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New SMC sampler (Herbst and Schorfheide).

rm-particles^2
Frédéric Karamé 2019-02-11 10:59:51 +01:00
parent 7c85187319
commit 22be3797a8
4 changed files with 426 additions and 30 deletions
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71
src/DSMH_sampler.m
View File

@ -56,17 +56,17 @@ function DSMH_sampler(TargetFun,xparam1,mh_bounds,dataset_,dataset_info,options_
lambda = exp(bsxfun(@minus,options_.dsmh.H,1:1:options_.dsmh.H)/(options_.dsmh.H-1)*log(options_.dsmh.lambda1));
c = 55 ;
c = 0.055 ;
% Step 0: Initialization of the sampler
[ param, tlogpost_iminus1, loglik, ~, ~, npar, nparticles, bayestopt_] = ...
DSMH_initialization(TargetFun, xparam1, mh_bounds, dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
[ param, tlogpost_iminus1, loglik, npar, ~, bayestopt_] = ...
SMC_samplers_initialization(TargetFun, xparam1, mh_bounds, dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_,options_.dsmh.number_of_particles);
ESS = zeros(options_.dsmh.H,1) ;
zhat = 1 ;
% The DSMH starts here
for i=1:options_.dsmh.H
for i=2:options_.dsmh.H
disp('');
disp('Tempered iteration');
disp(i) ;
@ -81,12 +81,33 @@ zhat = 1 ;
weights = exp(loglik*(lambda(end)-lambda(end-1)));
weights = weights/sum(weights);
indx_resmpl = DSMH_resampling(weights,rand(1,1),nparticles);
indx_resmpl = smc_resampling(weights,rand(1,1),options_.dsmh.number_of_particles);
distrib_param = param(:,indx_resmpl);
%% Plot parameters densities
mean_xparam = mean(distrib_param,2);
%mat_var_cov = bsxfun(@minus,distrib_param,mean_xparam) ;
%mat_var_cov = (mat_var_cov*mat_var_cov')/(options_.HSsmc.nparticles-1) ;
%std_xparam = sqrt(diag(mat_var_cov)) ;
lb95_xparam = zeros(npar,1) ;
ub95_xparam = zeros(npar,1) ;
for i=1:npar
temp = sortrows(distrib_param(i,:)') ;
lb95_xparam(i) = temp(0.025*options_.HSsmc.nparticles) ;
ub95_xparam(i) = temp(0.975*options_.HSsmc.nparticles) ;
end
TeX = options_.TeX;
str = sprintf(' Param. \t Lower Bound (95%%) \t Mean \t Upper Bound (95%%)');
for l=1:npar
[name,~] = get_the_name(l,TeX,M_,estim_params_,options_);
str = sprintf('%s\n %s \t\t %5.4f \t\t %7.5f \t\t %5.4f', str, name, lb95_xparam(l), mean_xparam(l), ub95_xparam(l));
end
disp([str])
disp('')
%% Plot parameters densities
[nbplt,nr,nc,lr,lc,nstar] = pltorg(npar);
if TeX
@ -99,16 +120,18 @@ end
number_of_grid_points = 2^9; % 2^9 = 512 !... Must be a power of two.
bandwidth = 0; % Rule of thumb optimal bandwidth parameter.
kernel_function = 'gaussian'; % Gaussian kernel for Fast Fourier Transform approximation.
for plt = 1:nbplt,
plt = 1 ;
%for plt = 1:nbplt,
if TeX
NAMES = [];
TeXNAMES = [];
end
hh = dyn_figure(options_.nodisplay,'Name','Parameters Densities');
for k=1:min(nstar,npar-(plt-1)*nstar)
subplot(nr,nc,k)
kk = (plt-1)*nstar+k;
[name,texname] = get_the_name(kk,TeX,M_,estim_params_,options_);
for k=1:npar %min(nstar,npar-(plt-1)*nstar)
subplot(ceil(sqrt(npar)),floor(sqrt(npar)),k)
%kk = (plt-1)*nstar+k;
[name,texname] = get_the_name(k,TeX,M_,estim_params_,options_);
if TeX
if isempty(NAMES)
NAMES = name;
@ -118,9 +141,9 @@ for plt = 1:nbplt,
TeXNAMES = char(TeXNAMES,texname);
end
end
optimal_bandwidth = mh_optimal_bandwidth(distrib_param(kk,:)',nparticles,bandwidth,kernel_function);
[density(:,1),density(:,2)] = kernel_density_estimate(distrib_param(kk,:)',number_of_grid_points,...
nparticles,optimal_bandwidth,kernel_function);
optimal_bandwidth = mh_optimal_bandwidth(distrib_param(k,:)',options_.dsmh.number_of_particles,bandwidth,kernel_function);
[density(:,1),density(:,2)] = kernel_density_estimate(distrib_param(k,:)',number_of_grid_points,...
options_.dsmh.number_of_particles,optimal_bandwidth,kernel_function);
plot(density(:,1),density(:,2));
hold on
title(name,'interpreter','none')
@ -142,19 +165,7 @@ for plt = 1:nbplt,
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,' \n');
end
end
function indx = DSMH_resampling(weights,noise,number)
indx = zeros(number,1);
cumweights = cumsum(weights);
randvec = (transpose(1:number)-1+noise(:))/number;
j = 1;
for i=1:number
while (randvec(i)>cumweights(j))
j = j+1;
end
indx(i) = j;
end
%end
function [tlogpost_iminus1,loglik,param] = sort_matrices(tlogpost_iminus1,loglik,param)
[~,indx_ord] = sortrows(tlogpost_iminus1);
@ -188,7 +199,7 @@ function c = tune_c(TargetFun,param,tlogpost_i,lambda,i,c,Omegachol,weights,data
stop=0 ;
while stop==0
acpt = 0.0;
indx_resmpl = DSMH_resampling(weights,rand(1,1),options_.dsmh.G);
indx_resmpl = smc_resampling(weights,rand(1,1),options_.dsmh.G);
param0 = param(:,indx_resmpl);
tlogpost0 = tlogpost_i(indx_resmpl);
for j=1:options_.dsmh.G
@ -240,7 +251,7 @@ function [out_param,out_tlogpost_iminus1,out_loglik] = mutation_DSMH(TargetFun,p
out_tlogpost_iminus1 = tlogpost_i;
out_loglik = loglik;
% resample and initialize the starting groups
indx_resmpl = DSMH_resampling(weights,rand(1,1),options_.dsmh.G);
indx_resmpl = smc_resampling(weights,rand(1,1),options_.dsmh.G);
param0 = param(:,indx_resmpl);
tlogpost_iminus10 = tlogpost_iminus1(indx_resmpl);
tlogpost_i0 = tlogpost_i(indx_resmpl);
@ -300,4 +311,4 @@ function [out_param,out_tlogpost_iminus1,out_loglik] = mutation_DSMH(TargetFun,p
out_loglik(rang) = loglik0;
end
end

257
src/Herbst_Schorfheide_sampler.m Normal file
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@ -0,0 +1,257 @@
function Herbst_Schorfheide_sampler(TargetFun,xparam1,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
% function Herbst_Schorfheide_sampler(TargetFun,ProposalFun,xparam1,sampler_options,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
% SMC sampler from JAE 2014 .
%
% INPUTS
% o TargetFun [char] string specifying the name of the objective
% function (posterior kernel).
% o ProposalFun [char] string specifying the name of the proposal
% density
% o xparam1 [double] (p*1) vector of parameters to be estimated (initial values).
% o sampler_options structure
% .invhess [double] (p*p) matrix, posterior covariance matrix (at the mode).
% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
% o dataset_ data structure
% o dataset_info dataset info structure
% o options_ options structure
% o M_ model structure
% o estim_params_ estimated parameters structure
% o bayestopt_ estimation options structure
% o oo_ outputs structure
%
% SPECIAL REQUIREMENTS
% None.
%
% PARALLEL CONTEXT
% The most computationally intensive part of this function may be executed
% in parallel. The code suitable to be executed in
% parallel on multi core or cluster machine (in general a 'for' cycle)
% has been removed from this function and been placed in the posterior_sampler_core.m funtion.
%
% The DYNARE parallel packages comprise a i) set of pairs of Matlab functions that can be executed in
% parallel and called name_function.m and name_function_core.m and ii) a second set of functions used
% to manage the parallel computations.
%
% This function was the first function to be parallelized. Later, other
% functions have been parallelized using the same methodology.
% Then the comments write here can be used for all the other pairs of
% parallel functions and also for management functions.
% Copyright (C) 2006-2017 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 <http://www.gnu.org/licenses/>.
% Create the tempering schedule
phi = bsxfun(@power,(bsxfun(@minus,1:1:options_.HSsmc.nphi,1)/(options_.HSsmc.nphi-1)),options_.HSsmc.lambda) ;
% tuning for MH algorithms matrices
zhat = 0 ; % normalization constant
csim = zeros(options_.HSsmc.nphi,1) ; % scale parameter
ESSsim = zeros(options_.HSsmc.nphi,1) ; % ESS
acptsim = zeros(options_.HSsmc.nphi,1) ; % average acceptance rate
% Step 0: Initialization of the sampler
[ param, tlogpost_i, loglik, npar, ~, bayestopt_] = ...
SMC_samplers_initialization(TargetFun, xparam1, mh_bounds, dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_,options_.HSsmc.nparticles);
weights = ones(options_.HSsmc.nparticles,1)/options_.HSsmc.nparticles ;
% The Herbst and Schorfheide sampler starts here
for i=2:options_.HSsmc.nphi
% (a) Correction
% incremental weights
incwt = exp((phi(i)-phi(i-1))*loglik) ;
% update weights
weights = bsxfun(@times,weights,incwt) ;
sum_weights = sum(weights) ;
zhat = zhat + log(sum_weights) ;
% normalize weights
weights = weights/sum_weights ;
% (b) Selection
ESSsim(i) = 1/sum(weights.^2) ;
if (ESSsim(i) < options_.HSsmc.nparticles/2)
indx_resmpl = smc_resampling(weights,rand(1,1),options_.HSsmc.nparticles) ;
param = param(:,indx_resmpl) ;
loglik = loglik(indx_resmpl) ;
tlogpost_i = tlogpost_i(indx_resmpl) ;
weights = ones(options_.HSsmc.nparticles,1)/options_.HSsmc.nparticles ;
end
% (c) Mutation
options_.HSsmc.c = options_.HSsmc.c*modified_logit(0.95,0.1,16.0,options_.HSsmc.acpt-options_.HSsmc.trgt) ;
% Calculate estimates of mean and variance
mu = param*weights ;
z = bsxfun(@minus,param,mu) ;
R = z*(bsxfun(@times,z',weights)) ;
Rchol = chol(R)' ;
% Mutation
if options_.HSsmc.option_mutation==1
[param,tlogpost_i,loglik,options_.HSsmc.acpt] = mutation_RW(TargetFun,param,tlogpost_i,loglik,phi,i,options_.HSsmc.c*Rchol,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_) ;
elseif options_.HSsmc.option_mutation==2
inv_R = inv(options_.HSsmc.c^2*R) ;
Rdiagchol = sqrt(diag(R)) ;
[param,tlogpost_i,loglik,options_.HSsmc.acpt] = mutation_Mixture(TargetFun,param,tlogpost_i,loglik,phi,i,options_.HSsmc.c*Rchol,options_.HSsmc.c*Rdiagchol,inv_R,mu,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_) ;
end
acptsim(i) = options_.HSsmc.acpt ;
csim(i) = options_.HSsmc.c ;
% print information
fprintf(' Iteration = %5.0f / %5.0f \n', i, options_.HSsmc.nphi);
fprintf(' phi = %5.4f \n', phi(i));
fprintf(' Neff = %5.4f \n', ESSsim(i));
fprintf(' %accept. = %5.4f \n', acptsim(i));
end
indx_resmpl = smc_resampling(weights,rand(1,1),options_.HSsmc.nparticles);
distrib_param = param(:,indx_resmpl);
fprintf(' Log_lik = %5.4f \n', zhat);
mean_xparam = mean(distrib_param,2);
%mat_var_cov = bsxfun(@minus,distrib_param,mean_xparam) ;
%mat_var_cov = (mat_var_cov*mat_var_cov')/(options_.HSsmc.nparticles-1) ;
%std_xparam = sqrt(diag(mat_var_cov)) ;
lb95_xparam = zeros(npar,1) ;
ub95_xparam = zeros(npar,1) ;
for i=1:npar
temp = sortrows(distrib_param(i,:)') ;
lb95_xparam(i) = temp(0.025*options_.HSsmc.nparticles) ;
ub95_xparam(i) = temp(0.975*options_.HSsmc.nparticles) ;
end
TeX = options_.TeX;
str = sprintf(' Param. \t Lower Bound (95%%) \t Mean \t Upper Bound (95%%)');
for l=1:npar
[name,~] = get_the_name(l,TeX,M_,estim_params_,options_);
str = sprintf('%s\n %s \t\t %5.4f \t\t %7.5f \t\t %5.4f', str, name, lb95_xparam(l), mean_xparam(l), ub95_xparam(l));
end
disp([str])
disp('')
%% Plot parameters densities
[nbplt,nr,nc,lr,lc,nstar] = pltorg(npar);
if TeX
fidTeX = fopen([M_.fname '_param_density.tex'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by DSMH.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
fprintf(fidTeX,' \n');
end
number_of_grid_points = 2^9; % 2^9 = 512 !... Must be a power of two.
bandwidth = 0; % Rule of thumb optimal bandwidth parameter.
kernel_function = 'gaussian'; % Gaussian kernel for Fast Fourier Transform approximation.
plt = 1 ;
%for plt = 1:nbplt,
if TeX
NAMES = [];
TeXNAMES = [];
end
hh = dyn_figure(options_.nodisplay,'Name','Parameters Densities');
for k=1:npar %min(nstar,npar-(plt-1)*nstar)
subplot(ceil(sqrt(npar)),floor(sqrt(npar)),k)
%kk = (plt-1)*nstar+k;
[name,texname] = get_the_name(k,TeX,M_,estim_params_,options_);
if TeX
if isempty(NAMES)
NAMES = name;
TeXNAMES = texname;
else
NAMES = char(NAMES,name);
TeXNAMES = char(TeXNAMES,texname);
end
end
optimal_bandwidth = mh_optimal_bandwidth(distrib_param(k,:)',options_.HSsmc.nparticles,bandwidth,kernel_function);
[density(:,1),density(:,2)] = kernel_density_estimate(distrib_param(k,:)',number_of_grid_points,...
options_.HSsmc.nparticles,optimal_bandwidth,kernel_function);
plot(density(:,1),density(:,2));
hold on
title(name,'interpreter','none')
hold off
axis tight
drawnow
end
dyn_saveas(hh,[ M_.fname '_param_density' int2str(plt) ],options_.nodisplay,options_.graph_format);
if TeX
% TeX eps loader file
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:min(nstar,length(x)-(plt-1)*nstar)
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_ParametersDensities%s}\n',M_.fname,int2str(plt));
fprintf(fidTeX,'\\caption{ParametersDensities.}');
fprintf(fidTeX,'\\label{Fig:ParametersDensities:%s}\n',int2str(plt));
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,' \n');
end
%end
function [param,tlogpost_i,loglik,acpt] = mutation_RW(TargetFun,param,tlogpost_i,loglik,phi,i,cRchol,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_)
acpt = 0.0 ;
tlogpost_i = tlogpost_i + (phi(i)-phi(i-1))*loglik ;
for j=1:options_.HSsmc.nparticles
validate= 0;
while validate==0
candidate = param(:,j) + cRchol*randn(size(param,1),1) ;
if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
[tlogpostx,loglikx] = tempered_likelihood(TargetFun,candidate,phi(i),dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
if isfinite(loglikx) % if returned log-density is not Inf or Nan (penalized value)
validate = 1;
if rand(1,1)<exp(tlogpostx-tlogpost_i(j)) % accept
acpt = acpt + 1 ;
param(:,j) = candidate;
loglik(j) = loglikx;
tlogpost_i(j) = tlogpostx;
end
end
end
end
end
acpt = acpt/options_.HSsmc.nparticles;
function [param,tlogpost_i,loglik,acpt] = mutation_Mixture(TargetFun,param,tlogpost_i,loglik,phi,i,cRchol,cRdiagchol,invR,mu,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_)
acpt = 0.0 ;
tlogpost_i = tlogpost_i + (phi(i)-phi(i-1))*loglik ;
for j=1:options_.HSsmc.nparticles
qx = 0 ;
q0 = 0 ;
alpind = rand(1,1) ;
validate= 0;
while validate==0
if alpind<options_.HSsmc.alp % RW, no need to modify qx and q0
candidate = param(:,j) + cRchol*randn(size(param,1),1);
elseif alpind<options_.HSsmc.alp + (1-options_.HSsmc.alp/2) % random walk with diagonal cov no need to modify qx and q0
candidate = param(:,j) + cRdiagchol*randn(size(param,1),1);
else % Proposal densities
candidate = mu + cRchol*randn(size(param,1),1);
qx = -.5*(candidate-mu)'*invR*(candidate-mu) ; % no need of the constants in the acceptation rule
q0 = -.5*(param(:,j)-mu)'*invR*(param(:,j)-mu) ;
end
if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
[tlogpostx,loglikx] = tempered_likelihood(TargetFun,candidate,phi(i),dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
if isfinite(loglikx) % if returned log-density is not Inf or Nan (penalized value)
validate = 1;
if rand(1,1)<exp((tlogpostx-qx)-(tlogpost_i(j)-q0)) % accept
acpt = acpt + 1 ;
param(:,j) = candidate;
loglik(j) = loglikx;
tlogpost_i(j) = tlogpostx;
end
end
end
end
end
acpt = acpt/options_.HSsmc.nparticles;
function x = modified_logit(a,b,c,d)
tmp = exp(c*d) ;
x = a + b*tmp/(1 + tmp) ;

117
src/SMC_samplers_initialization.m Normal file
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@ -0,0 +1,117 @@
function [ ix2, temperedlogpost, loglik, npar, NumberOfParticles, bayestopt_] = ...
SMC_samplers_initialization(TargetFun, xparam1, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_,NumberOfParticles)
% function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npar, NumberOfParticles, bayestopt_] = ...
% SMC_samplers_initialization(TargetFun, xparam1, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_,NumberOfParticles)
% Draw in prior distribution to initialize samplers.
%
% INPUTS
% o TargetFun [char] string specifying the name of the objective
% function (tempered posterior kernel and likelihood).
% o xparam1 [double] (p*1) vector of parameters to be estimated (initial values).
% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
% o dataset_ data structure
% o dataset_info dataset info structure
% o options_ options structure
% o M_ model structure
% o estim_params_ estimated parameters structure
% o bayestopt_ estimation options structure
% o oo_ outputs structure
% o NumberOfParticles scalar
%
% OUTPUTS
% o ix2 [double] (NumberOfParticles*npar) vector of starting points for different chains
% o ilogpo2 [double] (NumberOfParticles*1) vector of initial posterior values for different chains
% o iloglik2 [double] (NumberOfParticles*1) vector of initial likelihood values for different chains
% o ModelName [string] name of the mod-file
% o MetropolisFolder [string] path to the Metropolis subfolder
% o npar [scalar] number of parameters estimated
% o NumberOfParticles [scalar] Number of particles requested for the parameters distributions
% o bayestopt_ [structure] estimation options structure
%
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 2006-2017 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 <http://www.gnu.org/licenses/>.
%Initialize outputs
ix2 = [];
ilogpo2 = [];
iloglik2 = [];
ModelName = [];
MetropolisFolder = [];
npar = [];
ModelName = M_.fname;
if ~isempty(M_.bvar)
ModelName = [ModelName '_bvar'];
end
MetropolisFolder = CheckPath('dsmh',M_.dname);
BaseName = [MetropolisFolder filesep ModelName];
npar = length(xparam1);
% Here we start a new DS Metropolis-Hastings, previous draws are discarded.
disp('Estimation:: Initialization...')
% Delete old dsmh files if any...
files = dir([BaseName '_dsmh*_blck*.mat']);
%if length(files)
% delete([BaseName '_dsmh*_blck*.mat']);
% disp('Estimation::smc: Old dsmh-files successfully erased!')
%end
% Delete old log file.
file = dir([ MetropolisFolder '/dsmh.log']);
%if length(file)
% delete([ MetropolisFolder '/dsmh.log']);
% disp('Estimation::dsmh: Old dsmh.log file successfully erased!')
% disp('Estimation::dsmh: Creation of a new dsmh.log file.')
%end
fidlog = fopen([MetropolisFolder '/dsmh.log'],'w');
fprintf(fidlog,'%% DSMH log file (Dynare).\n');
fprintf(fidlog,['%% ' datestr(now,0) '.\n']);
fprintf(fidlog,' \n\n');
fprintf(fidlog,'%% Session 1.\n');
fprintf(fidlog,' \n');
prior_draw(bayestopt_,options_.prior_trunc);
% Find initial values for the NumberOfParticles chains...
set_dynare_seed('default');
fprintf(fidlog,[' Initial values of the parameters:\n']);
disp('Estimation::dsmh: Searching for initial values...');
ix2 = zeros(npar,NumberOfParticles);
temperedlogpost = zeros(NumberOfParticles,1);
loglik = zeros(NumberOfParticles,1);
%stderr = sqrt(bsxfun(@power,mh_bounds.ub-mh_bounds.lb,2)/12)/10;
for j=1:NumberOfParticles
validate = 0;
while validate == 0
candidate = prior_draw()';
% candidate = xparam1(:) + 0.001*randn(npar,1);%bsxfun(@times,stderr,randn(npar,1)) ;
if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
ix2(:,j) = candidate ;
[temperedlogpost(j),loglik(j)] = tempered_likelihood(TargetFun,candidate,0.0,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
if isfinite(loglik(j)) % if returned log-density is Inf or Nan (penalized value)
validate = 1;
end
end
end
end
fprintf(fidlog,' \n');
disp('Estimation:: Initial values found!')
skipline()

11
src/smc_resampling.m Normal file
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@ -0,0 +1,11 @@
function indx = smc_resampling(weights,noise,number)
indx = zeros(number,1);
cumweights = cumsum(weights);
randvec = (transpose(1:number)-1+noise(:))/number;
j = 1;
for i=1:number
while (randvec(i)>cumweights(j))
j = j+1;
end
indx(i) = j;
end