From 4921000f0ac66280fd39d577d81532b9b43f986d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?St=C3=A9phane=20Adjemian=20=28Charybdis=29?=
<stephane.adjemian@univ-lemans.fr>
Date: Thu, 18 May 2017 23:59:10 +0200
Subject: [PATCH] Fixed indentation.
---
src/auxiliary_initialization.m | 2 +-
src/auxiliary_particle_filter.m | 10 +--
src/conditional_filter_proposal.m | 14 ++---
src/conditional_particle_filter.m | 50 +++++++--------
src/fit_gaussian_mixture.m | 55 ++++++++--------
src/gaussian_densities.m | 14 ++---
src/gaussian_filter.m | 22 +++----
src/gaussian_filter_bank.m | 4 +-
src/gaussian_mixture_densities.m | 17 +++--
src/gaussian_mixture_filter.m | 58 ++++++++---------
src/gaussian_mixture_filter_bank.m | 20 +++---
src/importance_sampling.m | 11 ++--
src/measurement_equations.m | 5 +-
src/multivariate_smooth_resampling.m | 6 +-
src/mykmeans.m | 56 ++++++++---------
src/nonlinear_kalman_filter.m | 18 +++---
src/online_auxiliary_filter.m | 93 ++++++++++++++--------------
src/probability.m | 24 +++----
src/probability2.m | 12 ++--
src/probability3.m | 24 +++----
src/residual_resampling.m | 40 ++++++------
src/solve_model_for_online_filter.m | 54 ++++++++--------
src/spherical_radial_sigma_points.m | 10 +--
src/traditional_resampling.m | 2 +-
src/univariate_smooth_resampling.m | 6 +-
src/unscented_sigma_points.m | 16 +++--
26 files changed, 317 insertions(+), 326 deletions(-)
diff --git a/src/auxiliary_initialization.m b/src/auxiliary_initialization.m
index 7034882..98f79ae 100644
--- a/src/auxiliary_initialization.m
+++ b/src/auxiliary_initialization.m
@@ -87,7 +87,7 @@ yhat = bsxfun(@minus,StateVectors,state_variables_steady_state);
% yhat_ = bsxfun(@minus,StateVectors_,state_variables_steady_state);
% [tmp, tmp_] = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,ThreadsOptions.local_state_space_iteration_2);
%else
- tmp = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
+tmp = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
%end
PredictedObservedMean = weights*(tmp(mf1,:)');
PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
diff --git a/src/auxiliary_particle_filter.m b/src/auxiliary_particle_filter.m
index a793411..42d57ca 100644
--- a/src/auxiliary_particle_filter.m
+++ b/src/auxiliary_particle_filter.m
@@ -1,6 +1,6 @@
function [LIK,lik] = auxiliary_particle_filter(ReducedForm,Y,start,ParticleOptions,ThreadsOptions)
-% Evaluates the likelihood of a nonlinear model with the auxiliary particle filter
+% Evaluates the likelihood of a nonlinear model with the auxiliary particle filter
% allowing eventually resampling.
%
% Copyright (C) 2011-2015 Dynare Team
@@ -91,11 +91,11 @@ end
% [nodes,nodes_weights,nodes_weights_c] = unscented_sigma_points(number_of_structural_innovations,ParticleOptions);
% else
% error('Estimation: This approximation for the proposal is not implemented or unknown!')
-% end
+% end
% nodes = (Q_lower_triangular_cholesky*(nodes'))' ;
nodes = zeros(1,number_of_structural_innovations) ;
-nodes_weights = ones(number_of_structural_innovations,1) ;
+nodes_weights = ones(number_of_structural_innovations,1) ;
for t=1:sample_size
yhat = bsxfun(@minus,StateVectors,state_variables_steady_state);
@@ -126,7 +126,7 @@ for t=1:sample_size
yhat_ = yhat_(:,indx) ;
end
yhat = yhat(:,indx) ;
- weights_stage_1 = weights(indx)./tau_tilde(indx) ;
+ weights_stage_1 = weights(indx)./tau_tilde(indx) ;
epsilon = Q_lower_triangular_cholesky*randn(number_of_structural_innovations,number_of_particles);
if pruning
[tmp, tmp_] = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,ThreadsOptions.local_state_space_iteration_2);
@@ -137,7 +137,7 @@ for t=1:sample_size
StateVectors = tmp(mf0,:);
PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
weights_stage_2 = weights_stage_1.*(exp(-.5*(const_lik+sum(PredictionError.*(H\PredictionError),1))) + 1e-99) ;
- lik(t) = log(mean(weights_stage_2)) ;
+ lik(t) = log(mean(weights_stage_2)) ;
weights = weights_stage_2/sum(weights_stage_2);
if (ParticleOptions.resampling.status.generic && neff(weights)<ParticleOptions.resampling.threshold*sample_size) || ParticleOptions.resampling.status.systematic
if pruning
diff --git a/src/conditional_filter_proposal.m b/src/conditional_filter_proposal.m
index e90efa1..0c41218 100644
--- a/src/conditional_filter_proposal.m
+++ b/src/conditional_filter_proposal.m
@@ -51,8 +51,8 @@ ghuu = ReducedForm.ghuu;
ghxu = ReducedForm.ghxu;
if any(any(isnan(ghx))) || any(any(isnan(ghu))) || any(any(isnan(ghxx))) || any(any(isnan(ghuu))) || any(any(isnan(ghxu))) || ...
- any(any(isinf(ghx))) || any(any(isinf(ghu))) || any(any(isinf(ghxx))) || any(any(isinf(ghuu))) || any(any(isinf(ghxu))) ...
- any(any(abs(ghx)>1e4)) || any(any(abs(ghu)>1e4)) || any(any(abs(ghxx)>1e4)) || any(any(abs(ghuu)>1e4)) || any(any(abs(ghxu)>1e4))
+ any(any(isinf(ghx))) || any(any(isinf(ghu))) || any(any(isinf(ghxx))) || any(any(isinf(ghuu))) || any(any(isinf(ghxu))) ...
+ any(any(abs(ghx)>1e4)) || any(any(abs(ghu)>1e4)) || any(any(abs(ghxx)>1e4)) || any(any(abs(ghuu)>1e4)) || any(any(abs(ghxu)>1e4))
ghx
ghu
ghxx
@@ -106,7 +106,7 @@ if ParticleOptions.proposal_approximation.cubature || ParticleOptions.proposal_a
Error = obs - PredictedObservedMean ;
StateVectorMean = PredictedStateMean + (CovarianceObservedStateSquareRoot/PredictedObservedVarianceSquareRoot)*Error ;
if ParticleOptions.cpf_weights_method.amisanotristani
- Weights = SampleWeights.*probability2(zeros(number_of_observed_variables,1),PredictedObservedVarianceSquareRoot,Error) ;
+ Weights = SampleWeights.*probability2(zeros(number_of_observed_variables,1),PredictedObservedVarianceSquareRoot,Error) ;
end
else
dState = bsxfun(@minus,tmp(mf0,:),PredictedStateMean);
@@ -120,15 +120,15 @@ else
StateVectorVariance = PredictedStateVariance - KalmanFilterGain*PredictedObservedVariance*KalmanFilterGain';
StateVectorVarianceSquareRoot = chol(StateVectorVariance + eye(number_of_state_variables)*1e-6)' ;
if ParticleOptions.cpf_weights_method.amisanotristani
- Weights = SampleWeights.*probability2(zeros(number_of_observed_variables,1),chol(PredictedObservedVariance)',Error) ;
+ Weights = SampleWeights.*probability2(zeros(number_of_observed_variables,1),chol(PredictedObservedVariance)',Error) ;
end
end
PredictedStateVarianceSquareRoot = chol(PredictedStateVariance + eye(number_of_state_variables)*1e-6)' ;
ProposalStateVector = StateVectorVarianceSquareRoot*randn(size(StateVectorVarianceSquareRoot,2),1)+StateVectorMean ;
if ParticleOptions.cpf_weights_method.murrayjonesparslow
- Prior = probability2(PredictedStateMean,PredictedStateVarianceSquareRoot,ProposalStateVector) ;
- Posterior = probability2(StateVectorMean,StateVectorVarianceSquareRoot,ProposalStateVector) ;
- Likelihood = probability2(obs,H_lower_triangular_cholesky,measurement_equations(ProposalStateVector,ReducedForm,ThreadsOptions)) ;
+ Prior = probability2(PredictedStateMean,PredictedStateVarianceSquareRoot,ProposalStateVector) ;
+ Posterior = probability2(StateVectorMean,StateVectorVarianceSquareRoot,ProposalStateVector) ;
+ Likelihood = probability2(obs,H_lower_triangular_cholesky,measurement_equations(ProposalStateVector,ReducedForm,ThreadsOptions)) ;
Weights = SampleWeights.*Likelihood.*(Prior./Posterior) ;
end
diff --git a/src/conditional_particle_filter.m b/src/conditional_particle_filter.m
index 89c37b9..0143777 100644
--- a/src/conditional_particle_filter.m
+++ b/src/conditional_particle_filter.m
@@ -1,24 +1,24 @@
function [LIK,lik] = conditional_particle_filter(ReducedForm,Y,start,ParticleOptions,ThreadsOptions)
-%
+%
% Evaluates the likelihood of a non-linear model with a particle filter
-% - the proposal is built using the Kalman updating step for each particle.
-% - we need draws in the errors distributions
-% Whether we use Monte-Carlo draws from a multivariate gaussian distribution
-% as in Amisano & Tristani (JEDC 2010).
-% Whether we use multidimensional Gaussian sparse grids approximations:
-% - a univariate Kronrod-Paterson Gaussian quadrature combined by the Smolyak
-% operator (ref: Winschel & Kratzig, 2010).
+% - the proposal is built using the Kalman updating step for each particle.
+% - we need draws in the errors distributions
+% Whether we use Monte-Carlo draws from a multivariate gaussian distribution
+% as in Amisano & Tristani (JEDC 2010).
+% Whether we use multidimensional Gaussian sparse grids approximations:
+% - a univariate Kronrod-Paterson Gaussian quadrature combined by the Smolyak
+% operator (ref: Winschel & Kratzig, 2010).
% - a spherical-radial cubature (ref: Arasaratnam & Haykin, 2009a,2009b).
-% - a scaled unscented transform cubature (ref: Julier & Uhlmann 1997, van der
+% - a scaled unscented transform cubature (ref: Julier & Uhlmann 1997, van der
% Merwe & Wan 2003).
-%
-% Pros:
-% - Allows using current observable information in the proposal
-% - The use of sparse grids Gaussian approximation is much faster than the Monte-Carlo approach
-% Cons:
-% - The use of the Kalman updating step may biais the proposal distribution since
+%
+% Pros:
+% - Allows using current observable information in the proposal
+% - The use of sparse grids Gaussian approximation is much faster than the Monte-Carlo approach
+% Cons:
+% - The use of the Kalman updating step may biais the proposal distribution since
% it has been derived in a linear context and is implemented in a nonlinear
-% context. That is why particle resampling is performed.
+% context. That is why particle resampling is performed.
%
% INPUTS
% reduced_form_model [structure] Matlab's structure describing the reduced form model.
@@ -58,8 +58,8 @@ function [LIK,lik] = conditional_particle_filter(ReducedForm,Y,start,ParticleOpt
% stephane DOT adjemian AT univ DASH lemans DOT fr
persistent init_flag mf1
-persistent number_of_particles
-persistent sample_size number_of_observed_variables
+persistent number_of_particles
+persistent sample_size number_of_observed_variables
% Set default
if isempty(start)
@@ -82,14 +82,14 @@ if isempty(H)
H = 0;
H_lower_triangular_cholesky = 0;
else
- H_lower_triangular_cholesky = chol(H)';
+ H_lower_triangular_cholesky = chol(H)';
end
% Get initial condition for the state vector.
StateVectorMean = ReducedForm.StateVectorMean;
StateVectorVarianceSquareRoot = chol(ReducedForm.StateVectorVariance)';
state_variance_rank = size(StateVectorVarianceSquareRoot,2);
-Q_lower_triangular_cholesky = chol(Q)';
+Q_lower_triangular_cholesky = chol(Q)';
% Set seed for randn().
set_dynare_seed('default');
@@ -102,13 +102,13 @@ ks = 0 ;
StateParticles = bsxfun(@plus,StateVectorVarianceSquareRoot*randn(state_variance_rank,number_of_particles),StateVectorMean);
SampleWeights = ones(1,number_of_particles)/number_of_particles ;
for t=1:sample_size
- for i=1:number_of_particles
- [StateParticles(:,i),SampleWeights(i)] = ...
- conditional_filter_proposal(ReducedForm,Y(:,t),StateParticles(:,i),SampleWeights(i),Q_lower_triangular_cholesky,H_lower_triangular_cholesky,H,ParticleOptions,ThreadsOptions,normconst2) ;
+ for i=1:number_of_particles
+ [StateParticles(:,i),SampleWeights(i)] = ...
+ conditional_filter_proposal(ReducedForm,Y(:,t),StateParticles(:,i),SampleWeights(i),Q_lower_triangular_cholesky,H_lower_triangular_cholesky,H,ParticleOptions,ThreadsOptions,normconst2) ;
end
SumSampleWeights = sum(SampleWeights) ;
- lik(t) = log(SumSampleWeights) ;
- SampleWeights = SampleWeights./SumSampleWeights ;
+ lik(t) = log(SumSampleWeights) ;
+ SampleWeights = SampleWeights./SumSampleWeights ;
if (ParticleOptions.resampling.status.generic && neff(SampleWeights)<ParticleOptions.resampling.threshold*sample_size) || ParticleOptions.resampling.status.systematic
ks = ks + 1 ;
StateParticles = resample(StateParticles',SampleWeights',ParticleOptions)';
diff --git a/src/fit_gaussian_mixture.m b/src/fit_gaussian_mixture.m
index 43f2302..cc34e66 100644
--- a/src/fit_gaussian_mixture.m
+++ b/src/fit_gaussian_mixture.m
@@ -1,4 +1,4 @@
-function [StateMu,StateSqrtP,StateWeights] = fit_gaussian_mixture(X,X_weights,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
%
@@ -17,36 +17,35 @@ function [StateMu,StateSqrtP,StateWeights] = fit_gaussian_mixture(X,X_weights,St
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-[dim,Ndata] = size(X);
+[dim,Ndata] = size(X);
M = size(StateMu,2) ;
if check % Ensure that covariances don't collapse
- MIN_COVAR_SQRT = sqrt(eps);
- init_covars = StateSqrtP;
+ MIN_COVAR_SQRT = sqrt(eps);
+ init_covars = StateSqrtP;
end
eold = -Inf;
for n=1:niters
- % Calculate posteriors based on old parameters
- [prior,likelihood,marginal,posterior] = probability3(StateMu,StateSqrtP,StateWeights,X,X_weights);
- e = sum(log(marginal));
- if (n > 1 && abs((e - eold)/eold) < crit)
- return;
- else
- eold = e;
- end
- new_pr = (sum(posterior,2))';
- StateWeights = new_pr/Ndata;
- StateMu = bsxfun(@rdivide,(posterior*X')',new_pr);
- for j=1:M
- diffs = bsxfun(@minus,X,StateMu(:,j));
- tpost = (1/sqrt(new_pr(j)))*sqrt(posterior(j,:));
- diffs = bsxfun(@times,diffs,tpost);
- [foo,tcov] = qr2(diffs',0);
- StateSqrtP(:,:,j) = tcov';
- if check
- if min(abs(diag(StateSqrtP(:,:,j)))) < MIN_COVAR_SQRT
- StateSqrtP(:,:,j) = init_covars(:,:,j);
- end
+ % Calculate posteriors based on old parameters
+ [prior,likelihood,marginal,posterior] = probability3(StateMu,StateSqrtP,StateWeights,X,X_weights);
+ e = sum(log(marginal));
+ if (n > 1 && abs((e - eold)/eold) < crit)
+ return;
+ else
+ eold = e;
end
- end
-end
-
+ new_pr = (sum(posterior,2))';
+ StateWeights = new_pr/Ndata;
+ StateMu = bsxfun(@rdivide,(posterior*X')',new_pr);
+ for j=1:M
+ diffs = bsxfun(@minus,X,StateMu(:,j));
+ tpost = (1/sqrt(new_pr(j)))*sqrt(posterior(j,:));
+ diffs = bsxfun(@times,diffs,tpost);
+ [foo,tcov] = qr2(diffs',0);
+ StateSqrtP(:,:,j) = tcov';
+ if check
+ if min(abs(diag(StateSqrtP(:,:,j)))) < MIN_COVAR_SQRT
+ StateSqrtP(:,:,j) = init_covars(:,:,j);
+ end
+ end
+ end
+end
diff --git a/src/gaussian_densities.m b/src/gaussian_densities.m
index ae3afb1..2246e45 100644
--- a/src/gaussian_densities.m
+++ b/src/gaussian_densities.m
@@ -1,6 +1,6 @@
function IncrementalWeights = gaussian_densities(obs,mut_t,sqr_Pss_t_t,st_t_1,sqr_Pss_t_t_1,particles,H,normconst,weigths1,weigths2,ReducedForm,ThreadsOptions)
%
-% Elements to calculate the importance sampling ratio
+% Elements to calculate the importance sampling ratio
%
% INPUTS
% reduced_form_model [structure] Matlab's structure describing the reduced form model.
@@ -36,11 +36,11 @@ function IncrementalWeights = gaussian_densities(obs,mut_t,sqr_Pss_t_t,st_t_1,sq
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-% proposal density
-proposal = probability2(mut_t,sqr_Pss_t_t,particles) ;
-% prior density
-prior = probability2(st_t_1,sqr_Pss_t_t_1,particles) ;
-% likelihood
+% proposal density
+proposal = probability2(mut_t,sqr_Pss_t_t,particles) ;
+% prior density
+prior = probability2(st_t_1,sqr_Pss_t_t_1,particles) ;
+% likelihood
yt_t_1_i = measurement_equations(particles,ReducedForm,ThreadsOptions) ;
eta_t_i = bsxfun(@minus,obs,yt_t_1_i)' ;
yt_t_1 = sum(yt_t_1_i*weigths1,2) ;
@@ -48,5 +48,5 @@ tmp = bsxfun(@minus,yt_t_1_i,yt_t_1) ;
Pyy = bsxfun(@times,weigths2',tmp)*tmp' + H ;
sqr_det = sqrt(det(Pyy)) ;
foo = (eta_t_i/Pyy).*eta_t_i ;
-likelihood = exp(-0.5*sum(foo,2))/(normconst*sqr_det) + 1e-99 ;
+likelihood = exp(-0.5*sum(foo,2))/(normconst*sqr_det) + 1e-99 ;
IncrementalWeights = likelihood.*prior./proposal ;
diff --git a/src/gaussian_filter.m b/src/gaussian_filter.m
index 2cee18e..d7e82df 100644
--- a/src/gaussian_filter.m
+++ b/src/gaussian_filter.m
@@ -112,18 +112,18 @@ for t=1:sample_size
if ParticleOptions.distribution_approximation.cubature || ParticleOptions.distribution_approximation.unscented
StateParticles = bsxfun(@plus,StateVectorMean,StateVectorVarianceSquareRoot*nodes2') ;
IncrementalWeights = ...
- gaussian_densities(Y(:,t),StateVectorMean,...
- StateVectorVarianceSquareRoot,PredictedStateMean,...
- PredictedStateVarianceSquareRoot,StateParticles,H,const_lik,...
- weights2,weights_c2,ReducedForm,ThreadsOptions) ;
+ gaussian_densities(Y(:,t),StateVectorMean,...
+ StateVectorVarianceSquareRoot,PredictedStateMean,...
+ PredictedStateVarianceSquareRoot,StateParticles,H,const_lik,...
+ weights2,weights_c2,ReducedForm,ThreadsOptions) ;
SampleWeights = weights2.*IncrementalWeights ;
- else
+ else
StateParticles = bsxfun(@plus,StateVectorVarianceSquareRoot*randn(state_variance_rank,number_of_particles),StateVectorMean) ;
IncrementalWeights = ...
- gaussian_densities(Y(:,t),StateVectorMean,...
- StateVectorVarianceSquareRoot,PredictedStateMean,...
- PredictedStateVarianceSquareRoot,StateParticles,H,const_lik,...
- 1/number_of_particles,1/number_of_particles,ReducedForm,ThreadsOptions) ;
+ gaussian_densities(Y(:,t),StateVectorMean,...
+ StateVectorVarianceSquareRoot,PredictedStateMean,...
+ PredictedStateVarianceSquareRoot,StateParticles,H,const_lik,...
+ 1/number_of_particles,1/number_of_particles,ReducedForm,ThreadsOptions) ;
SampleWeights = IncrementalWeights/number_of_particles ;
end
SampleWeights = SampleWeights + 1e-6*ones(size(SampleWeights,1),1) ;
@@ -132,9 +132,9 @@ for t=1:sample_size
SampleWeights = SampleWeights./SumSampleWeights ;
if not(ParticleOptions.distribution_approximation.cubature || ParticleOptions.distribution_approximation.unscented)
if (ParticleOptions.resampling.status.generic && neff(SampleWeights)<ParticleOptions.resampling.threshold*sample_size) || ParticleOptions.resampling.status.systematic
- StateParticles = resample(StateParticles',SampleWeights,ParticleOptions)' ;
+ StateParticles = resample(StateParticles',SampleWeights,ParticleOptions)' ;
SampleWeights = ones(number_of_particles,1)/number_of_particles;
- end
+ end
end
StateVectorMean = StateParticles*SampleWeights ;
temp = bsxfun(@minus,StateParticles,StateVectorMean) ;
diff --git a/src/gaussian_filter_bank.m b/src/gaussian_filter_bank.m
index f758973..3e9deb3 100644
--- a/src/gaussian_filter_bank.m
+++ b/src/gaussian_filter_bank.m
@@ -49,8 +49,8 @@ ghuu = ReducedForm.ghuu;
ghxu = ReducedForm.ghxu;
if any(any(isnan(ghx))) || any(any(isnan(ghu))) || any(any(isnan(ghxx))) || any(any(isnan(ghuu))) || any(any(isnan(ghxu))) || ...
- any(any(isinf(ghx))) || any(any(isinf(ghu))) || any(any(isinf(ghxx))) || any(any(isinf(ghuu))) || any(any(isinf(ghxu))) ...
- any(any(abs(ghx)>1e4)) || any(any(abs(ghu)>1e4)) || any(any(abs(ghxx)>1e4)) || any(any(abs(ghuu)>1e4)) || any(any(abs(ghxu)>1e4))
+ any(any(isinf(ghx))) || any(any(isinf(ghu))) || any(any(isinf(ghxx))) || any(any(isinf(ghuu))) || any(any(isinf(ghxu))) ...
+ any(any(abs(ghx)>1e4)) || any(any(abs(ghu)>1e4)) || any(any(abs(ghxx)>1e4)) || any(any(abs(ghuu)>1e4)) || any(any(abs(ghxu)>1e4))
ghx
ghu
ghxx
diff --git a/src/gaussian_mixture_densities.m b/src/gaussian_mixture_densities.m
index 06c3eec..7efe1ce 100644
--- a/src/gaussian_mixture_densities.m
+++ b/src/gaussian_mixture_densities.m
@@ -1,8 +1,8 @@
function IncrementalWeights = gaussian_mixture_densities(obs,StateMuPrior,StateSqrtPPrior,StateWeightsPrior,...
- StateMuPost,StateSqrtPPost,StateWeightsPost,...
- StateParticles,H,normconst,weigths1,weigths2,ReducedForm,ThreadsOptions)
+ StateMuPost,StateSqrtPPost,StateWeightsPost,...
+ StateParticles,H,normconst,weigths1,weigths2,ReducedForm,ThreadsOptions)
%
-% Elements to calculate the importance sampling ratio
+% Elements to calculate the importance sampling ratio
%
% INPUTS
% reduced_form_model [structure] Matlab's structure describing the reduced form model.
@@ -38,11 +38,11 @@ function IncrementalWeights = gaussian_mixture_densities(obs,StateMuPrior,State
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-% Compute the density of particles under the prior distribution
-[ras,ras,prior] = probability(StateMuPrior,StateSqrtPPrior,StateWeightsPrior,StateParticles) ;
+% Compute the density of particles under the prior distribution
+[ras,ras,prior] = probability(StateMuPrior,StateSqrtPPrior,StateWeightsPrior,StateParticles) ;
prior = prior' ;
-% Compute the density of particles under the proposal distribution
-[ras,ras,proposal] = probability(StateMuPost,StateSqrtPPost,StateWeightsPost,StateParticles) ;
+% Compute the density of particles under the proposal distribution
+[ras,ras,proposal] = probability(StateMuPost,StateSqrtPPost,StateWeightsPost,StateParticles) ;
proposal = proposal' ;
% Compute the density of the current observation conditionally to each particle
yt_t_1_i = measurement_equations(StateParticles,ReducedForm,ThreadsOptions) ;
@@ -52,6 +52,5 @@ tmp = bsxfun(@minus,yt_t_1_i,yt_t_1) ;
Pyy = bsxfun(@times,weigths2',tmp)*tmp' + H ;
sqr_det = sqrt(det(Pyy)) ;
foo = (eta_t_i/Pyy).*eta_t_i ;
-likelihood = exp(-0.5*sum(foo,2))/(normconst*sqr_det) + 1e-99 ;
+likelihood = exp(-0.5*sum(foo,2))/(normconst*sqr_det) + 1e-99 ;
IncrementalWeights = likelihood.*prior./proposal ;
-
diff --git a/src/gaussian_mixture_filter.m b/src/gaussian_mixture_filter.m
index 0dc257c..eaab007 100644
--- a/src/gaussian_mixture_filter.m
+++ b/src/gaussian_mixture_filter.m
@@ -63,15 +63,15 @@ end
% Set persistent variables.
if isempty(init_flag)
- mf0 = ReducedForm.mf0;
- mf1 = ReducedForm.mf1;
- sample_size = size(Y,2);
- number_of_state_variables = length(mf0);
- number_of_observed_variables = length(mf1);
- number_of_structural_innovations = length(ReducedForm.Q);
- G = ParticleOptions.mixture_state_variables; % number of GM components in state
- number_of_particles = ParticleOptions.number_of_particles;
- init_flag = 1;
+ mf0 = ReducedForm.mf0;
+ mf1 = ReducedForm.mf1;
+ sample_size = size(Y,2);
+ number_of_state_variables = length(mf0);
+ number_of_observed_variables = length(mf1);
+ number_of_structural_innovations = length(ReducedForm.Q);
+ G = ParticleOptions.mixture_state_variables; % number of GM components in state
+ number_of_particles = ParticleOptions.number_of_particles;
+ init_flag = 1;
end
% compute gaussian quadrature nodes and weights on states and shocks
@@ -104,14 +104,14 @@ else
end
Q_lower_triangular_cholesky = reduced_rank_cholesky(Q)';
-% Initialize mixtures
+% Initialize mixtures
StateWeights = ones(1,G)/G ;
StateMu = ReducedForm.StateVectorMean ;
StateSqrtP = zeros(number_of_state_variables,number_of_state_variables,G) ;
temp = reduced_rank_cholesky(ReducedForm.StateVectorVariance)' ;
StateMu = bsxfun(@plus,StateMu,bsxfun(@times,diag(temp),(-(G-1)/2:1:(G-1)/2))/10) ;
for g=1:G
- StateSqrtP(:,:,g) = temp/sqrt(G) ;
+ StateSqrtP(:,:,g) = temp/sqrt(G) ;
end
% if ParticleOptions.mixture_structural_shocks==1
@@ -135,11 +135,11 @@ end
% for i=1:I
% StructuralShocksSqrtP(:,:,i) = Q_lower_triangular_cholesky/sqrt(StructuralShocksWeights(i)) ;
% end
-%
-% if ParticleOptions.mixture_measurement_shocks==1
+%
+% if ParticleOptions.mixture_measurement_shocks==1
% ObservationShocksMu = zeros(1,number_of_observed_variables) ;
% ObservationShocksWeights = 1 ;
-% else
+% else
% if ParticleOptions.proposal_approximation.cubature
% [ObservationShocksMu,ObservationShocksWeights] = spherical_radial_sigma_points(number_of_observed_variables);
% ObservationShocksWeights = ones(size(ObservationShocksMu,1),1)*ObservationShocksWeights;
@@ -150,7 +150,7 @@ end
% error('Estimation: This approximation for the proposal is not implemented or unknown!')
% end
% end
-% end
+% end
% J = size(ObservationShocksWeights,1) ;
% ObservationShocksMu = H_lower_triangular_cholesky*(ObservationShocksMu') ;
% ObservationShocksSqrtP = zeros(number_of_observed_variables,number_of_observed_variables,J) ;
@@ -180,7 +180,7 @@ elseif ParticleOptions.mixture_structural_shocks==1
StructuralShocksMu = Q_lower_triangular_cholesky*(StructuralShocksMu') ;
StructuralShocksSqrtP = zeros(number_of_structural_innovations,number_of_structural_innovations,I) ;
for i=1:I
- StructuralShocksSqrtP(:,:,i) = Q_lower_triangular_cholesky ;
+ StructuralShocksSqrtP(:,:,i) = Q_lower_triangular_cholesky ;
end
else
if ParticleOptions.proposal_approximation.cubature
@@ -197,7 +197,7 @@ else
StructuralShocksMu = Q_lower_triangular_cholesky*(StructuralShocksMu') ;
StructuralShocksSqrtP = zeros(number_of_structural_innovations,number_of_structural_innovations,I) ;
for i=1:I
- StructuralShocksSqrtP(:,:,i) = Q_lower_triangular_cholesky/sqrt(StructuralShocksWeights(i)) ;
+ StructuralShocksSqrtP(:,:,i) = Q_lower_triangular_cholesky/sqrt(StructuralShocksWeights(i)) ;
end
end
@@ -208,14 +208,14 @@ ObservationShocksMu = H_lower_triangular_cholesky*(ObservationShocksMu') ;
ObservationShocksSqrtP = zeros(number_of_observed_variables,number_of_observed_variables,J) ;
ObservationShocksSqrtP(:,:,1) = H_lower_triangular_cholesky ;
-% if ParticleOptions.mixture_measurement_shocks==0
+% if ParticleOptions.mixture_measurement_shocks==0
% ObservationShocksMu = zeros(1,number_of_observed_variables) ;
% ObservationShocksWeights = 1 ;
% J = 1 ;
% ObservationShocksMu = H_lower_triangular_cholesky*(ObservationShocksMu') ;
% ObservationShocksSqrtP = zeros(number_of_observed_variables,number_of_observed_variables,J) ;
% ObservationShocksSqrtP(:,:,1) = H_lower_triangular_cholesky ;
-% elseif ParticleOptions.mixture_measurement_shocks==1
+% elseif ParticleOptions.mixture_measurement_shocks==1
% if ParticleOptions.proposal_approximation.cubature
% [ObservationShocksMu,ObservationShocksWeights] = spherical_radial_sigma_points(number_of_observed_variables);
% ObservationShocksWeights = ones(size(ObservationShocksMu,1),1)*ObservationShocksWeights;
@@ -232,7 +232,7 @@ ObservationShocksSqrtP(:,:,1) = H_lower_triangular_cholesky ;
% for j=1:J
% ObservationShocksSqrtP(:,:,j) = H_lower_triangular_cholesky ;
% end
-% else
+% else
% if ParticleOptions.proposal_approximation.cubature
% [ObservationShocksMu,ObservationShocksWeights] = spherical_radial_sigma_points(number_of_observed_variables);
% ObservationShocksWeights = ones(size(ObservationShocksMu,1),1)*ObservationShocksWeights;
@@ -277,10 +277,10 @@ for t=1:sample_size
gsecond = gprime + (j-1)*Gprime ;
[StateMuPrior(:,gprime),StateSqrtPPrior(:,:,gprime),StateWeightsPrior(1,gprime),...
StateMuPost(:,gsecond),StateSqrtPPost(:,:,gsecond),StateWeightsPost(1,gsecond)] =...
- gaussian_mixture_filter_bank(ReducedForm,Y(:,t),StateMu(:,g),StateSqrtP(:,:,g),StateWeights(g),...
- StructuralShocksMu(:,i),StructuralShocksSqrtP(:,:,i),StructuralShocksWeights(i),...
- ObservationShocksMu(:,j),ObservationShocksSqrtP(:,:,j),ObservationShocksWeights(j),...
- H,H_lower_triangular_cholesky,const_lik,ParticleOptions,ThreadsOptions) ;
+ gaussian_mixture_filter_bank(ReducedForm,Y(:,t),StateMu(:,g),StateSqrtP(:,:,g),StateWeights(g),...
+ StructuralShocksMu(:,i),StructuralShocksSqrtP(:,:,i),StructuralShocksWeights(i),...
+ ObservationShocksMu(:,j),ObservationShocksSqrtP(:,:,j),ObservationShocksWeights(j),...
+ H,H_lower_triangular_cholesky,const_lik,ParticleOptions,ThreadsOptions) ;
end
end
end
@@ -293,8 +293,8 @@ for t=1:sample_size
for i=1:Gsecond
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) ;
+ StateMuPost,StateSqrtPPost,StateWeightsPost,...
+ StateParticles,H,const_lik,weights,weights_c,ReducedForm,ThreadsOptions) ;
SampleWeights(i) = sum(StateWeightsPost(i)*weights.*IncrementalWeights) ;
end
SumSampleWeights = sum(SampleWeights) ;
@@ -311,9 +311,9 @@ for t=1:sample_size
% Sample particle in the proposal distribution, ie the posterior state GM
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) ;
+ StateMuPost,StateSqrtPPost,StateWeightsPost,...
+ StateParticles,H,const_lik,1/number_of_particles,...
+ 1/number_of_particles,ReducedForm,ThreadsOptions) ;
SampleWeights = IncrementalWeights/number_of_particles ;
SumSampleWeights = sum(SampleWeights,1) ;
SampleWeights = SampleWeights./SumSampleWeights ;
diff --git a/src/gaussian_mixture_filter_bank.m b/src/gaussian_mixture_filter_bank.m
index 5bcf702..0171927 100644
--- a/src/gaussian_mixture_filter_bank.m
+++ b/src/gaussian_mixture_filter_bank.m
@@ -1,12 +1,12 @@
function [StateMuPrior,StateSqrtPPrior,StateWeightsPrior,StateMuPost,StateSqrtPPost,StateWeightsPost] =...
- gaussian_mixture_filter_bank(ReducedForm,obs,StateMu,StateSqrtP,StateWeights,...
- StructuralShocksMu,StructuralShocksSqrtP,StructuralShocksWeights,...
- ObservationShocksMu,ObservationShocksSqrtP,ObservationShocksWeights,...
- H,H_lower_triangular_cholesky,normfactO,ParticleOptions,ThreadsOptions)
+ gaussian_mixture_filter_bank(ReducedForm,obs,StateMu,StateSqrtP,StateWeights,...
+ StructuralShocksMu,StructuralShocksSqrtP,StructuralShocksWeights,...
+ ObservationShocksMu,ObservationShocksSqrtP,ObservationShocksWeights,...
+ H,H_lower_triangular_cholesky,normfactO,ParticleOptions,ThreadsOptions)
%
% Computes the proposal with a gaussian approximation for importance
-% sampling
-% This proposal is a gaussian distribution calculated à la Kalman
+% sampling
+% This proposal is a gaussian distribution calculated à la Kalman
%
% INPUTS
% reduced_form_model [structure] Matlab's structure describing the reduced form model.
@@ -43,8 +43,8 @@ function [StateMuPrior,StateSqrtPPrior,StateWeightsPrior,StateMuPost,StateSqrtPP
persistent init_flag2 mf0 mf1 %nodes3 weights3 weights_c3
-persistent number_of_state_variables number_of_observed_variables
-persistent number_of_structural_innovations
+persistent number_of_state_variables number_of_observed_variables
+persistent number_of_structural_innovations
% Set local state space model (first-order approximation).
ghx = ReducedForm.ghx;
@@ -55,8 +55,8 @@ ghuu = ReducedForm.ghuu;
ghxu = ReducedForm.ghxu;
if any(any(isnan(ghx))) || any(any(isnan(ghu))) || any(any(isnan(ghxx))) || any(any(isnan(ghuu))) || any(any(isnan(ghxu))) || ...
- any(any(isinf(ghx))) || any(any(isinf(ghu))) || any(any(isinf(ghxx))) || any(any(isinf(ghuu))) || any(any(isinf(ghxu))) ...
- any(any(abs(ghx)>1e4)) || any(any(abs(ghu)>1e4)) || any(any(abs(ghxx)>1e4)) || any(any(abs(ghuu)>1e4)) || any(any(abs(ghxu)>1e4))
+ any(any(isinf(ghx))) || any(any(isinf(ghu))) || any(any(isinf(ghxx))) || any(any(isinf(ghuu))) || any(any(isinf(ghxu))) ...
+ any(any(abs(ghx)>1e4)) || any(any(abs(ghu)>1e4)) || any(any(abs(ghxx)>1e4)) || any(any(abs(ghuu)>1e4)) || any(any(abs(ghxu)>1e4))
ghx
ghu
ghxx
diff --git a/src/importance_sampling.m b/src/importance_sampling.m
index 403b6be..b4a6daf 100644
--- a/src/importance_sampling.m
+++ b/src/importance_sampling.m
@@ -17,13 +17,12 @@ function State_Particles = importance_sampling(StateMuPost,StateSqrtPPost,StateW
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-[Xdim,Gsecond] = size(StateMuPost) ;
+[Xdim,Gsecond] = size(StateMuPost) ;
u = rand(numP,1);
-[Nc,comp] = histc(u, cumsum([0; StateWeightsPost]));
+[Nc,comp] = histc(u, cumsum([0; StateWeightsPost]));
State_Particles = zeros(Xdim,numP);
for k=1:Gsecond
- idx = bsxfun(@eq,comp,k*ones(size(comp))) ;
- State_Particles(:,idx) = StateSqrtPPost(:,:,k)*randn(Xdim,Nc(k));
+ idx = bsxfun(@eq,comp,k*ones(size(comp))) ;
+ State_Particles(:,idx) = StateSqrtPPost(:,:,k)*randn(Xdim,Nc(k));
end
-State_Particles= State_Particles + StateMuPost(:,comp);
-
+State_Particles= State_Particles + StateMuPost(:,comp);
diff --git a/src/measurement_equations.m b/src/measurement_equations.m
index d38c59a..7cf4d93 100644
--- a/src/measurement_equations.m
+++ b/src/measurement_equations.m
@@ -1,4 +1,4 @@
-function measure = measurement_equations(StateVectors,ReducedForm,ThreadsOptions)
+function measure = measurement_equations(StateVectors,ReducedForm,ThreadsOptions)
% Copyright (C) 2013 Dynare Team
%
@@ -28,6 +28,3 @@ state_variables_steady_state = ReducedForm.state_variables_steady_state;
number_of_structural_innovations = length(ReducedForm.Q);
yhat = bsxfun(@minus,StateVectors,state_variables_steady_state) ;
measure = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,size(yhat,2)),ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
-
-
-
diff --git a/src/multivariate_smooth_resampling.m b/src/multivariate_smooth_resampling.m
index 09ca287..e62111c 100644
--- a/src/multivariate_smooth_resampling.m
+++ b/src/multivariate_smooth_resampling.m
@@ -63,10 +63,10 @@ number_of_states = size(particles,2);
[P,D] = eig(particles'*(bsxfun(@times,1/number_of_particles,particles))) ;
D = diag(D) ;
vectors = bsxfun(@times,P,sqrt(D)') ;
-orthogonalized_particles = bsxfun(@rdivide,particles*vectors,D') ;
+orthogonalized_particles = bsxfun(@rdivide,particles*vectors,D') ;
new_particles = zeros(number_of_particles,number_of_states) ;
for j=1:number_of_states
- tout = sortrows( [orthogonalized_particles(:,j) weights],1) ;
- new_particles(:,j) = univariate_smooth_resampling(tout(:,2),tout(:,1),number_of_particles) ;
+ tout = sortrows( [orthogonalized_particles(:,j) weights],1) ;
+ new_particles(:,j) = univariate_smooth_resampling(tout(:,2),tout(:,1),number_of_particles) ;
end
new_particles = new_particles*(vectors') ;
diff --git a/src/mykmeans.m b/src/mykmeans.m
index 56af539..4506aca 100644
--- a/src/mykmeans.m
+++ b/src/mykmeans.m
@@ -1,4 +1,4 @@
-function [c,SqrtVariance,Weights] = mykmeans(x,g,init,cod)
+function [c,SqrtVariance,Weights] = mykmeans(x,g,init,cod)
% Copyright (C) 2013 Dynare Team
%
@@ -20,34 +20,34 @@ function [c,SqrtVariance,Weights] = mykmeans(x,g,init,cod)
[n,m] = size(x) ;
indold = zeros(1,m) ;
if cod==0
- d = transpose(sum(bsxfun(@power,bsxfun(@minus,x,mean(x)),2)));
- d = sortrows( [transpose(1:m) d],2) ;
- d = d((1+(0:1:g-1))*m/g,1) ;
- c = x(:,d);
+ d = transpose(sum(bsxfun(@power,bsxfun(@minus,x,mean(x)),2)));
+ d = sortrows( [transpose(1:m) d],2) ;
+ d = d((1+(0:1:g-1))*m/g,1) ;
+ c = x(:,d);
else
- c = init ;
-end
-for iter=1:300
- dist = zeros(g,m) ;
- for i=1:g
- dist(i,:) = sum(bsxfun(@power,bsxfun(@minus,x,c(:,i)),2));
- end
- [rien,ind] = min(dist) ;
- if isequal(ind,indold)
- break ;
- end
- indold = ind ;
- for i=1:g
- lin = bsxfun(@eq,ind,i.*ones(1,m)) ;
- h = x(:,lin) ;
- c(:,i) = mean(h,2) ;
- end
+ c = init ;
end
-SqrtVariance = zeros(n,n,g) ;
-Weights = zeros(1,g) ;
+for iter=1:300
+ dist = zeros(g,m) ;
+ for i=1:g
+ dist(i,:) = sum(bsxfun(@power,bsxfun(@minus,x,c(:,i)),2));
+ end
+ [rien,ind] = min(dist) ;
+ if isequal(ind,indold)
+ break ;
+ end
+ indold = ind ;
+ for i=1:g
+ lin = bsxfun(@eq,ind,i.*ones(1,m)) ;
+ h = x(:,lin) ;
+ c(:,i) = mean(h,2) ;
+ end
+end
+SqrtVariance = zeros(n,n,g) ;
+Weights = zeros(1,g) ;
for i=1:g
- temp = x(:,bsxfun(@eq,ind,i*ones(1,m))) ;
- u = bsxfun(@minus,temp,mean(temp,2)); %temp-mean(temp,1)' ;
- SqrtVariance(:,:,i) = chol( (u*u')/size(temp,2) )' ;
- Weights(i) = size(temp,2)/m ;
+ temp = x(:,bsxfun(@eq,ind,i*ones(1,m))) ;
+ u = bsxfun(@minus,temp,mean(temp,2)); %temp-mean(temp,1)' ;
+ SqrtVariance(:,:,i) = chol( (u*u')/size(temp,2) )' ;
+ Weights(i) = size(temp,2)/m ;
end
diff --git a/src/nonlinear_kalman_filter.m b/src/nonlinear_kalman_filter.m
index dd61a5e..608fbac 100644
--- a/src/nonlinear_kalman_filter.m
+++ b/src/nonlinear_kalman_filter.m
@@ -10,9 +10,9 @@ function [LIK,lik] = nonlinear_kalman_filter(ReducedForm, Y, start, ParticleOpti
% from the resulting nodes/particles and allows to generate new distributions at the
% following observation.
% Pros: The use of nodes is much faster than Monte-Carlo Gaussian particle and standard particles
-% filters since it treats a lesser number of particles.
-% Cons: 1. Application a linear projection formulae in a nonlinear context.
-% 2. Parameter estimations may be biaised if the model is truly non-gaussian since predictive and
+% filters since it treats a lesser number of particles.
+% Cons: 1. Application a linear projection formulae in a nonlinear context.
+% 2. Parameter estimations may be biaised if the model is truly non-gaussian since predictive and
% filtered densities are unimodal.
%
% INPUTS
@@ -47,7 +47,7 @@ function [LIK,lik] = nonlinear_kalman_filter(ReducedForm, Y, start, ParticleOpti
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-persistent init_flag mf0 mf1 nodes weights weights_c
+persistent init_flag mf0 mf1 nodes weights weights_c
persistent sample_size number_of_state_variables number_of_observed_variables number_of_structural_innovations
% Set default
@@ -64,8 +64,8 @@ ghuu = ReducedForm.ghuu;
ghxu = ReducedForm.ghxu;
if any(any(isnan(ghx))) || any(any(isnan(ghu))) || any(any(isnan(ghxx))) || any(any(isnan(ghuu))) || any(any(isnan(ghxu))) || ...
- any(any(isinf(ghx))) || any(any(isinf(ghu))) || any(any(isinf(ghxx))) || any(any(isinf(ghuu))) || any(any(isinf(ghxu))) ...
- any(any(abs(ghx)>1e4)) || any(any(abs(ghu)>1e4)) || any(any(abs(ghxx)>1e4)) || any(any(abs(ghuu)>1e4)) || any(any(abs(ghxu)>1e4))
+ any(any(isinf(ghx))) || any(any(isinf(ghu))) || any(any(isinf(ghxx))) || any(any(isinf(ghuu))) || any(any(isinf(ghxu))) ...
+ any(any(abs(ghx)>1e4)) || any(any(abs(ghu)>1e4)) || any(any(abs(ghxx)>1e4)) || any(any(abs(ghuu)>1e4)) || any(any(abs(ghxu)>1e4))
ghx
ghu
ghxx
@@ -100,12 +100,12 @@ end
if ParticleOptions.distribution_approximation.montecarlo
set_dynare_seed('default');
-end
+end
% Get covariance matrices
H = ReducedForm.H;
-H_lower_triangular_cholesky = chol(H)' ;
-Q_lower_triangular_cholesky = chol(ReducedForm.Q)';
+H_lower_triangular_cholesky = chol(H)' ;
+Q_lower_triangular_cholesky = chol(ReducedForm.Q)';
% Get initial condition for the state vector.
StateVectorMean = ReducedForm.StateVectorMean;
diff --git a/src/online_auxiliary_filter.m b/src/online_auxiliary_filter.m
index 8018ca6..a145485 100644
--- a/src/online_auxiliary_filter.m
+++ b/src/online_auxiliary_filter.m
@@ -1,5 +1,5 @@
function [xparam,std_param,lb_95,ub_95,median_param] = online_auxiliary_filter(xparam1,DynareDataset,dataset_info,DynareOptions,Model,EstimatedParameters,BayesInfo,bounds,DynareResults)
-
+
% Liu & West particle filter = auxiliary particle filter including Liu & West filter on parameters.
%
% INPUTS
@@ -47,8 +47,8 @@ second_resample = 0 ;
variance_update = 1 ;
% initialization of state particles
-[ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults,ReducedForm] = ...
- solve_model_for_online_filter(1,xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults) ;
+[ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults,ReducedForm] = ...
+ solve_model_for_online_filter(1,xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults) ;
% Set persistent variables.
if isempty(init_flag)
@@ -61,11 +61,11 @@ if isempty(init_flag)
number_of_observed_variables = length(mf1);
number_of_structural_innovations = length(ReducedForm.Q);
liu_west_delta = DynareOptions.particle.liu_west_delta ;
- start_param = xparam1 ;
+ start_param = xparam1 ;
init_flag = 1;
end
-% Get initial conditions for the state particles
+% Get initial conditions for the state particles
StateVectorMean = ReducedForm.StateVectorMean;
StateVectorVarianceSquareRoot = chol(ReducedForm.StateVectorVariance)';
state_variance_rank = size(StateVectorVarianceSquareRoot,2);
@@ -73,13 +73,13 @@ StateVectors = bsxfun(@plus,StateVectorVarianceSquareRoot*randn(state_variance_r
if pruning
StateVectors_ = StateVectors;
end
-
-% parameters for the Liu & West filter
+
+% parameters for the Liu & West filter
h_square = (3*liu_west_delta-1)/(2*liu_west_delta) ;
h_square = 1-h_square*h_square ;
small_a = sqrt(1-h_square) ;
-% Initialization of parameter particles
+% Initialization of parameter particles
xparam = zeros(number_of_parameters,number_of_particles) ;
stderr = sqrt(bsxfun(@power,bounds.ub-bounds.lb,2)/12)/100 ;
stderr = sqrt(bsxfun(@power,bounds.ub-bounds.lb,2)/12)/50 ;
@@ -107,14 +107,14 @@ std_xparam = zeros(number_of_parameters,sample_size) ;
lb95_xparam = zeros(number_of_parameters,sample_size) ;
ub95_xparam = zeros(number_of_parameters,sample_size) ;
-%% The Online filter
+%% The Online filter
for t=1:sample_size
if t>1
fprintf('\nSubsample with %s first observations.\n\n', int2str(t))
else
fprintf('\nSubsample with only the first observation.\n\n', int2str(t))
end
- % Moments of parameters particles distribution
+ % Moments of parameters particles distribution
m_bar = xparam*(weights') ;
temp = bsxfun(@minus,xparam,m_bar) ;
sigma_bar = (bsxfun(@times,weights,temp))*(temp') ;
@@ -124,8 +124,8 @@ for t=1:sample_size
% Prediction (without shocks)
tau_tilde = zeros(1,number_of_particles) ;
for i=1:number_of_particles
- % model resolution
- [ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults,ReducedForm] = ...
+ % model resolution
+ [ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults,ReducedForm] = ...
solve_model_for_online_filter(t,xparam(:,i),DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults) ;
steadystate = ReducedForm.steadystate;
state_variables_steady_state = ReducedForm.state_variables_steady_state;
@@ -136,7 +136,7 @@ for t=1:sample_size
ghxx = ReducedForm.ghxx;
ghuu = ReducedForm.ghuu;
ghxu = ReducedForm.ghxu;
- % particle likelihood contribution
+ % particle likelihood contribution
yhat = bsxfun(@minus,StateVectors(:,i),state_variables_steady_state);
if pruning
yhat_ = bsxfun(@minus,StateVectors_(:,i),state_variables_steady_state);
@@ -151,7 +151,7 @@ for t=1:sample_size
tau_tilde(i) = weights(i).*(tpdf(z,3*ones(size(z)))+1e-99) ;
%tau_tilde(i) = weights(i).*exp(-.5*(const_lik+log(det(ReducedForm.H))+sum(PredictionError.*(ReducedForm.H\PredictionError),1))) ;
end
- % particles selection
+ % particles selection
tau_tilde = tau_tilde/sum(tau_tilde) ;
indx = resample(0,tau_tilde',DynareOptions.particle);
StateVectors = StateVectors(:,indx) ;
@@ -160,7 +160,7 @@ for t=1:sample_size
end
xparam = bsxfun(@plus,(1-small_a).*m_bar,small_a.*xparam(:,indx)) ;
w_stage1 = weights(indx)./tau_tilde(indx) ;
- % draw in the new distributions
+ % draw in the new distributions
wtilde = zeros(1,number_of_particles) ;
i = 1 ;
while i<=number_of_particles
@@ -179,9 +179,9 @@ for t=1:sample_size
ghxx = ReducedForm.ghxx;
ghuu = ReducedForm.ghuu;
ghxu = ReducedForm.ghxu;
- % Get covariance matrices and structural shocks
+ % Get covariance matrices and structural shocks
epsilon = chol(ReducedForm.Q)'*randn(number_of_structural_innovations,1) ;
- % compute particles likelihood contribution
+ % compute particles likelihood contribution
yhat = bsxfun(@minus,StateVectors(:,i),state_variables_steady_state);
if pruning
yhat_ = bsxfun(@minus,StateVectors_(:,i),state_variables_steady_state);
@@ -196,13 +196,13 @@ for t=1:sample_size
i = i+1 ;
end
end
- % normalization
+ % normalization
weights = wtilde/sum(wtilde);
if (variance_update==1) && (neff(weights)<DynareOptions.particle.resampling.threshold*sample_size)
variance_update = 0 ;
end
% final resampling (not advised)
- if second_resample==1
+ if second_resample==1
indx = resample(0,weights,DynareOptions.particle);
StateVectors = StateVectors(:,indx) ;
if pruning
@@ -212,13 +212,13 @@ for t=1:sample_size
weights = ones(1,number_of_particles)/number_of_particles ;
mean_xparam(:,t) = mean(xparam,2);
mat_var_cov = bsxfun(@minus,xparam,mean_xparam(:,t)) ;
- mat_var_cov = (mat_var_cov*mat_var_cov')/(number_of_particles-1) ;
+ mat_var_cov = (mat_var_cov*mat_var_cov')/(number_of_particles-1) ;
std_xparam(:,t) = sqrt(diag(mat_var_cov)) ;
for i=1:number_of_parameters
- temp = sortrows(xparam(i,:)') ;
- lb95_xparam(i,t) = temp(0.025*number_of_particles) ;
- median_xparam(i,t) = temp(0.5*number_of_particles) ;
- ub95_xparam(i,t) = temp(0.975*number_of_particles) ;
+ temp = sortrows(xparam(i,:)') ;
+ lb95_xparam(i,t) = temp(0.025*number_of_particles) ;
+ median_xparam(i,t) = temp(0.5*number_of_particles) ;
+ ub95_xparam(i,t) = temp(0.975*number_of_particles) ;
end
end
if second_resample==0
@@ -227,25 +227,25 @@ for t=1:sample_size
mat_var_cov = mat_var_cov*(bsxfun(@times,mat_var_cov,weights)') ;
std_xparam(:,t) = sqrt(diag(mat_var_cov)) ;
for i=1:number_of_parameters
- temp = sortrows([xparam(i,:)' weights'],1) ;
- cumulated_weights = cumsum(temp(:,2)) ;
- pass1=1 ;
- pass2=1 ;
- pass3=1 ;
- for j=1:number_of_particles
- if cumulated_weights(j)>=0.025 && pass1==1
- lb95_xparam(i,t) = temp(j,1) ;
- pass1 = 2 ;
- end
- if cumulated_weights(j)>=0.5 && pass2==1
- median_xparam(i,t) = temp(j,1) ;
- pass2 = 2 ;
- end
- if cumulated_weights(j)>=0.975 && pass3==1
- ub95_xparam(i,t) = temp(j,1) ;
- pass3 = 2 ;
- end
- end
+ temp = sortrows([xparam(i,:)' weights'],1) ;
+ cumulated_weights = cumsum(temp(:,2)) ;
+ pass1=1 ;
+ pass2=1 ;
+ pass3=1 ;
+ for j=1:number_of_particles
+ if cumulated_weights(j)>=0.025 && pass1==1
+ lb95_xparam(i,t) = temp(j,1) ;
+ pass1 = 2 ;
+ end
+ if cumulated_weights(j)>=0.5 && pass2==1
+ median_xparam(i,t) = temp(j,1) ;
+ pass2 = 2 ;
+ end
+ if cumulated_weights(j)>=0.975 && pass3==1
+ ub95_xparam(i,t) = temp(j,1) ;
+ pass3 = 2 ;
+ end
+ end
end
end
str = sprintf(' Lower Bound (95%%) \t Mean \t\t\t Upper Bound (95%%)');
@@ -262,7 +262,7 @@ lb_95 = lb95_xparam(:,sample_size) ;
ub_95 = ub95_xparam(:,sample_size) ;
median_param = median_xparam(:,sample_size) ;
-%% Plot parameters trajectory
+%% Plot parameters trajectory
TeX = DynareOptions.TeX;
[nbplt,nr,nc,lr,lc,nstar] = pltorg(number_of_parameters);
@@ -322,7 +322,7 @@ end
%% Plot Parameter Densities
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.
+kernel_function = 'gaussian'; % Gaussian kernel for Fast Fourier Transform approximation.
for plt = 1:nbplt,
if TeX
NAMES = [];
@@ -366,5 +366,4 @@ for plt = 1:nbplt,
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,' \n');
end
-end
-
+end
diff --git a/src/probability.m b/src/probability.m
index 5d76e49..94f09b9 100644
--- a/src/probability.m
+++ b/src/probability.m
@@ -17,23 +17,23 @@ function [prior,likelihood,C,posterior] = probability(mu,sqrtP,prior,X)
% 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);
+[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
+ 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
likelihood = likelihood + 1e-99;
if nargout>2
- C = prior*likelihood + 1e-99;
+ C = prior*likelihood + 1e-99;
end
if nargout>3
- posterior = bsxfun(@rdivide,bsxfun(@times,prior',likelihood),C) + 1e-99 ;
- posterior = bsxfun(@rdivide,posterior,sum(posterior,1));
+ posterior = bsxfun(@rdivide,bsxfun(@times,prior',likelihood),C) + 1e-99 ;
+ posterior = bsxfun(@rdivide,posterior,sum(posterior,1));
end
diff --git a/src/probability2.m b/src/probability2.m
index a7486d1..dfc71ff 100644
--- a/src/probability2.m
+++ b/src/probability2.m
@@ -1,7 +1,7 @@
-function [density] = probability2(mu,S,X)
+function [density] = probability2(mu,S,X)
%
% Multivariate gaussian density
-%
+%
% INPUTS
% n [integer] scalar, number of variables.
%
@@ -10,10 +10,10 @@ function [density] = probability2(mu,S,X)
% weigths [double] associated weigths
%
% REFERENCES
-%
+%
%
% NOTES
-%
+%
% Copyright (C) 2009-2012 Dynare Team
%
% This file is part of Dynare.
@@ -31,7 +31,7 @@ function [density] = probability2(mu,S,X)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-dim = size(X,1) ;
-normfact = bsxfun(@power,(2*pi),(dim/2)) ;
+dim = size(X,1) ;
+normfact = bsxfun(@power,(2*pi),(dim/2)) ;
foo = S\(bsxfun(@minus,X,mu)) ;
density = exp(-0.5*sum(foo.*foo)')./abs((normfact*prod(diag(S)))) + 1e-99 ;
\ No newline at end of file
diff --git a/src/probability3.m b/src/probability3.m
index 49a14b4..5e2a9b4 100644
--- a/src/probability3.m
+++ b/src/probability3.m
@@ -17,23 +17,23 @@ function [prior,likelihood,C,posterior] = probability3(mu,sqrtP,prior,X,X_weight
% 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);
+[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
+ 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;
+ 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));
+ posterior = bsxfun(@rdivide,bsxfun(@times,prior',wlikelihood),C) + 1e-99 ;
+ posterior = bsxfun(@rdivide,posterior,sum(posterior,1));
end
diff --git a/src/residual_resampling.m b/src/residual_resampling.m
index c2f09e3..9a7d8cc 100644
--- a/src/residual_resampling.m
+++ b/src/residual_resampling.m
@@ -76,33 +76,33 @@ iWEIGHTS = fix(WEIGHTS);
number_of_trials = number_of_particles-sum(iWEIGHTS);
if number_of_trials
- WEIGHTS = (WEIGHTS-iWEIGHTS)/number_of_trials;
- EmpiricalCDF = cumsum(WEIGHTS);
- if kitagawa_resampling
- u = (transpose(1:number_of_trials)-1+noise(:))/number_of_trials;
- else
- u = fliplr(cumprod(noise(1:number_of_trials).^(1./(number_of_trials:-1:1))));
- end
- j=1;
- for i=1:number_of_trials
- while (u(i)>EmpiricalCDF(j))
- j=j+1;
+ WEIGHTS = (WEIGHTS-iWEIGHTS)/number_of_trials;
+ EmpiricalCDF = cumsum(WEIGHTS);
+ if kitagawa_resampling
+ u = (transpose(1:number_of_trials)-1+noise(:))/number_of_trials;
+ else
+ u = fliplr(cumprod(noise(1:number_of_trials).^(1./(number_of_trials:-1:1))));
end
- iWEIGHTS(j)=iWEIGHTS(j)+1;
- if kitagawa_resampling==0
- j=1;
+ j=1;
+ for i=1:number_of_trials
+ while (u(i)>EmpiricalCDF(j))
+ j=j+1;
+ end
+ iWEIGHTS(j)=iWEIGHTS(j)+1;
+ if kitagawa_resampling==0
+ j=1;
+ end
end
- end
end
k=1;
for i=1:number_of_particles
- if (iWEIGHTS(i)>0)
- for j=k:k+iWEIGHTS(i)-1
- indx(j) = jndx(i);
+ if (iWEIGHTS(i)>0)
+ for j=k:k+iWEIGHTS(i)-1
+ indx(j) = jndx(i);
+ end
end
- end
- k = k + iWEIGHTS(i);
+ k = k + iWEIGHTS(i);
end
if particles==0
diff --git a/src/solve_model_for_online_filter.m b/src/solve_model_for_online_filter.m
index 62ed99c..1ea2e67 100644
--- a/src/solve_model_for_online_filter.m
+++ b/src/solve_model_for_online_filter.m
@@ -185,18 +185,18 @@ if EstimatedParameters.ncx
Q(k2,k1) = Q(k1,k2);
end
% Try to compute the cholesky decomposition of Q (possible iff Q is positive definite)
-% [CholQ,testQ] = chol(Q);
-% if testQ
- % The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
-% a = diag(eig(Q));
-% k = find(a < 0);
-% if k > 0
-% fval = objective_function_penalty_base+sum(-a(k));
-% exit_flag = 0;
-% info = 43;
-% return
-% end
-% end
+ % [CholQ,testQ] = chol(Q);
+ % if testQ
+ % The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
+ % a = diag(eig(Q));
+ % k = find(a < 0);
+ % if k > 0
+ % fval = objective_function_penalty_base+sum(-a(k));
+ % exit_flag = 0;
+ % info = 43;
+ % return
+ % end
+ % end
offset = offset+EstimatedParameters.ncx;
end
@@ -210,18 +210,18 @@ if EstimatedParameters.ncn
H(k2,k1) = H(k1,k2);
end
% Try to compute the cholesky decomposition of H (possible iff H is positive definite)
-% [CholH,testH] = chol(H);
-% if testH
- % The variance-covariance matrix of the measurement errors is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
-% a = diag(eig(H));
-% k = find(a < 0);
-% if k > 0
-% fval = objective_function_penalty_base+sum(-a(k));
-% exit_flag = 0;
-% info = 44;
-% return
-% end
-% end
+ % [CholH,testH] = chol(H);
+ % if testH
+ % The variance-covariance matrix of the measurement errors is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
+ % a = diag(eig(H));
+ % k = find(a < 0);
+ % if k > 0
+ % fval = objective_function_penalty_base+sum(-a(k));
+ % exit_flag = 0;
+ % info = 44;
+ % return
+ % end
+ % end
offset = offset+EstimatedParameters.ncn;
end
@@ -244,7 +244,7 @@ Model.H = H;
%disp(info)
if info(1) ~= 0
- ReducedForm = 0 ;
+ ReducedForm = 0 ;
exit_flag = 55;
return
end
@@ -312,7 +312,7 @@ if DynareOptions.order>1
ReducedForm.ghuu = dr.ghuu(restrict_variables_idx,:);
ReducedForm.ghxu = dr.ghxu(restrict_variables_idx,:);
ReducedForm.constant = ReducedForm.steadystate + .5*dr.ghs2(restrict_variables_idx);
-else
+else
ReducedForm.ghxx = zeros(size(restrict_variables_idx,1),size(dr.kstate,2));
ReducedForm.ghuu = zeros(size(restrict_variables_idx,1),size(dr.ghu,2));
ReducedForm.ghxu = zeros(size(restrict_variables_idx,1),size(dr.ghx,2));
@@ -325,7 +325,7 @@ ReducedForm.mf0 = mf0;
ReducedForm.mf1 = mf1;
% Set initial condition for t=1
-if observation_number==1
+if observation_number==1
switch DynareOptions.particle.initialization
case 1% Initial state vector covariance is the ergodic variance associated to the first order Taylor-approximation of the model.
StateVectorMean = ReducedForm.constant(mf0);
diff --git a/src/spherical_radial_sigma_points.m b/src/spherical_radial_sigma_points.m
index 754caac..db71f61 100644
--- a/src/spherical_radial_sigma_points.m
+++ b/src/spherical_radial_sigma_points.m
@@ -1,7 +1,7 @@
-function [nodes,weights] = spherical_radial_sigma_points(n)
+function [nodes,weights] = spherical_radial_sigma_points(n)
%
-% Computes nodes and weigths from a third-degree spherical-radial cubature
-% rule.
+% Computes nodes and weigths from a third-degree spherical-radial cubature
+% rule.
% INPUTS
% n [integer] scalar, number of variables.
%
@@ -11,10 +11,10 @@ function [nodes,weights] = spherical_radial_sigma_points(n)
%
% REFERENCES
%
-% Arasaratnam & Haykin 2008,2009.
+% Arasaratnam & Haykin 2008,2009.
%
% NOTES
-%
+%
% Copyright (C) 2009-2012 Dynare Team
%
% This file is part of Dynare.
diff --git a/src/traditional_resampling.m b/src/traditional_resampling.m
index a9d86eb..87f5503 100644
--- a/src/traditional_resampling.m
+++ b/src/traditional_resampling.m
@@ -75,7 +75,7 @@ c = cumsum(weights);
% Draw a starting point.
if kitagawa_resampling
randvec = (transpose(1:number_of_particles)-1+noise(:))/number_of_particles ;
-else
+else
randvec = fliplr(cumprod(noise.^(1./(number_of_particles:-1:1))));
end
diff --git a/src/univariate_smooth_resampling.m b/src/univariate_smooth_resampling.m
index 8238902..e89b2cc 100644
--- a/src/univariate_smooth_resampling.m
+++ b/src/univariate_smooth_resampling.m
@@ -61,16 +61,16 @@ lambda_tilde = [ (.5*weights(1)) ;
lambda_bar = cumsum(lambda_tilde) ;
u = rand(1,1) ;
new_particles = zeros(number_of_new_particles,1) ;
-rj = 0 ;
+rj = 0 ;
i = 1 ;
j = 1 ;
while i<=number_of_new_particles
u_j = ( i-1 + u)/number_of_new_particles ;
while u_j>lambda_bar(j)
- rj = j ;
+ rj = j ;
j = j+1 ;
end
- if rj==0
+ if rj==0
new_particles(i) = particles(1) ;
elseif rj==M
new_particles(i) = particles(M) ;
diff --git a/src/unscented_sigma_points.m b/src/unscented_sigma_points.m
index 3877175..d1cba6e 100644
--- a/src/unscented_sigma_points.m
+++ b/src/unscented_sigma_points.m
@@ -1,6 +1,6 @@
-function [nodes,W_m,W_c] = unscented_sigma_points(n,ParticleOptions)
+function [nodes,W_m,W_c] = unscented_sigma_points(n,ParticleOptions)
%
-% Computes nodes and weigths for a scaled unscented transform cubature
+% Computes nodes and weigths for a scaled unscented transform cubature
% INPUTS
% n [integer] scalar, number of variables.
%
@@ -10,10 +10,10 @@ function [nodes,W_m,W_c] = unscented_sigma_points(n,ParticleOptions)
%
% REFERENCES
%
-%
+%
%
% NOTES
-%
+%
% Copyright (C) 2009-2012 Dynare Team
%
% This file is part of Dynare.
@@ -31,12 +31,10 @@ function [nodes,W_m,W_c] = unscented_sigma_points(n,ParticleOptions)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-lambda = (ParticleOptions.unscented.alpha^2)*(n+ParticleOptions.unscented.kappa) - n ;
+lambda = (ParticleOptions.unscented.alpha^2)*(n+ParticleOptions.unscented.kappa) - n ;
nodes = [ zeros(n,1) ( sqrt(n+lambda).*([ eye(n) -eye(n)]) ) ]' ;
W_m = lambda/(n+lambda) ;
W_c = W_m + (1-ParticleOptions.unscented.alpha^2+ParticleOptions.unscented.beta) ;
temp = ones(2*n,1)/(2*(n+lambda)) ;
-W_m = [W_m ; temp] ;
-W_c = [W_c ; temp] ;
-
-
+W_m = [W_m ; temp] ;
+W_c = [W_c ; temp] ;
--
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