provisions for reworked posterior sampling options:
- handle sub lists of individual samplers - split checks in dynare_estimation_init.m and before running posterior_sampler.m [invhess checks] - posterior sampler options checks moved from initial_estimation_checks.m to check_posterior_sampler_options.m - added use_mh_covariance_matrix to imh and rwmh - slice re-sets mode_compute=0 cova_compute=0 - updated test functiontime-shift
Marco Ratto
Johannes Pfeifer
parent
8b85ca19bf
commit
7b3c42c6e1
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@ -1,6 +1,6 @@
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function [posterior_sampler_options, options_] = check_posterior_sampler_options(posterior_sampler_options, options_)
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function [posterior_sampler_options, options_] = check_posterior_sampler_options(posterior_sampler_options, options_, bounds)
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% function posterior_sampler_options = check_posterior_sampler_options(posterior_sampler_options, options_)
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% function [posterior_sampler_options, options_] = check_posterior_sampler_options(posterior_sampler_options, options_, bounds)
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% initialization of posterior samplers
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%
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% INPUTS
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@ -30,163 +30,375 @@ function [posterior_sampler_options, options_] = check_posterior_sampler_options
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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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posterior_sampler_options.posterior_sampling_method = options_.posterior_sampling_method;
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posterior_sampler_options.proposal_distribution = options_.proposal_distribution;
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bounds = posterior_sampler_options.bounds;
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invhess = posterior_sampler_options.invhess;
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init=0;
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if isempty(posterior_sampler_options),
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init=1;
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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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posterior_sampler_options.bounds = bounds;
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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=3;
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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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posterior_sampler_options.save_tmp_file=1;
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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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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 'tailored_random_block_metropolis_hastings'
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posterior_sampler_options.parallel_bar_refresh_rate=5;
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posterior_sampler_options.serial_bar_refresh_rate=1;
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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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posterior_sampler_options.save_tmp_file=1;
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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 '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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otherwise
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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=3;
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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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posterior_sampler_options.save_tmp_file=1;
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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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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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posterior_sampler_options.save_tmp_file=1;
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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 a list of mode files,
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% one per mode. With this info, the code will automatically
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% set the <mode> option. The mode files need only to
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% contain the xparam1 variable.
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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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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=1;
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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=0;
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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 = 0;
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% this is done below, but perhaps preprocessing should do this?
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if ~isfield(posterior_sampler_options,'mode'),
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posterior_sampler_options.mode = [];
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else % multimodal case
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posterior_sampler_options.rotated = 1;
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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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for j=1:length( modes ),
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load(modes{j}, 'xparam1')
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mode(j).m=xparam1;
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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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otherwise
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end
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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
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error('I Cannot start the MCMC because the Hessian of the posterior kernel at the mode was not computed.')
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if ~options_.cova_compute && ~(options_.load_mh_file && posterior_sampler_options.use_mh_covariance_matrix)
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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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if options_.load_mh_file && options_.use_mh_covariance_matrix,
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[junk, invhess] = compute_mh_covariance_matrix;
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posterior_sampler_options.invhess = invhess;
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end
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posterior_sampler_options.parallel_bar_refresh_rate=50;
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posterior_sampler_options.serial_bar_refresh_rate=3;
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posterior_sampler_options.parallel_bar_title='MH';
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posterior_sampler_options.serial_bar_title='Metropolis-Hastings';
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posterior_sampler_options.save_tmp_file=1;
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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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[junk, invhess] = compute_mh_covariance_matrix;
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posterior_sampler_options.invhess = invhess;
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end
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% check specific options for slice sampler
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if strcmp(options_.posterior_sampling_method,'slice')
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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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% moreover slice must be associated to:
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% options_.mh_posterior_mode_estimation = 0;
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% this is done below, but perhaps preprocessing should do this?
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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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posterior_sampler_options.save_tmp_file=1;
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posterior_sampler_options = set_default_option(posterior_sampler_options,'rotated',0);
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posterior_sampler_options = set_default_option(posterior_sampler_options,'slice_initialize_with_mode',0);
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posterior_sampler_options = set_default_option(posterior_sampler_options,'use_mh_covariance_matrix',0);
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posterior_sampler_options = set_default_option(posterior_sampler_options,'WR',[]);
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if ~isfield(posterior_sampler_options,'mode'),
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posterior_sampler_options.mode = [];
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else % multimodal case
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posterior_sampler_options.rotated = 1;
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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 = set_default_option(posterior_sampler_options,'W1',0.8*(bounds.ub-bounds.lb));
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if ~isempty(options_.optim_opt)
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options_list = read_key_value_string(options_.optim_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'
|
||||
% for multimodal posteriors provide a list of mode files,
|
||||
% one per mode. With this info, the code will automatically
|
||||
% set the <mode> option. The mode files need only to
|
||||
% contain the xparam1 variable.
|
||||
% This will automatically trigger <rotated>
|
||||
% default = []
|
||||
posterior_sampler_options.mode_files = options_list{i,2};
|
||||
|
||||
case 'slice_initialize_with_mode'
|
||||
% the default for slice is to set mode_compute = 0 in the
|
||||
% preprocessor and start the chain(s) from a random location in the prior.
|
||||
% This option first runs the optimizer and then starts the
|
||||
% chain from the mode. Associated with optios <rotated>, it will
|
||||
% use invhess from the mode to perform rotated slice
|
||||
% iterations.
|
||||
% default = 0
|
||||
posterior_sampler_options.slice_initialize_with_mode = options_list{i,2};
|
||||
|
||||
case 'initial_step_size'
|
||||
% sets the initial size of the interval in the STEPPING-OUT PROCEDURE
|
||||
% the initial_step_size must be a real number in [0, 1],
|
||||
% and it sets the size as a proportion of the prior bounds,
|
||||
% i.e. the size will be initial_step_size*(UB-LB)
|
||||
% slice sampler requires prior_truncation > 0!
|
||||
% default = 0.8
|
||||
posterior_sampler_options.W1 = options_list{i,2}*(bounds.ub-bounds.lb);
|
||||
|
||||
case 'use_mh_covariance_matrix'
|
||||
% in association with <rotated> indicates to use the
|
||||
% covariance matrix from previous iterations to define the
|
||||
% rotated slice
|
||||
% default = 0
|
||||
posterior_sampler_options.use_mh_covariance_matrix = options_list{i,2};
|
||||
|
||||
otherwise
|
||||
warning(['slice_sampler: Unknown option (' options_list{i,1} ')!'])
|
||||
end
|
||||
end
|
||||
end
|
||||
if options_.load_mh_file,
|
||||
posterior_sampler_options.slice_initialize_with_mode = 0;
|
||||
else
|
||||
if ~posterior_sampler_options.slice_initialize_with_mode,
|
||||
posterior_sampler_options.invhess=[];
|
||||
end
|
||||
end
|
||||
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('I cannot start rotated slice sampler because')
|
||||
disp('there is no previous MCMC to load ')
|
||||
disp('or the Hessian at the mode is not computed.')
|
||||
error('Rotated slice cannot start')
|
||||
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('oops! This error should not occur, please contact developers.')
|
||||
end
|
||||
if options_.load_mh_file && posterior_sampler_options.use_mh_covariance_matrix,
|
||||
[junk, invhess] = compute_mh_covariance_matrix;
|
||||
posterior_sampler_options.invhess = 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,
|
||||
% % % [junk, invhess] = compute_mh_covariance_matrix;
|
||||
% % % posterior_sampler_options.invhess = invhess;
|
||||
% % % end
|
||||
[V1 D]=eig(invhess);
|
||||
posterior_sampler_options.V1=V1;
|
||||
posterior_sampler_options.WR=sqrt(diag(D))*3;
|
||||
end
|
||||
end
|
||||
|
||||
if ~isempty(posterior_sampler_options.mode_files), % multimodal case
|
||||
modes = posterior_sampler_options.mode_files; % these can be also mean files from previous parallel slice chains
|
||||
for j=1:length( modes ),
|
||||
load(modes{j}, 'xparam1')
|
||||
mode(j).m=xparam1;
|
||||
end
|
||||
posterior_sampler_options.mode = mode;
|
||||
posterior_sampler_options.rotated = 1;
|
||||
posterior_sampler_options.WR=[];
|
||||
end
|
||||
% needs to be re-set to zero otherwise posterior analysis is filtered
|
||||
% out in dynare_estimation_1.m
|
||||
options_.mh_posterior_mode_estimation = 0;
|
||||
|
||||
|
||||
else
|
||||
|
||||
|
||||
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
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -420,15 +420,16 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
|
|||
if options_.mh_replic
|
||||
ana_deriv_old = options_.analytic_derivation;
|
||||
options_.analytic_derivation = 0;
|
||||
posterior_sampler_options = options_.posterior_sampler_options;
|
||||
posterior_sampler_options.bounds = bounds;
|
||||
posterior_sampler_options = options_.posterior_sampler_options.current_options;
|
||||
posterior_sampler_options.invhess = invhess;
|
||||
[posterior_sampler_options, options_] = check_posterior_sampler_options(posterior_sampler_options, options_);
|
||||
if strcmpi(options_.posterior_sampling_method,'adaptive_metropolis_hastings'), % keep old form only for this ...
|
||||
% store current options in global
|
||||
options_.posterior_sampler_options.current_options = posterior_sampler_options;
|
||||
if strcmpi(options_.posterior_sampler_options.posterior_sampling_method,'adaptive_metropolis_hastings'), % keep old form only for this ...
|
||||
invhess = posterior_sampler_options.invhess;
|
||||
feval(options_.posterior_sampling_method,objective_function,options_.proposal_distribution,xparam1,invhess,bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
|
||||
else
|
||||
posterior_sampler(objective_function,options_.proposal_distribution,xparam1,posterior_sampler_options,bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
|
||||
posterior_sampler(objective_function,posterior_sampler_options.proposal_distribution,xparam1,posterior_sampler_options,bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
|
||||
end
|
||||
options_.analytic_derivation = ana_deriv_old;
|
||||
end
|
||||
|
|
|
|||
|
|
@ -591,28 +591,7 @@ else
|
|||
end
|
||||
|
||||
|
||||
% slice posterior sampler does not require mode or hessian to run
|
||||
if strcmp(options_.posterior_sampling_method,'slice')
|
||||
options_.mh_posterior_mode_estimation=1;
|
||||
options_.posterior_sampler_options = set_default_option(options_.posterior_sampler_options,'slice_initialize_with_mode',0);
|
||||
|
||||
if options_.posterior_sampler_options.slice_initialize_with_mode
|
||||
if (isequal(options_.mode_compute,0) && isempty(options_.mode_file) )
|
||||
skipline()
|
||||
disp('You have specified the option "slice_initialize_with_mode"')
|
||||
disp('to initialize the slice sampler using mode information')
|
||||
disp('but no mode file nor posterior maximization is selected,')
|
||||
error('The option "slice_initialize_with_mode" is inconsistent with mode_compute=0 or empty mode_file.')
|
||||
else
|
||||
options_.mh_posterior_mode_estimation=0;
|
||||
end
|
||||
end
|
||||
|
||||
if any(isinf(bounds.lb)) || any(isinf(bounds.ub)),
|
||||
skipline()
|
||||
disp('some priors are unbounded and prior_trunc is set to zero')
|
||||
error('The option "slice" is inconsistent with prior_trunc=0.')
|
||||
end
|
||||
|
||||
if options_.mh_replic
|
||||
[current_options, options_] = check_posterior_sampler_options([], options_, bounds);
|
||||
options_.posterior_sampler_options.current_options = current_options;
|
||||
end
|
||||
|
||||
|
|
|
|||
|
|
@ -435,11 +435,6 @@ options_.MCMC_jumping_covariance='hessian';
|
|||
options_.use_calibration_initialization = 0;
|
||||
options_.endo_vars_for_moment_computations_in_estimation=[];
|
||||
|
||||
% Tailored Random Block Metropolis-Hastings
|
||||
options_.TaRB.use_TaRB = 0;
|
||||
options_.TaRB.mode_compute=4;
|
||||
options_.TaRB.new_block_probability=0.25; %probability that next parameter belongs to new block
|
||||
|
||||
% Run optimizer silently
|
||||
options_.silent_optimizer=0;
|
||||
|
||||
|
|
@ -460,15 +455,32 @@ options_.prior_trunc = 1e-10;
|
|||
options_.smoother = 0;
|
||||
options_.posterior_max_subsample_draws = 1200;
|
||||
options_.sub_draws = [];
|
||||
options_.use_mh_covariance_matrix = 0;
|
||||
% options_.use_mh_covariance_matrix = 0;
|
||||
options_.gradient_method = 2; %used by csminwel and newrat
|
||||
options_.gradient_epsilon = 1e-6; %used by csminwel and newrat
|
||||
options_.posterior_sampler_options = []; %extended set of options for individual posterior samplers
|
||||
options_.posterior_sampler_options.sampling_opt = []; %extended set of options for individual posterior samplers
|
||||
% Random Walk Metropolis-Hastings
|
||||
options_.posterior_sampler_options.posterior_sampling_method = 'random_walk_metropolis_hastings';
|
||||
options_.posterior_sampler_options.rwmh.proposal_distribution = 'rand_multivariate_normal';
|
||||
options_.posterior_sampler_options.rwmh.student_degrees_of_freedom = 3;
|
||||
options_.posterior_sampler_options.rwmh.use_mh_covariance_matrix=0;
|
||||
% Tailored Random Block Metropolis-Hastings
|
||||
options_.posterior_sampler_options.tarb.proposal_distribution = 'rand_multivariate_normal';
|
||||
options_.posterior_sampler_options.tarb.student_degrees_of_freedom = 3;
|
||||
options_.posterior_sampler_options.tarb.mode_compute=4;
|
||||
options_.posterior_sampler_options.tarb.new_block_probability=0.25; %probability that next parameter belongs to new block
|
||||
% Slice
|
||||
options_.posterior_sampler_options.slice.proposal_distribution = '';
|
||||
options_.posterior_sampler_options.slice.rotated=0;
|
||||
options_.posterior_sampler_options.slice.slice_initialize_with_mode=0;
|
||||
options_.posterior_sampler_options.slice.use_mh_covariance_matrix=0;
|
||||
options_.posterior_sampler_options.slice.WR=[];
|
||||
options_.posterior_sampler_options.slice.mode_files=[];
|
||||
options_.posterior_sampler_options.slice.initial_step_size=0.8;
|
||||
% Independent Metropolis-Hastings
|
||||
options_.posterior_sampler_options.imh.proposal_distribution = 'rand_multivariate_normal';
|
||||
options_.posterior_sampler_options.imh.use_mh_covariance_matrix=0;
|
||||
|
||||
options_.trace_plot_ma = 200;
|
||||
options_.mh_autocorrelation_function_size = 30;
|
||||
options_.plot_priors = 1;
|
||||
|
|
|
|||
|
|
@ -53,20 +53,6 @@ if maximum_number_non_missing_observations>length(find(diag(Model.Sigma_e)))+Est
|
|||
error(['initial_estimation_checks:: Estimation can''t take place because too many shocks have been calibrated with a zero variance!'])
|
||||
end
|
||||
|
||||
if ~(strcmpi(DynareOptions.proposal_distribution, 'rand_multivariate_student') || ...
|
||||
strcmpi(DynareOptions.proposal_distribution, 'rand_multivariate_normal'))
|
||||
error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
|
||||
'rand_multivariate_student or rand_multivariate_normal as options']);
|
||||
end
|
||||
|
||||
if DynareOptions.student_degrees_of_freedom <= 0
|
||||
error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
|
||||
end
|
||||
|
||||
if DynareOptions.TaRB.use_TaRB && (DynareOptions.TaRB.new_block_probability<0 || DynareOptions.TaRB.new_block_probability>1)
|
||||
error(['initial_estimation_checks:: The tarb_new_block_probability must be between 0 and 1!'])
|
||||
end
|
||||
|
||||
if (any(BayesInfo.pshape >0 ) && DynareOptions.mh_replic) && DynareOptions.mh_nblck<1
|
||||
error(['initial_estimation_checks:: Bayesian estimation cannot be conducted with mh_nblocks=0.'])
|
||||
end
|
||||
|
|
|
|||
|
|
@ -89,11 +89,6 @@ load_last_mh_history_file(MetropolisFolder, ModelName);
|
|||
% on many cores). The mandatory variables for local/remote parallel
|
||||
% computing are stored in the localVars struct.
|
||||
|
||||
if options_.TaRB.use_TaRB
|
||||
options_.silent_optimizer=1; %locally set optimizer to silent mode
|
||||
sampler_options.posterior_sampling_method='tailored_random_block_metropolis_hastings';
|
||||
end
|
||||
|
||||
localVars = struct('TargetFun', TargetFun, ...
|
||||
'ProposalFun', ProposalFun, ...
|
||||
'xparam1', xparam1, ...
|
||||
|
|
@ -120,6 +115,9 @@ localVars = struct('TargetFun', TargetFun, ...
|
|||
'oo_', oo_,...
|
||||
'varargin',[]);
|
||||
|
||||
if strcmp(sampler_options.posterior_sampling_method,'tailored_random_block_metropolis_hastings');
|
||||
localVars.options_.silent_optimizer=1; %locally set optimizer to silent mode
|
||||
end
|
||||
|
||||
% User doesn't want to use parallel computing, or wants to compute a
|
||||
% single chain compute Random walk Metropolis-Hastings algorithm sequentially.
|
||||
|
|
@ -145,11 +143,7 @@ else
|
|||
end
|
||||
% from where to get back results
|
||||
% NamFileOutput(1,:) = {[M_.dname,'/metropolis/'],'*.*'};
|
||||
if options_.TaRB.use_TaRB
|
||||
[fout, nBlockPerCPU, totCPU] = masterParallel(options_.parallel, fblck, nblck,NamFileInput,'TaRB_metropolis_hastings_core', localVars, globalVars, options_.parallel_info);
|
||||
else
|
||||
[fout, nBlockPerCPU, totCPU] = masterParallel(options_.parallel, fblck, nblck,NamFileInput,'posterior_sampler_core', localVars, globalVars, options_.parallel_info);
|
||||
end
|
||||
[fout, nBlockPerCPU, totCPU] = masterParallel(options_.parallel, fblck, nblck,NamFileInput,'posterior_sampler_core', localVars, globalVars, options_.parallel_info);
|
||||
for j=1:totCPU,
|
||||
offset = sum(nBlockPerCPU(1:j-1))+fblck-1;
|
||||
record.LastLogPost(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.LastLogPost(offset+1:sum(nBlockPerCPU(1:j)));
|
||||
|
|
|
|||
|
|
@ -202,7 +202,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
|
|||
fprintf(['Estimation::mcmc: Write details about the MCMC... ']);
|
||||
AnticipatedNumberOfFiles = ceil(nruns(1)/MAX_nruns);
|
||||
AnticipatedNumberOfLinesInTheLastFile = nruns(1) - (AnticipatedNumberOfFiles-1)*MAX_nruns;
|
||||
record.Sampler = options_.posterior_sampling_method;
|
||||
record.Sampler = options_.posterior_sampler_options.posterior_sampling_method;
|
||||
record.Nblck = NumberOfBlocks;
|
||||
record.MhDraws = zeros(1,3);
|
||||
record.MhDraws(1,1) = nruns(1);
|
||||
|
|
|
|||
|
|
@ -70,7 +70,12 @@ end;
|
|||
|
||||
varobs gp_obs gy_obs;
|
||||
|
||||
options_.posterior_sampling_method = 'slice';
|
||||
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=50,mh_nblocks=2,mh_drop=0.2,mode_compute=0,cova_compute=0);
|
||||
//options_.posterior_sampling_method = 'slice';
|
||||
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=50,mh_nblocks=2,mh_drop=0.2, //mode_compute=0,cova_compute=0,
|
||||
posterior_sampling_method='slice'
|
||||
);
|
||||
// continue with rotated slice
|
||||
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=100,mh_nblocks=2,mh_drop=0.5,load_mh_file,mode_compute=0,optim=('rotated',1,'use_mh_covariance_matrix',1));
|
||||
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=100,mh_nblocks=2,mh_drop=0.5,load_mh_file,//mode_compute=0,
|
||||
posterior_sampling_method='slice',
|
||||
posterior_sampler_options=('rotated',1,'use_mh_covariance_matrix',1)
|
||||
);
|
||||
|
|
|
|||
Loading…
Reference in New Issue