56 lines
2.6 KiB
Matlab
56 lines
2.6 KiB
Matlab
function IncrementalWeights = gaussian_mixture_densities(obs, StateMuPrior, StateSqrtPPrior, StateWeightsPrior, ...
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StateMuPost, StateSqrtPPost, StateWeightsPost, StateParticles, H, ...
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ReducedForm, ThreadsOptions, options_, M_)
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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 © 2009-2023 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 <https://www.gnu.org/licenses/>.
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% Compute the density of particles under the prior distribution
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[~, ~, 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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[~, ~, 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, options_, M_);
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% likelihood
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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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