.mode_compute,options_,[mh_bounds.lb(indices(blocks==block_iter,1),1) mh_bounds.ub(indices(blocks==block_iter,1),1)],bayestopt_.name,bayestopt_,[],...
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current_draw,indices(blocks==block_iter,1),TargetFun,...% inputs for wrapper
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dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_); %inputs for objective
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objective_function_penalty_base=Inf; %reset penalty that may have been changed by optimizer
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%% covariance for proposal density
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hessian_mat = reshape(hessian('TaRB_optimizer_wrapper',xopt_current_block, ...
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options_.gstep,...
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current_draw,indices(blocks==block_iter,1),TargetFun,...% inputs for wrapper
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dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_),nxopt,nxopt);
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if any(any(isnan(hessian_mat))) || any(any(isinf(hessian_mat)))
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inverse_hessian_mat=eye(nxopt)*1e-4; %use diagonal
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else
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inverse_hessian_mat=inv(hessian_mat); %get inverse Hessian
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if any(any((isnan(inverse_hessian_mat)))) || any(any((isinf(inverse_hessian_mat))))
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inverse_hessian_mat=eye(nxopt)*1e-4; %use diagonal
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end
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end
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[proposal_covariance_Cholesky_decomposition_upper,negeigenvalues]=chol(inverse_hessian_mat);
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%if not positive definite, use generalized Cholesky if
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%Eskow/Schnabel
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if negeigenvalues~=0
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proposal_covariance_Cholesky_decomposition_upper=chol_SE(inverse_hessian_mat,0);
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end
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proposal_covariance_Cholesky_decomposition_upper=proposal_covariance_Cholesky_decomposition_upper*diag(bayestopt_.jscale(indices(blocks==block_iter,1),:));
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%get proposal draw
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if strcmpi(ProposalFun,'rand_multivariate_normal')
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n = nxopt;
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elseif strcmpi(ProposalFun,'rand_multivariate_student')
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n = options_.student_degrees_of_freedom;
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end
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proposed_par = feval(ProposalFun, xopt_current_block', proposal_covariance_Cholesky_decomposition_upper, n);
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% chech whether draw is valid and compute posterior
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if all( proposed_par(:) > mh_bounds.lb(indices(blocks==block_iter,1),:) ) && all( proposed_par(:) < mh_bounds.ub(indices(blocks==block_iter,1),:) )
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try
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logpost = - feval('TaRB_optimizer_wrapper', proposed_par(:),...
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current_draw,indices(blocks==block_iter,1),TargetFun,...% inputs for wrapper
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dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
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catch
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logpost = -inf;
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end
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else
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logpost = -inf;
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end
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%get ratio of proposal densities, required because proposal depends
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%on current mode via Hessian and is thus not symmetric anymore
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if strcmpi(ProposalFun,'rand_multivariate_normal')
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proposal_density_proposed_move_forward=multivariate_normal_pdf(proposed_par,xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
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proposal_density_proposed_move_backward=multivariate_normal_pdf(par_start_current_block',xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
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elseif strcmpi(ProposalFun,'rand_multivariate_student')
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proposal_density_proposed_move_forward=multivariate_student_pdf(proposed_par,xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
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proposal_density_proposed_move_backward=multivariate_student_pdf(par_start_current_block',xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
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end
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accprob=logpost-last_posterior(curr_chain)+ log(proposal_density_proposed_move_backward)-log(proposal_density_proposed_move_forward); %Formula (6), Chib/Ramamurthy
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if (logpost > -inf) && (log(rand) < accprob)
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current_draw(indices(blocks==block_iter,1))=proposed_par;
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last_posterior(curr_chain)=logpost;
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accepted_draws_this_chain =accepted_draws_this_chain +1;
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accepted_draws_this_file = accepted_draws_this_file + 1;
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else %no updating
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%do nothing, keep old value
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end
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end
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%save draws and update stored last values
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x2(draw_index_current_file,:) = current_draw;
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last_draw(curr_chain,:) = current_draw;
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%save posterior after full run through all blocks
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logpo2(draw_index_current_file) = last_posterior(curr_chain);
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prtfrc = draw_iter/nruns(curr_chain);
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if (mod(draw_iter, 3)==0 && ~whoiam) || (mod(draw_iter,50)==0 && whoiam)
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dyn_waitbar(prtfrc,hh,[ 'MH (' int2str(curr_chain) '/' int2str(options_.mh_nblck) ') ' sprintf('Current acceptance ratio %4.3f', accepted_draws_this_chain/blocked_draws_counter_this_chain)]);
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end
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if (draw_index_current_file == InitSizeArray(curr_chain)) || (draw_iter == nruns(curr_chain)) % Now I save the simulations, either because the current file is full or the chain is done
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save([BaseName '_mh' int2str(NewFile(curr_chain)) '_blck' int2str(curr_chain) '.mat'],'x2','logpo2');
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fidlog = fopen([MetropolisFolder '/metropolis.log'],'a');
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fprintf(fidlog,['\n']);
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fprintf(fidlog,['%% Mh' int2str(NewFile(curr_chain)) 'Blck' int2str(curr_chain) ' (' datestr(now,0) ')\n']);
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fprintf(fidlog,' \n');
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fprintf(fidlog,[' Number of simulations.: ' int2str(length(logpo2)) '\n']);
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fprintf(fidlog,[' Acceptance ratio......: ' num2str(accepted_draws_this_file /blocked_draws_counter_this_chain_this_file) '\n']);
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fprintf(fidlog,[' Posterior mean........:\n']);
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for i=1:length(x2(1,:))
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fprintf(fidlog,[' params:' int2str(i) ': ' num2str(mean(x2(:,i))) '\n']);
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end
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fprintf(fidlog,[' log2po:' num2str(mean(logpo2)) '\n']);
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fprintf(fidlog,[' Minimum value.........:\n']);
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for i=1:length(x2(1,:))
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fprintf(fidlog,[' params:' int2str(i) ': ' num2str(min(x2(:,i))) '\n']);
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end
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fprintf(fidlog,[' log2po:' num2str(min(logpo2)) '\n']);
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fprintf(fidlog,[' Maximum value.........:\n']);
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for i=1:length(x2(1,:))
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fprintf(fidlog,[' params:' int2str(i) ': ' num2str(max(x2(:,i))) '\n']);
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end
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fprintf(fidlog,[' log2po:' num2str(max(logpo2)) '\n']);
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fprintf(fidlog,' \n');
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fclose(fidlog);
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%reset counters;
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accepted_draws_this_file = 0;
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blocked_draws_counter_this_chain_this_file=0;
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if draw_iter == nruns(curr_chain) % I record the last draw...
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record.LastParameters(curr_chain,:) = x2(end,:);
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record.LastLogPost(curr_chain) = logpo2(end);
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end
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% size of next file in chain curr_chain
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InitSizeArray(curr_chain) = min(nruns(curr_chain)-draw_iter,MAX_nruns);
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% initialization of next file if necessary
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if InitSizeArray(curr_chain)
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x2 = zeros(InitSizeArray(curr_chain),npar);
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logpo2 = zeros(InitSizeArray(curr_chain),1);
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NewFile(curr_chain) = NewFile(curr_chain) + 1;
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draw_index_current_file = 0;
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end
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end
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draw_iter=draw_iter+1;
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draw_index_current_file = draw_index_current_file + 1;
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end% End of the simulations for one mh-block.
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record.AcceptanceRatio(curr_chain) = accepted_draws_this_chain/blocked_draws_counter_this_chain;
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dyn_waitbar_close(hh);
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[record.LastSeeds(curr_chain).Unifor, record.LastSeeds(curr_chain).Normal] = get_dynare_random_generator_state();
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OutputFileName(block_iter,:) = {[MetropolisFolder,filesep], [ModelName '_mh*_blck' int2str(curr_chain) '.mat']};
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end% End of the loop over the mh-blocks.
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myoutput.record = record;
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myoutput.irun = draw_index_current_file;
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myoutput.NewFile = NewFile;
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myoutput.OutputFileName = OutputFileName; |