58 lines
2.9 KiB
Matlab
58 lines
2.9 KiB
Matlab
function IncrementalWeights = gaussian_mixture_densities(obs,StateMuPrior,StateSqrtPPrior,StateWeightsPrior,...
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StateMuPost,StateSqrtPPost,StateWeightsPost,...
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StateParticles,H,normconst,weigths1,weigths2,ReducedForm,ThreadsOptions)
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%
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% Elements to calculate the importance sampling ratio
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%
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% INPUTS
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% reduced_form_model [structure] Matlab's structure describing the reduced form model.
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% reduced_form_model.measurement.H [double] (pp x pp) variance matrix of measurement errors.
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% reduced_form_model.state.Q [double] (qq x qq) variance matrix of state errors.
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% reduced_form_model.state.dr [structure] output of resol.m.
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% Y [double] pp*smpl matrix of (detrended) data, where pp is the maximum number of observed variables.
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% start [integer] scalar, likelihood evaluation starts at 'start'.
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% smolyak_accuracy [integer] scalar.
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%
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% OUTPUTS
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% LIK [double] scalar, likelihood
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% lik [double] vector, density of observations in each period.
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%
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% REFERENCES
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%
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% NOTES
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% The vector "lik" is used to evaluate the jacobian of the likelihood.
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% Copyright (C) 2009-2017 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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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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% Compute the density of particles under the prior distribution
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[ras,ras,prior] = probability(StateMuPrior,StateSqrtPPrior,StateWeightsPrior,StateParticles) ;
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prior = prior' ;
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% Compute the density of particles under the proposal distribution
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[ras,ras,proposal] = probability(StateMuPost,StateSqrtPPost,StateWeightsPost,StateParticles) ;
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proposal = proposal' ;
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% Compute the density of the current observation conditionally to each particle
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yt_t_1_i = measurement_equations(StateParticles,ReducedForm,ThreadsOptions) ;
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%eta_t_i = bsxfun(@minus,obs,yt_t_1_i)' ;
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%yt_t_1 = sum(yt_t_1_i*weigths1,2) ;
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%tmp = bsxfun(@minus,yt_t_1_i,yt_t_1) ;
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%Pyy = bsxfun(@times,weigths2',tmp)*tmp' + H ;
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%sqr_det = sqrt(det(Pyy)) ;
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%foo = (eta_t_i/Pyy).*eta_t_i ;
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%likelihood = exp(-0.5*sum(foo,2))/(normconst*sqr_det) + 1e-99 ;
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likelihood = probability2(obs,sqrt(H),yt_t_1_i) ;
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IncrementalWeights = likelihood.*prior./proposal ;
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