190 lines
9.5 KiB
Matlab
190 lines
9.5 KiB
Matlab
function [par, logpost, accepted, neval] = posterior_sampler_iteration(TargetFun,last_draw, last_posterior, sampler_options,varargin)
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% function [par, logpost, accepted, neval] = posterior_sampler_iteration(TargetFun,last_draw, last_posterior, sampler_options,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_)
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% posterior samplers
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%
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% INPUTS
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% TargetFun: string storing the objective function (e.g. 'dsge_likelihood.m')
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% last_draw: parameter vector in last iteration
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% last_posterior: value of the posterior in last iteration
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% sampler_options: posterior sampler options
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% dataset_: the dataset after required transformation
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% dataset_info: Various informations about the dataset (descriptive statistics and missing observations).
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% options_: structure storing the options
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% M_: structure storing the model information
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% estim_params_: structure storing information about estimated parameters
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% bayestopt_: structure storing information about priors
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% mh_bounds: structure containing prior bounds
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% oo_: structure storing the results
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%
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% OUTPUTS
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% par: last accepted parameter vector
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% logpost: value of the posterior after current iteration
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% accepted: share of proposed draws that were accepted
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% neval: number of evaluations (>1 only for slice)
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright © 2015-2023 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 <https://www.gnu.org/licenses/>.
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posterior_sampling_method = sampler_options.posterior_sampling_method;
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mh_bounds = sampler_options.bounds;
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switch posterior_sampling_method
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case 'slice'
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[par, logpost, neval] = slice_sampler(TargetFun,last_draw, [mh_bounds.lb mh_bounds.ub], sampler_options,varargin{:});
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accepted = 1;
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case 'random_walk_metropolis_hastings'
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neval = 1;
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ProposalFun = sampler_options.proposal_distribution;
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proposal_covariance_Cholesky_decomposition = sampler_options.proposal_covariance_Cholesky_decomposition;
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n = sampler_options.n;
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par = feval(ProposalFun, last_draw, proposal_covariance_Cholesky_decomposition, n);
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if all( par(:) > mh_bounds.lb ) && all( par(:) < mh_bounds.ub )
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try
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logpost = - feval(TargetFun, par(:),varargin{:});
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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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r = logpost-last_posterior;
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if (logpost > -inf) && (log(rand) < r)
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accepted = 1;
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else
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accepted = 0;
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par = last_draw;
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logpost = last_posterior;
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end
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case 'tailored_random_block_metropolis_hastings'
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options_=varargin{3};
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bayestopt_=varargin{6};
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npar=length(last_draw);
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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-sampler_options.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'; %get starting point for current draw for updating
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blocked_draws_counter=0;
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accepted_draws_counter=0;
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for block_iter=1:nblocks
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blocked_draws_counter=blocked_draws_counter+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, ~, ~, ~, options_] = dynare_minimize_objective(@TaRB_optimizer_wrapper,par_start_current_block,sampler_options.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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varargin{:}); %inputs for objective
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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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varargin{:}),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 of 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(sampler_options.proposal_distribution,'rand_multivariate_normal')
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n = nxopt;
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elseif strcmpi(sampler_options.proposal_distribution,'rand_multivariate_student')
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n = sampler_options.student_degrees_of_freedom;
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end
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proposed_par = feval(sampler_options.proposal_distribution, xopt_current_block', proposal_covariance_Cholesky_decomposition_upper, n);
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% check 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 = - TaRB_optimizer_wrapper(proposed_par(:),...
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current_draw,indices(blocks==block_iter,1),TargetFun,...% inputs for wrapper
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varargin{:});
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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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if (logpost > -inf)
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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(sampler_options.proposal_distribution,'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(sampler_options.proposal_distribution,'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+ log(proposal_density_proposed_move_backward)-log(proposal_density_proposed_move_forward); %Formula (6), Chib/Ramamurthy
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if (log(rand) < accprob)
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current_draw(indices(blocks==block_iter,1))=proposed_par;
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last_posterior=logpost;
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accepted_draws_counter =accepted_draws_counter +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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end
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accepted=accepted_draws_counter/blocked_draws_counter;
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par = current_draw;
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neval=1;
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logpost = last_posterior; %make sure not a temporary draw is returned;
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case 'independent_metropolis_hastings'
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neval = 1;
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ProposalFun = sampler_options.proposal_distribution;
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ProposalDensity = sampler_options.ProposalDensity;
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proposal_covariance_Cholesky_decomposition = sampler_options.proposal_covariance_Cholesky_decomposition;
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n = sampler_options.n;
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xparam1 = sampler_options.xparam1';
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par = feval(ProposalFun, xparam1, proposal_covariance_Cholesky_decomposition, n);
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if all( par(:) > mh_bounds.lb ) && all( par(:) < mh_bounds.ub )
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try
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logpost = - feval(TargetFun, par(:),varargin{:});
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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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r = logpost - last_posterior + ...
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log(feval(ProposalDensity, last_draw, xparam1, proposal_covariance_Cholesky_decomposition, n)) - ...
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log(feval(ProposalDensity, par, xparam1, proposal_covariance_Cholesky_decomposition, n));
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if (logpost > -inf) && (log(rand) < r)
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accepted = 1;
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else
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accepted = 0;
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par = last_draw;
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logpost = last_posterior;
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end
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end
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