function [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, fname, dname, options_, bounds, bayestopt_, outputFolderName)
% [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, fname, dname, options_, bounds, bayestopt_, outputFolderName)
% Initialization of posterior samplers
%
% INPUTS
% - posterior_sampler_options   [struct]       posterior sampler options
% - options_                    [struct]       options
% - dname                       [string]       directory name
% - fname                       [string]       file name
% - outputFolderName            [string]       folder name
% - bounds                      [struct]       prior bounds
% - bayestopt_                  [struct]       information about priors
%
% OUTPUTS
% - posterior_sampler_options   [struct]       checked posterior sampler options (updated)
% - options_                    [struct]       options (updated)
% - bayestopt_                  [struct]       information about priors (updated)
%
% SPECIAL REQUIREMENTS
%   none

% Copyright © 2015-2026 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.

if nargin < 7
    outputFolderName = 'Output';
end

init = false;
if isempty(posterior_sampler_options)
    init = true;
end

if init
    % set default options and user defined options
    posterior_sampler_options.posterior_sampling_method = options_.posterior_sampler_options.posterior_sampling_method;
    if ~issmc(options_)
        posterior_sampler_options.bounds = bounds;
    end

    switch posterior_sampler_options.posterior_sampling_method

      case 'random_walk_metropolis_hastings'
        posterior_sampler_options.parallel_bar_refresh_rate=50;
        posterior_sampler_options.serial_bar_refresh_rate=20;
        posterior_sampler_options.parallel_bar_title='RWMH';
        posterior_sampler_options.serial_bar_title='RW Metropolis-Hastings';

        % default options
        posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.rwmh);

        % user defined options
        if ~isempty(options_.posterior_sampler_options.sampling_opt)
            options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
            for i=1:rows(options_list)
                switch options_list{i,1}

                  case 'proposal_distribution'
                    if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
                         strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
                        error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
                               'rand_multivariate_student or rand_multivariate_normal as options']);
                    else
                        posterior_sampler_options.proposal_distribution=options_list{i,2};
                    end


                  case 'student_degrees_of_freedom'
                    if options_list{i,2} <= 0
                        error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
                    else
                        posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
                    end

                  case 'use_mh_covariance_matrix'
                    % indicates to use the covariance matrix from previous iterations to
                    % define the covariance of the proposal distribution
                    % default = 0
                    posterior_sampler_options.use_mh_covariance_matrix = options_list{i,2};
                    options_.use_mh_covariance_matrix = options_list{i,2};
                  case 'scale_file'
                    % load optimal_mh_scale parameter if previous run was with mode_compute=6
                    % will overwrite jscale from set_prior.m
                    if isfile(options_list{i,2}) || isfile([options_list{i,2},'.mat'])
                        tmp = load(options_list{i,2},'Scale');
                        bayestopt_.mh_jscale = tmp.Scale;
                        options_.mh_jscale = tmp.Scale;
                        bayestopt_.jscale = ones(size(bounds.lb,1),1)*tmp.Scale;
                        %                                 options_.mh_init_scale = 2*options_.mh_jscale;
                    else
                        error('initial_estimation_checks:: The specified mh_scale_file does not exist.')
                    end
                  case 'save_tmp_file'
                    posterior_sampler_options.save_tmp_file = options_list{i,2};
                  otherwise
                    warning(['rwmh_sampler: Unknown option (' options_list{i,1} ')!'])
                end
            end
        end

      case 'tailored_random_block_metropolis_hastings'
        posterior_sampler_options.parallel_bar_refresh_rate=50;
        posterior_sampler_options.serial_bar_refresh_rate=20;
        posterior_sampler_options.parallel_bar_title='TaRB-MH';
        posterior_sampler_options.serial_bar_title='TaRB Metropolis-Hastings';

        % default options
        posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.tarb);

        % user defined options
        if ~isempty(options_.posterior_sampler_options.sampling_opt)
            options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
            for i=1:rows(options_list)

                switch options_list{i,1}

                  case 'proposal_distribution'
                    if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
                         strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
                        error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
                               'rand_multivariate_student or rand_multivariate_normal as options']);
                    else
                        posterior_sampler_options.proposal_distribution=options_list{i,2};
                    end


                  case 'student_degrees_of_freedom'
                    if options_list{i,2} <= 0
                        error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
                    else
                        posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
                    end

                  case 'mode_compute'
                    posterior_sampler_options.mode_compute=options_list{i,2};

                  case 'optim'
                    posterior_sampler_options.optim_opt=options_list{i,2};

                  case 'new_block_probability'
                    if options_list{i,2}<0 || options_list{i,2}>1
                        error('check_posterior_sampler_options:: The tarb new_block_probability must be between 0 and 1!')
                    else
                        posterior_sampler_options.new_block_probability=options_list{i,2};
                    end
                  case 'scale_file'
                    % load optimal_mh_scale parameter if previous run was with mode_compute=6
                    % will overwrite jscale from set_prior.m
                    if isfile(options_list{i,2}) || isfile([options_list{i,2},'.mat'])
                        tmp = load(options_list{i,2},'Scale');
                        bayestopt_.mh_jscale = tmp.Scale;
                        options_.mh_jscale = tmp.Scale;
                        bayestopt_.jscale = ones(size(bounds.lb,1),1)*tmp.Scale;
                        %                                 options_.mh_init_scale = 2*options_.mh_jscale;
                    else
                        error('initial_estimation_checks:: The specified scale_file does not exist.')
                    end
                  case 'save_tmp_file'
                    posterior_sampler_options.save_tmp_file = options_list{i,2};

                  otherwise
                    warning(['tarb_sampler: Unknown option (' options_list{i,1} ')!'])

                end

            end

        end

      case 'independent_metropolis_hastings'
        posterior_sampler_options.parallel_bar_refresh_rate=50;
        posterior_sampler_options.serial_bar_refresh_rate=10;
        posterior_sampler_options.parallel_bar_title='IMH';
        posterior_sampler_options.serial_bar_title='Ind. Metropolis-Hastings';

        % default options
        posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.imh);

        % user defined options
        if ~isempty(options_.posterior_sampler_options.sampling_opt)
            options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
            for i=1:rows(options_list)
                switch options_list{i,1}

                  case 'proposal_distribution'
                    if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
                         strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
                        error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
                               'rand_multivariate_student or rand_multivariate_normal as options']);
                    else
                        posterior_sampler_options.proposal_distribution=options_list{i,2};
                    end


                  case 'student_degrees_of_freedom'
                    if options_list{i,2} <= 0
                        error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
                    else
                        posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
                    end

                  case 'use_mh_covariance_matrix'
                    % indicates to use the covariance matrix from previous iterations to
                    % define the covariance of the proposal distribution
                    % default = 0
                    posterior_sampler_options.use_mh_covariance_matrix = options_list{i,2};
                    options_.use_mh_covariance_matrix = options_list{i,2};

                  case 'save_tmp_file'
                    posterior_sampler_options.save_tmp_file = options_list{i,2};

                  otherwise
                    warning(['imh_sampler: Unknown option (' options_list{i,1} ')!'])
                end
            end
        end

      case 'slice'
        posterior_sampler_options.parallel_bar_refresh_rate=1;
        posterior_sampler_options.serial_bar_refresh_rate=1;
        posterior_sampler_options.parallel_bar_title='SLICE';
        posterior_sampler_options.serial_bar_title='SLICE';

        % default options
        posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.slice);

        % user defined options
        if ~isempty(options_.posterior_sampler_options.sampling_opt)
            options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
            for i=1:rows(options_list)
                switch options_list{i,1}
                  case 'rotated'
                    % triggers rotated slice iterations using a covariance
                    % matrix from initial burn-in iterations
                    % must be associated with:
                    % <use_mh_covariance_matrix> or <slice_initialize_with_mode>
                    % default  = 0
                    posterior_sampler_options.rotated = options_list{i,2};

                  case 'mode'
                    % for multimodal posteriors, provide the list of modes as a
                    % matrix, ordered by column, i.e. [x1 x2 x3] for three
                    % modes x1 x2 x3
                    % MR note: not sure this is possible with the
                    % read_key_value_string ???
                    % if this is not possible <mode_files> does to job in any case
                    % This will automatically trigger <rotated>
                    % default = []
                    tmp_mode = options_list{i,2};
                    for j=1:size(tmp_mode,2)
                        posterior_sampler_options.mode(j).m = tmp_mode(:,j);
                    end

                  case 'mode_files'
                    % for multimodal posteriors provide the name of
                    % a file containing a variable array xparams = [nparam * nmodes]
                    % one column per mode. With this info, the code will automatically
                    % set the <mode> option.
                    % 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
                    if options_list{i,2}<=0 || options_list{i,2}>=1
                        error('check_posterior_sampler_options:: slice initial_step_size must be between 0 and 1')
                    else
                        posterior_sampler_options.initial_step_size=options_list{i,2};
                    end
                  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};
                    options_.use_mh_covariance_matrix = options_list{i,2};

                  case 'save_tmp_file'
                    posterior_sampler_options.save_tmp_file = options_list{i,2};

                  case 'save_iter_info_file'
                    posterior_sampler_options.save_iter_info_file = options_list{i,2};

                  case 'fast_likelihood_evaluation_for_rejection'

                      posterior_sampler_options.fast_likelihood_evaluation_for_rejection = options_list{i,2};

                  case 'fast_likelihood_evaluation_for_rejection_penalty'

                      posterior_sampler_options.fast_likelihood_evaluation_for_rejection_penalty = options_list{i,2};

                   case 'use_prior_draws'

                       posterior_sampler_options.use_prior_draws.status = options_list{i,2};
                       posterior_sampler_options.use_prior_draws.mh_blck = repmat(options_list{i,2},[1 options_.mh_nblck]);



                  case 'draw_init_state_from_smoother'
                    posterior_sampler_options.draw_init_state_from_smoother = options_list{i,2};

                  case 'draw_init_state_with_rotated_slice'
                    posterior_sampler_options.draw_init_state_with_rotated_slice = options_list{i,2};

                  case 'maximize'
                    posterior_sampler_options.maximize = options_list{i,2};

                  case 'maximize_using_mh_bounds'

                      posterior_sampler_options.maximize_using_mh_bounds = options_list{i,2};

                  case 'mode_compute'
                    posterior_sampler_options.mode_compute = options_list{i,2};

                  otherwise
                    warning(['slice_sampler: Unknown option (' options_list{i,1} ')!'])
                end
            end
        end

        % check if we estimate init state with endogenous prior
        if isfield(options_, 'estimate_initial_states_endogenous_prior') && options_.estimate_initial_states_endogenous_prior
            if ~posterior_sampler_options.draw_init_state_with_rotated_slice && ~posterior_sampler_options.draw_init_state_from_smoother
                % this is the default to be used in this case, that
                % overrides the one set in occbin.set_default_options
                posterior_sampler_options.draw_init_state_from_smoother=true;
            end
        end
        % slice posterior sampler does not require mode or Hessian to run
        % needs to be set to 1 to skip parts in dynare_estimation_1.m or mom.run.m
        % requiring posterior maximization/calibrated smoother before MCMC
        options_.mh_posterior_mode_estimation=true;

        if ~ posterior_sampler_options.slice_initialize_with_mode
            % by default, slice sampler should trigger
            % mode_compute=0 and
            % mh_replic=100 (much smaller than the default mh_replic=20000 of RWMH)
            options_.mode_compute = 0;
            options_.cova_compute = 0;
        else
            if (isequal(options_.mode_compute,0) && isempty(options_.mode_file) )
                skipline()
                disp('check_posterior_sampler_options:: You have specified the option "slice_initialize_with_mode"')
                disp('check_posterior_sampler_options:: to initialize the slice sampler using mode information')
                disp('check_posterior_sampler_options:: but no mode file nor posterior maximization is selected,')
                error('check_posterior_sampler_options:: The option "slice_initialize_with_mode" is inconsistent with mode_compute=0 or empty mode_file.')
            else
                options_.mh_posterior_mode_estimation=false;
            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

        % moreover slice must be associated to:
            %     options_.mh_posterior_mode_estimation = false;
            % this is done below, but perhaps preprocessing should do this?

            if ~isempty(posterior_sampler_options.mode)
                % multimodal case
                posterior_sampler_options.rotated = 1;
                posterior_sampler_options.WR=[];
            end
            %     posterior_sampler_options = set_default_option(posterior_sampler_options,'mode_files',[]);


            posterior_sampler_options.W1=posterior_sampler_options.initial_step_size*(bounds.ub-bounds.lb);
            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 ~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
                load(modes, 'xparams')
                if size(xparams,2)<2
                    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.'])
                end
                for j=1:size(xparams,2)
                    mode(j).m=xparams(:,j);
                end
                posterior_sampler_options.mode = mode;
                posterior_sampler_options.rotated = 1;
                posterior_sampler_options.WR=[];
            end

      case 'hssmc'

          % default options
          posterior_sampler_options = add_fields_(posterior_sampler_options, options_.posterior_sampler_options.hssmc);

          % user defined options
          if ~isempty(options_.posterior_sampler_options.sampling_opt)
              options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
              for i=1:rows(options_list)
                  switch options_list{i,1}
                      case 'target'
                          posterior_sampler_options.target = options_list{i,2};
                      case 'steps'
                          posterior_sampler_options.steps = options_list{i,2};
                      case 'scale'
                          posterior_sampler_options.scale = options_list{i,2};
                      case 'particles'
                          posterior_sampler_options.particles = options_list{i,2};
                      case 'lambda'
                          posterior_sampler_options.lambda = options_list{i,2};
                      otherwise
                          warning(['hssmc: Unknown option (' options_list{i,1} ')!'])
                  end
              end
          end

          options_.mode_compute = 0;
          options_.cova_compute = 0;
          options_.mh_replic = 0;
          options_.mh_posterior_mode_estimation = false;

      case 'dime_mcmc'

          % default options
          posterior_sampler_options = add_fields_(posterior_sampler_options, options_.posterior_sampler_options.dime);

          if ~isempty(options_.posterior_sampler_options.sampling_opt)
              options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
              for i=1:rows(options_list)
                  switch options_list{i,1}
                      case 'nchain'
                          posterior_sampler_options.nchain = options_list{i,2};
                      case 'niter'
                          posterior_sampler_options.niter = options_list{i,2};
                      case 'tune'
                          posterior_sampler_options.tune = options_list{i,2};
                      case 'aimh_prob'
                          posterior_sampler_options.aimh_prob = options_list{i,2};
                      case 'sigma'
                          posterior_sampler_options.sigma = options_list{i,2};
                      case 'gamma'
                          posterior_sampler_options.gamma = options_list{i,2};
                      case 'df_proposal_dist'
                          posterior_sampler_options.df_proposal_dist = options_list{i,2};
                      case 'rho'
                          posterior_sampler_options.rho = options_list{i,2};
                      otherwise
                          warning(['dime: Unknown option (' options_list{i,1} ')!'])
                  end
              end
          end

          if ~isfield(posterior_sampler_options,'tune')
              posterior_sampler_options.tune = ceil(50000 / posterior_sampler_options.nchain);
              dprintf('dime: setting number of ensemble iterations to keep (`tune`) to %d.', posterior_sampler_options.tune)
          end

          if posterior_sampler_options.nchain < 6
              error('check_posterior_sampler_options:: DIME requires nchain to be at least 6 (got %d). The algorithm needs at least 2 chains per half-ensemble for differential evolution proposals.', posterior_sampler_options.nchain);
          end

      case 'online'

          % default options
          posterior_sampler_options = add_fields_(posterior_sampler_options, options_.posterior_sampler_options.online);

          % user defined options
          if ~isempty(options_.posterior_sampler_options.sampling_opt)
              options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
              for i=1:rows(options_list)
                  switch options_list{i,1}
                      case 'particles'
                          posterior_sampler_options.particles = options_list{i,2};
                      case 'liu_west_delta'
                          posterior_sampler_options.liu_west_delta = options_list{i,2};
                      case 'liu_west_max_resampling_tries'
                          posterior_sampler_options.liu_west_max_resampling_tries = options_list{i,2};
                      case 'second_resampling'
                          posterior_sampler_options.second_resampling = options_list{i,2};
                      otherwise
                          warning(['online: Unknown option (' options_list{i,1} ')!'])
                  end
              end
          end

          options_.mode_compute = 0;
          options_.cova_compute = 0;
          options_.mh_replic = 0;
          options_.mh_posterior_mode_estimation = false;

      case 'dsmh'

          % default options
          posterior_sampler_options = add_fields_(posterior_sampler_options, options_.posterior_sampler_options.dsmh);

          % user defined options
          if ~isempty(options_.posterior_sampler_options.sampling_opt)
              options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
              for i=1:rows(options_list)
                  switch options_list{i,1}
                      case 'H'
                          posterior_sampler_options.H = options_list{i,2};
                      case 'M'
                          posterior_sampler_options.M = options_list{i,2};
                      case 'N'
                          posterior_sampler_options.N = options_list{i,2};
                      case 'G'
                          posterior_sampler_options.G = options_list{i,2};
                      case 'K'
                          posterior_sampler_options.K = options_list{i,2};
                      case 'tau'
                          posterior_sampler_options.tau = options_list{i,2};
                      case 'alpha0'
                          posterior_sampler_options.alpha0 = options_list{i,2};
                      case 'alpha1'
                          posterior_sampler_options.alpha1 = options_list{i,2};
                      otherwise
                          warning(['dsmh: Unknown option (' options_list{i,1} ')!'])
                  end
              end
          end

          if mod(posterior_sampler_options.N*posterior_sampler_options.G, posterior_sampler_options.M) ~= 0
              error('check_posterior_sampler_options:: DSMH requires M to divide N*G = %u evenly.', posterior_sampler_options.N*posterior_sampler_options.G);
          end
          options_.mode_compute = 0;
          options_.cova_compute = 0;
          options_.mh_replic = 0;
          options_.mh_posterior_mode_estimation = false;

      otherwise
          error('check_posterior_sampler_options:: Unknown posterior_sampling_method option %s ',posterior_sampler_options.posterior_sampling_method);
    end

    return

end % if init

% here are all samplers requiring a proposal distribution
if ~strcmp(posterior_sampler_options.posterior_sampling_method,'slice')
    if ~options_.cova_compute && ~(options_.load_mh_file && posterior_sampler_options.use_mh_covariance_matrix)
        if strcmp('hessian',options_.MCMC_jumping_covariance)
        skipline()
        disp('check_posterior_sampler_options:: I cannot start the MCMC because the Hessian of the posterior kernel at the mode was not computed')
        disp('check_posterior_sampler_options:: or there is no previous MCMC to load ')
        error('check_posterior_sampler_options:: MCMC cannot start')
        end
    end
end

if options_.load_mh_file && posterior_sampler_options.use_mh_covariance_matrix
    [~, invhess] = compute_posterior_covariance_matrix(bayestopt_.name, fname, dname, options_, outputFolderName);
    posterior_sampler_options.invhess = invhess;
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
            [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
    if posterior_sampler_options.maximize_using_mh_bounds
        if ~posterior_sampler_options.maximize
            error('check_posterior_sampler_options:: the maximize_using_mh_bounds requires the maximize option.')
        end
        if ~options_.load_mh_file
            error('check_posterior_sampler_options:: the maximize_using_mh_bounds requires the load_mh_file option.')
        end
        if options_.mh_recover
            error('check_posterior_sampler_options:: the maximize_using_mh_bounds is incompatible with the mh_recover option.')
        end
    end
    if posterior_sampler_options.maximize && posterior_sampler_options.maximize_using_mh_bounds && options_.load_mh_file && not(options_.mh_recover)
        % restrict maximization within slice in the range of mcmc posterior draws

        params1 = GetAllPosteriorDraws(options_, dname, fname, 'all');
        opt_bounds.lb=min(params1)';
        opt_bounds.ub=max(params1)';
        opt_bounds.lb=max(min(opt_bounds.lb.*0.99,opt_bounds.lb-1.e-6),0.5*(opt_bounds.lb+posterior_sampler_options.bounds.lb));
        opt_bounds.ub=min(max(opt_bounds.ub.*1.01,opt_bounds.ub+1.e-6),0.5*(opt_bounds.ub+posterior_sampler_options.bounds.ub));
        posterior_sampler_options.bounds = opt_bounds;
    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
