Commit a73e4d94 authored by Frédéric Karamé's avatar Frédéric Karamé

Add the possibility of Gaussian-Mixture Particle Filter without resampling.

parent a6a3e1b1
function [StateMu,StateSqrtP,StateWeights] = fit_gaussian_mixture(X,StateMu,StateSqrtP,StateWeights,crit,niters,check)
function [StateMu,StateSqrtP,StateWeights] = fit_gaussian_mixture(X,X_weights,StateMu,StateSqrtP,StateWeights,crit,niters,check)
% Copyright (C) 2013 Dynare Team
%
......@@ -26,7 +26,7 @@ end
eold = -Inf;
for n=1:niters
% Calculate posteriors based on old parameters
[prior,likelihood,marginal,posterior] = probability(StateMu,StateSqrtP,StateWeights,X);
[prior,likelihood,marginal,posterior] = probability3(StateMu,StateSqrtP,StateWeights,X,X_weights);
e = sum(log(marginal));
if (n > 1 && abs((e - eold)/eold) < crit)
return;
......
......@@ -294,7 +294,7 @@ for t=1:sample_size
StateParticles = bsxfun(@plus,StateMuPost(:,i),StateSqrtPPost(:,:,i)*nodes') ;
IncrementalWeights = gaussian_mixture_densities(Y(:,t),StateMuPrior,StateSqrtPPrior,StateWeightsPrior,...
StateMuPost,StateSqrtPPost,StateWeightsPost,...
StateParticles,H,const_lik,weights,weights_c,ReducedForm,ThreadsOptions) ;
StateParticles,H,const_lik,ReducedForm,ThreadsOptions) ;
SampleWeights(i) = sum(StateWeightsPost(i)*weights.*IncrementalWeights) ;
end
SumSampleWeights = sum(SampleWeights) ;
......@@ -312,13 +312,16 @@ for t=1:sample_size
StateParticles = importance_sampling(StateMuPost,StateSqrtPPost,StateWeightsPost',number_of_particles) ;
IncrementalWeights = gaussian_mixture_densities(Y(:,t),StateMuPrior,StateSqrtPPrior,StateWeightsPrior,...
StateMuPost,StateSqrtPPost,StateWeightsPost,...
StateParticles,H,const_lik,1/number_of_particles,...
1/number_of_particles,ReducedForm,ThreadsOptions) ;
StateParticles,H,const_lik,ReducedForm,ThreadsOptions) ;
SampleWeights = IncrementalWeights/number_of_particles ;
SumSampleWeights = sum(SampleWeights,1) ;
SampleWeights = SampleWeights./SumSampleWeights ;
lik(t) = log(SumSampleWeights) ;
[StateMu,StateSqrtP,StateWeights] = fit_gaussian_mixture(StateParticles,StateMu,StateSqrtP,StateWeights,0.001,10,1) ;
if (ParticleOptions.resampling.status.generic && neff(SampleWeights)<ParticleOptions.resampling.threshold*sample_size) || ParticleOptions.resampling.status.systematic
StateParticles = resample(StateParticles',SampleWeights',ParticleOptions)';
SampleWeights = ones(number_of_particles,1)/number_of_particles;
end
[StateMu,StateSqrtP,StateWeights] = fit_gaussian_mixture(StateParticles,SampleWeights',StateMu,StateSqrtP,StateWeights,0.001,10,1) ;
end
end
......
function [prior,likelihood,C,posterior] = probability3(mu,sqrtP,prior,X,X_weights)
% Copyright (C) 2013 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/>.
[dim,nov] = size(X);
M = size(mu,2) ;
if nargout>1
likelihood = zeros(M,nov);
normfact = (2*pi)^(dim/2);
for k=1:M
XX = bsxfun(@minus,X,mu(:,k));
S = sqrtP(:,:,k);
foo = S \ XX;
likelihood(k,:) = exp(-0.5*sum(foo.*foo, 1))/abs((normfact*prod(diag(S))));
end
end
wlikelihood = bsxfun(@times,X_weights,likelihood) + 1e-99;
if nargout>2
C = prior*wlikelihood + 1e-99;
end
if nargout>3
posterior = bsxfun(@rdivide,bsxfun(@times,prior',wlikelihood),C) + 1e-99 ;
posterior = bsxfun(@rdivide,posterior,sum(posterior,1));
end
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment