514 lines
25 KiB
Matlab
514 lines
25 KiB
Matlab
function [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, fname, dname, options_, bounds, bayestopt_, outputFolderName)
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% Initialization of posterior samplers
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%
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% INPUTS
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% - posterior_sampler_options [struct] posterior sampler options
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% - options_ [struct] options
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% - bounds [struct] prior bounds
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% - bayestopt_ [struct] information about priors
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%
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% OUTPUTS
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% - posterior_sampler_options [struct] checked posterior sampler options (updated)
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% - options_ [struct] options (updated)
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% - bayestopt_ [struct] information about priors (updated)
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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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if nargin < 7
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outputFolderName = 'Output';
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end
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init = false;
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if isempty(posterior_sampler_options)
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init = true;
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end
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if init
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% set default options and user defined options
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posterior_sampler_options.posterior_sampling_method = options_.posterior_sampler_options.posterior_sampling_method;
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if ~issmc(options_)
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posterior_sampler_options.bounds = bounds;
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end
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switch posterior_sampler_options.posterior_sampling_method
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case 'random_walk_metropolis_hastings'
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posterior_sampler_options.parallel_bar_refresh_rate=50;
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posterior_sampler_options.serial_bar_refresh_rate=20;
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posterior_sampler_options.parallel_bar_title='RWMH';
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posterior_sampler_options.serial_bar_title='RW Metropolis-Hastings';
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% default options
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posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.rwmh);
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% user defined options
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if ~isempty(options_.posterior_sampler_options.sampling_opt)
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options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
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for i=1:rows(options_list)
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switch options_list{i,1}
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case 'proposal_distribution'
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if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
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strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
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error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
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'rand_multivariate_student or rand_multivariate_normal as options']);
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else
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posterior_sampler_options.proposal_distribution=options_list{i,2};
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end
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case 'student_degrees_of_freedom'
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if options_list{i,2} <= 0
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error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
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else
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posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
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end
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case 'use_mh_covariance_matrix'
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% indicates to use the covariance matrix from previous iterations to
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% define the covariance of the proposal distribution
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% default = 0
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posterior_sampler_options.use_mh_covariance_matrix = options_list{i,2};
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options_.use_mh_covariance_matrix = options_list{i,2};
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case 'scale_file'
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% load optimal_mh_scale parameter if previous run was with mode_compute=6
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% will overwrite jscale from set_prior.m
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if exist(options_list{i,2},'file') || exist([options_list{i,2},'.mat'],'file')
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tmp = load(options_list{i,2},'Scale');
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bayestopt_.mh_jscale = tmp.Scale;
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options_.mh_jscale = tmp.Scale;
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bayestopt_.jscale = ones(size(bounds.lb,1),1)*tmp.Scale;
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% options_.mh_init_scale = 2*options_.mh_jscale;
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else
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error('initial_estimation_checks:: The specified mh_scale_file does not exist.')
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end
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case 'save_tmp_file'
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posterior_sampler_options.save_tmp_file = options_list{i,2};
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otherwise
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warning(['rwmh_sampler: Unknown option (' options_list{i,1} ')!'])
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end
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end
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end
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case 'tailored_random_block_metropolis_hastings'
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posterior_sampler_options.parallel_bar_refresh_rate=50;
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posterior_sampler_options.serial_bar_refresh_rate=20;
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posterior_sampler_options.parallel_bar_title='TaRB-MH';
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posterior_sampler_options.serial_bar_title='TaRB Metropolis-Hastings';
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% default options
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posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.tarb);
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% user defined options
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if ~isempty(options_.posterior_sampler_options.sampling_opt)
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options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
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for i=1:rows(options_list)
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switch options_list{i,1}
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case 'proposal_distribution'
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if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
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strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
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error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
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'rand_multivariate_student or rand_multivariate_normal as options']);
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else
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posterior_sampler_options.proposal_distribution=options_list{i,2};
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end
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case 'student_degrees_of_freedom'
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if options_list{i,2} <= 0
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error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
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else
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posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
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end
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case 'mode_compute'
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posterior_sampler_options.mode_compute=options_list{i,2};
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case 'optim'
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posterior_sampler_options.optim_opt=options_list{i,2};
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case 'new_block_probability'
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if options_list{i,2}<0 || options_list{i,2}>1
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error('check_posterior_sampler_options:: The tarb new_block_probability must be between 0 and 1!')
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else
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posterior_sampler_options.new_block_probability=options_list{i,2};
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end
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case 'scale_file'
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% load optimal_mh_scale parameter if previous run was with mode_compute=6
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% will overwrite jscale from set_prior.m
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if exist(options_list{i,2},'file') || exist([options_list{i,2},'.mat'],'file')
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tmp = load(options_list{i,2},'Scale');
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bayestopt_.mh_jscale = tmp.Scale;
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options_.mh_jscale = tmp.Scale;
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bayestopt_.jscale = ones(size(bounds.lb,1),1)*tmp.Scale;
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% options_.mh_init_scale = 2*options_.mh_jscale;
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else
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error('initial_estimation_checks:: The specified scale_file does not exist.')
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end
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case 'save_tmp_file'
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posterior_sampler_options.save_tmp_file = options_list{i,2};
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otherwise
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warning(['tarb_sampler: Unknown option (' options_list{i,1} ')!'])
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end
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end
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end
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case 'independent_metropolis_hastings'
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posterior_sampler_options.parallel_bar_refresh_rate=50;
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posterior_sampler_options.serial_bar_refresh_rate=10;
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posterior_sampler_options.parallel_bar_title='IMH';
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posterior_sampler_options.serial_bar_title='Ind. Metropolis-Hastings';
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% default options
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posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.imh);
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% user defined options
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if ~isempty(options_.posterior_sampler_options.sampling_opt)
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options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
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for i=1:rows(options_list)
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switch options_list{i,1}
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case 'proposal_distribution'
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if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
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strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
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error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
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'rand_multivariate_student or rand_multivariate_normal as options']);
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else
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posterior_sampler_options.proposal_distribution=options_list{i,2};
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end
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case 'student_degrees_of_freedom'
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if options_list{i,2} <= 0
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error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
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else
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posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
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end
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case 'use_mh_covariance_matrix'
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% indicates to use the covariance matrix from previous iterations to
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% define the covariance of the proposal distribution
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% default = 0
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posterior_sampler_options.use_mh_covariance_matrix = options_list{i,2};
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options_.use_mh_covariance_matrix = options_list{i,2};
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case 'save_tmp_file'
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posterior_sampler_options.save_tmp_file = options_list{i,2};
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otherwise
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warning(['imh_sampler: Unknown option (' options_list{i,1} ')!'])
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end
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end
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end
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case 'slice'
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posterior_sampler_options.parallel_bar_refresh_rate=1;
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posterior_sampler_options.serial_bar_refresh_rate=1;
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posterior_sampler_options.parallel_bar_title='SLICE';
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posterior_sampler_options.serial_bar_title='SLICE';
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% default options
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posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.slice);
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% user defined options
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if ~isempty(options_.posterior_sampler_options.sampling_opt)
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options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
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for i=1:rows(options_list)
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switch options_list{i,1}
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case 'rotated'
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% triggers rotated slice iterations using a covariance
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% matrix from initial burn-in iterations
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% must be associated with:
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% <use_mh_covariance_matrix> or <slice_initialize_with_mode>
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% default = 0
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posterior_sampler_options.rotated = options_list{i,2};
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case 'mode'
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% for multimodal posteriors, provide the list of modes as a
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% matrix, ordered by column, i.e. [x1 x2 x3] for three
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% modes x1 x2 x3
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% MR note: not sure this is possible with the
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% read_key_value_string ???
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% if this is not possible <mode_files> does to job in any case
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% This will automatically trigger <rotated>
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% default = []
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tmp_mode = options_list{i,2};
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for j=1:size(tmp_mode,2)
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posterior_sampler_options.mode(j).m = tmp_mode(:,j);
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end
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case 'mode_files'
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% for multimodal posteriors provide the name of
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% a file containing a variable array xparams = [nparam * nmodes]
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% one column per mode. With this info, the code will automatically
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% set the <mode> option.
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% This will automatically trigger <rotated>
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% default = []
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posterior_sampler_options.mode_files = options_list{i,2};
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case 'slice_initialize_with_mode'
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% the default for slice is to set mode_compute = 0 in the
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% preprocessor and start the chain(s) from a random location in the prior.
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% This option first runs the optimizer and then starts the
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% chain from the mode. Associated with optios <rotated>, it will
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% use invhess from the mode to perform rotated slice
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% iterations.
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% default = 0
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posterior_sampler_options.slice_initialize_with_mode = options_list{i,2};
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case 'initial_step_size'
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% sets the initial size of the interval in the STEPPING-OUT PROCEDURE
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% the initial_step_size must be a real number in [0, 1],
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% and it sets the size as a proportion of the prior bounds,
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% i.e. the size will be initial_step_size*(UB-LB)
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% slice sampler requires prior_truncation > 0!
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% default = 0.8
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if options_list{i,2}<=0 || options_list{i,2}>=1
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error('check_posterior_sampler_options:: slice initial_step_size must be between 0 and 1')
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else
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posterior_sampler_options.initial_step_size=options_list{i,2};
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end
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case 'use_mh_covariance_matrix'
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% in association with <rotated> indicates to use the
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% covariance matrix from previous iterations to define the
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% rotated slice
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% default = 0
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posterior_sampler_options.use_mh_covariance_matrix = options_list{i,2};
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options_.use_mh_covariance_matrix = options_list{i,2};
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case 'save_tmp_file'
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posterior_sampler_options.save_tmp_file = options_list{i,2};
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otherwise
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warning(['slice_sampler: Unknown option (' options_list{i,1} ')!'])
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end
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end
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end
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% slice posterior sampler does not require mode or hessian to run
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% needs to be set to 1 to skip parts in dynare_estimation_1.m
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% requiring posterior maximization/calibrated smoother before MCMC
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options_.mh_posterior_mode_estimation=true;
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if ~ posterior_sampler_options.slice_initialize_with_mode
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% by default, slice sampler should trigger
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% mode_compute=0 and
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% mh_replic=100 (much smaller than the default mh_replic=20000 of RWMH)
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options_.mode_compute = 0;
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options_.cova_compute = 0;
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else
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if (isequal(options_.mode_compute,0) && isempty(options_.mode_file) )
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skipline()
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disp('check_posterior_sampler_options:: You have specified the option "slice_initialize_with_mode"')
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disp('check_posterior_sampler_options:: to initialize the slice sampler using mode information')
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disp('check_posterior_sampler_options:: but no mode file nor posterior maximization is selected,')
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error('check_posterior_sampler_options:: The option "slice_initialize_with_mode" is inconsistent with mode_compute=0 or empty mode_file.')
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else
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options_.mh_posterior_mode_estimation=false;
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end
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end
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if any(isinf(bounds.lb)) || any(isinf(bounds.ub))
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skipline()
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disp('some priors are unbounded and prior_trunc is set to zero')
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error('The option "slice" is inconsistent with prior_trunc=0.')
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end
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% moreover slice must be associated to:
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% options_.mh_posterior_mode_estimation = false;
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% this is done below, but perhaps preprocessing should do this?
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if ~isempty(posterior_sampler_options.mode)
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% multimodal case
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posterior_sampler_options.rotated = 1;
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posterior_sampler_options.WR=[];
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end
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% posterior_sampler_options = set_default_option(posterior_sampler_options,'mode_files',[]);
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posterior_sampler_options.W1=posterior_sampler_options.initial_step_size*(bounds.ub-bounds.lb);
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if options_.load_mh_file
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posterior_sampler_options.slice_initialize_with_mode = 0;
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else
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if ~posterior_sampler_options.slice_initialize_with_mode
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posterior_sampler_options.invhess=[];
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end
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end
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if ~isempty(posterior_sampler_options.mode_files) % multimodal case
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modes = posterior_sampler_options.mode_files; % these can be also mean files from previous parallel slice chains
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load(modes, 'xparams')
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if size(xparams,2)<2
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error(['check_posterior_sampler_options:: Variable xparams loaded in file <' modes '> has size [' int2str(size(xparams,1)) 'x' int2str(size(xparams,2)) ']: it must contain at least two columns, to allow multi-modal sampling.'])
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end
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for j=1:size(xparams,2)
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mode(j).m=xparams(:,j);
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end
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posterior_sampler_options.mode = mode;
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posterior_sampler_options.rotated = 1;
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posterior_sampler_options.WR=[];
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end
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case 'hssmc'
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% default options
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posterior_sampler_options = add_fields_(posterior_sampler_options, options_.posterior_sampler_options.hssmc);
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% user defined options
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if ~isempty(options_.posterior_sampler_options.sampling_opt)
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options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
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for i=1:rows(options_list)
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switch options_list{i,1}
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case 'target'
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posterior_sampler_options.target = options_list{i,2};
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case 'steps'
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posterior_sampler_options.steps = options_list{i,2};
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case 'scale'
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posterior_sampler_options.scale = options_list{i,2};
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case 'particles'
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posterior_sampler_options.particles = options_list{i,2};
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case 'lambda'
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posterior_sampler_options.lambda = options_list{i,2};
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otherwise
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warning(['hssmc: Unknown option (' options_list{i,1} ')!'])
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end
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end
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end
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options_.mode_compute = 0;
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options_.cova_compute = 0;
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options_.mh_replic = 0;
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options_.mh_posterior_mode_estimation = false;
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case 'dsmh'
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% default options
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posterior_sampler_options = add_fields_(posterior_sampler_options, options_.posterior_sampler_options.dsmh);
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% user defined options
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if ~isempty(options_.posterior_sampler_options.sampling_opt)
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options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
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for i=1:rows(options_list)
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switch options_list{i,1}
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case 'proposal_distribution'
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if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
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strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
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error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
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'rand_multivariate_student or rand_multivariate_normal as options']);
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else
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posterior_sampler_options.proposal_distribution=options_list{i,2};
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end
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case 'student_degrees_of_freedom'
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if options_list{i,2} <= 0
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error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
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else
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posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
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end
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case 'save_tmp_file'
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posterior_sampler_options.save_tmp_file = options_list{i,2};
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case 'number_of_particles'
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posterior_sampler_options.particles = options_list{i,2};
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otherwise
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warning(['rwmh_sampler: Unknown option (' options_list{i,1} ')!'])
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end
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end
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end
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options_.mode_compute = 0;
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options_.cova_compute = 0;
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options_.mh_replic = 0;
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options_.mh_posterior_mode_estimation = true;
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|
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otherwise
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error('check_posterior_sampler_options:: Unknown posterior_sampling_method option %s ',posterior_sampler_options.posterior_sampling_method);
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end
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|
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return
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end
|
|
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% here are all samplers requiring a proposal distribution
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if ~strcmp(posterior_sampler_options.posterior_sampling_method,'slice')
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if ~options_.cova_compute && ~(options_.load_mh_file && posterior_sampler_options.use_mh_covariance_matrix)
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if strcmp('hessian',options_.MCMC_jumping_covariance)
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|
skipline()
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disp('check_posterior_sampler_options:: I cannot start the MCMC because the Hessian of the posterior kernel at the mode was not computed')
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disp('check_posterior_sampler_options:: or there is no previous MCMC to load ')
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error('check_posterior_sampler_options:: MCMC cannot start')
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end
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|
end
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|
end
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|
|
|
if options_.load_mh_file && posterior_sampler_options.use_mh_covariance_matrix
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|
[~, invhess] = compute_mh_covariance_matrix(bayestopt_,fname,dname,outputFolderName);
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|
posterior_sampler_options.invhess = invhess;
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|
end
|
|
|
|
|
|
|
|
% check specific options for slice sampler
|
|
if strcmp(posterior_sampler_options.posterior_sampling_method,'slice')
|
|
invhess = posterior_sampler_options.invhess;
|
|
if posterior_sampler_options.rotated
|
|
if isempty(posterior_sampler_options.mode_files) && isempty(posterior_sampler_options.mode) % rotated unimodal
|
|
if ~options_.cova_compute && ~(options_.load_mh_file && posterior_sampler_options.use_mh_covariance_matrix)
|
|
skipline()
|
|
disp('check_posterior_sampler_options:: I cannot start rotated slice sampler because')
|
|
disp('check_posterior_sampler_options:: there is no previous MCMC to load ')
|
|
disp('check_posterior_sampler_options:: or the Hessian at the mode is not computed.')
|
|
error('check_posterior_sampler_options:: Rotated slice cannot start')
|
|
end
|
|
if isempty(invhess)
|
|
error('check_posterior_sampler_options:: This error should not occur, please contact developers.')
|
|
end
|
|
% % % if options_.load_mh_file && options_.use_mh_covariance_matrix,
|
|
% % % [~, invhess] = compute_mh_covariance_matrix(bayestopt_,M_.fname,M_.dname));
|
|
% % % posterior_sampler_options.invhess = invhess;
|
|
% % % end
|
|
[V1, D]=eig(invhess);
|
|
posterior_sampler_options.V1=V1;
|
|
posterior_sampler_options.WR=sqrt(diag(D))*3;
|
|
end
|
|
else
|
|
if ~options_.load_mh_file && ~posterior_sampler_options.slice_initialize_with_mode
|
|
posterior_sampler_options.invhess=[];
|
|
end
|
|
end
|
|
% needs to be re-set to zero otherwise posterior analysis is filtered
|
|
% out in dynare_estimation_1.m
|
|
options_.mh_posterior_mode_estimation = false;
|
|
end
|
|
|
|
return
|
|
|
|
function posterior_sampler_options = add_fields_(posterior_sampler_options, sampler_options)
|
|
|
|
fnam = fieldnames(sampler_options);
|
|
for j=1:length(fnam)
|
|
posterior_sampler_options.(fnam{j}) = sampler_options.(fnam{j});
|
|
end
|