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function [particles, tlogpostkernel, loglikelihood, SimulationFolder] = smc_samplers_initialization(funobj, sampler, n, Prior, SimulationFolder, nsteps)
% Initialize SMC samplers by drawing initial particles in the prior distribution.
%
% INPUTS
% - TargetFun [char] string specifying the name of the objective function (posterior kernel).
% - sampler [char] name of the sampler.
% - n [integer] scalar, number of particles.
% - mh_bounds [double] p×2 matrix defining lower and upper bounds for the estimated parameters.
% - dataset_ [dseries] sample
% - dataset_info [struct] informations about the dataset
% - options_ [struct] dynare's options
% - M_ [struct] model description
% - estim_params_ [struct] estimated parameters
% - bayestopt_ [struct] estimated parameters
% - oo_ [struct] outputs
%
% OUTPUTS
% - ix2 [double] p×n matrix of particles
% - ilogpo2 [double] n×1 vector of posterior kernel values for the particles
% - iloglik2 [double] n×1 vector of likelihood values for the particles
% - ModelName [string] name of the mod-file
% - MetropolisFolder [string] path to the Metropolis subfolder
% - bayestopt_ [structure] estimation options structure
%
% SPECIAL REQUIREMENTS
% None.
% Copyright © 2022-2023 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/>.
dprintf('Estimation:%s: Initialization...', sampler)
% Delete old mat files storign particles if any...
matfiles = sprintf('%s%sparticles*.mat', SimulationFolder, filesep());
files = dir(matfiles);
if ~isempty(files)
delete(matfiles);
dprintf('Estimation:%s: Old %s-files successfully erased.', sampler, sampler)
end
% Simulate a pool of particles characterizing the prior distribution (with the additional constraint that the likelihood is finite)
set_dynare_seed('default');
dprintf('Estimation:%s: Searching for initial values...', sampler);
particles = zeros(Prior.length(), n);
tlogpostkernel = zeros(n, 1);
loglikelihood = zeros(n, 1);
t0 = tic;
for j=1:n
notvalid = true;
while notvalid
candidate = Prior.draw();
if Prior.admissible(candidate)
particles(:,j) = candidate;
[tlogpostkernel(j), loglikelihood(j)] = tempered_likelihood(funobj, candidate, 0.0, Prior);
if isfinite(loglikelihood(j)) % if returned log-density is Inf or Nan (penalized value)
notvalid = false;
end
end
end
end
tt = toc(t0);
save(sprintf('%s%sparticles-1-%u.mat', SimulationFolder, filesep(), nsteps), 'particles', 'tlogpostkernel', 'loglikelihood')
dprintf('Estimation:%s: Initial values found (%.2fs)', sampler, tt)
skipline()
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