Fix a bug in likelihood calculation.

src/auxiliary_particle_filter.m

@@ -64,7 +64,7 @@ end
 
 % Get initial condition for the state vector.
 StateVectorMean = ReducedForm.StateVectorMean;
-StateVectorVarianceSquareRoot = reduced_rank_cholesky(ReducedForm.StateVectorVariance)';
+StateVectorVarianceSquareRoot = chol(ReducedForm.StateVectorVariance)';%reduced_rank_cholesky(ReducedForm.StateVectorVariance)';
 state_variance_rank = size(StateVectorVarianceSquareRoot,2);
 Q_lower_triangular_cholesky = chol(Q)';
 if pruning
@@ -76,7 +76,7 @@ end
 set_dynare_seed('default');
 
 % Initialization of the likelihood.
-const_lik = log(2*pi)*number_of_observed_variables;
+const_lik = log(2*pi)*number_of_observed_variables +log(det(H));
 lik  = NaN(sample_size,1);
 LIK  = NaN;
 
@@ -125,11 +125,11 @@ for t=1:sample_size
             tmp = tmp + nodes_weights(i)*local_state_space_iteration_2(yhat,nodes(i,:)*ones(1,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
         end
     end
-    PredictedObservedMean = weights*(tmp(mf1,:)');
+    %PredictedObservedMean = weights*(tmp(mf1,:)');
     PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
-    dPredictedObservedMean = bsxfun(@minus,tmp(mf1,:),PredictedObservedMean');
-    PredictedObservedVariance = bsxfun(@times,weights,dPredictedObservedMean)*dPredictedObservedMean' +H;
-    wtilde = exp(-.5*(const_lik+log(det(PredictedObservedVariance))+sum(PredictionError.*(PredictedObservedVariance\PredictionError),1))) ;
+    %dPredictedObservedMean = bsxfun(@minus,tmp(mf1,:),PredictedObservedMean');
+    %PredictedObservedVariance = bsxfun(@times,weights,dPredictedObservedMean)*dPredictedObservedMean' +H;
+    wtilde = exp(-.5*(const_lik+sum(PredictionError.*(H\PredictionError),1))) ;
     tau_tilde = weights.*wtilde ;
     sum_tau_tilde = sum(tau_tilde) ;
     lik(t) = log(sum_tau_tilde) ; 
@@ -148,11 +148,11 @@ for t=1:sample_size
         tmp = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
     end
     StateVectors = tmp(mf0,:);
-    PredictedObservedMean = mean(tmp(mf1,:),2);
+    %PredictedObservedMean = mean(tmp(mf1,:),2);
     PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
-    dPredictedObservedMean = bsxfun(@minus,tmp(mf1,:),PredictedObservedMean);
-    PredictedObservedVariance = (dPredictedObservedMean*dPredictedObservedMean')/number_of_particles + H;
-    lnw = exp(-.5*(const_lik+log(det(PredictedObservedVariance))+sum(PredictionError.*(PredictedObservedVariance\PredictionError),1)));
+    %dPredictedObservedMean = bsxfun(@minus,tmp(mf1,:),PredictedObservedMean);
+    %PredictedObservedVariance = (dPredictedObservedMean*dPredictedObservedMean')/number_of_particles + H;
+    lnw = exp(-.5*(const_lik+sum(PredictionError.*(H\PredictionError),1)));
     wtilde = lnw.*factor ;
     weights = wtilde/sum(wtilde);
 end

src/conditional_filter_proposal.m

@@ -113,7 +113,7 @@ else
     StateVectorMean = PredictedStateMean + KalmanFilterGain*(obs - PredictedObservedMean);
     StateVectorVariance = PredictedStateVariance - KalmanFilterGain*PredictedObservedVariance*KalmanFilterGain';
     StateVectorVariance = .5*(StateVectorVariance+StateVectorVariance');
-    StateVectorVarianceSquareRoot = reduced_rank_cholesky(StateVectorVariance)';
+    StateVectorVarianceSquareRoot = chol(StateVectorVariance)';%reduced_rank_cholesky(StateVectorVariance)';
 end
 
 ProposalStateVector = StateVectorVarianceSquareRoot*randn(size(StateVectorVarianceSquareRoot,2),1)+StateVectorMean ;

src/conditional_particle_filter.m

@@ -57,9 +57,9 @@ function [LIK,lik] = conditional_particle_filter(ReducedForm,Y,start,ParticleOpt
 % AUTHOR(S) frederic DOT karame AT univ DASH lemans DOT fr
 %           stephane DOT adjemian AT univ DASH lemans DOT fr
 
-persistent init_flag mf0 mf1
+persistent init_flag mf1
 persistent number_of_particles 
-persistent sample_size number_of_state_variables number_of_observed_variables 
+persistent sample_size number_of_observed_variables 
 
 % Set default
 if isempty(start)
@@ -68,10 +68,10 @@ end
 
 % Set persistent variables.
 if isempty(init_flag)
-    mf0 = ReducedForm.mf0;
+    %mf0 = ReducedForm.mf0;
     mf1 = ReducedForm.mf1;
     sample_size = size(Y,2);
-    number_of_state_variables = length(mf0);
+    %number_of_state_variables = length(mf0);
     number_of_observed_variables = length(mf1);
     init_flag = 1;
     number_of_particles = ParticleOptions.number_of_particles ;
@@ -84,14 +84,14 @@ if isempty(H)
     H = 0;
     H_lower_triangular_cholesky = 0;
 else
-    H_lower_triangular_cholesky = reduced_rank_cholesky(H)';
+    H_lower_triangular_cholesky = chol(H)'; %reduced_rank_cholesky(H)';
 end
 
 % Get initial condition for the state vector.
 StateVectorMean = ReducedForm.StateVectorMean;
 StateVectorVarianceSquareRoot = reduced_rank_cholesky(ReducedForm.StateVectorVariance)';
 state_variance_rank = size(StateVectorVarianceSquareRoot,2);
-Q_lower_triangular_cholesky = reduced_rank_cholesky(Q)';
+Q_lower_triangular_cholesky = chol(Q)'; %reduced_rank_cholesky(Q)';
 
 % Set seed for randn().
 set_dynare_seed('default');

src/gaussian_densities.m

@@ -43,9 +43,10 @@ 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) ;
-tmp = bsxfun(@minus,yt_t_1_i,yt_t_1) ;
-Pyy = bsxfun(@times,weigths2',tmp)*tmp' + H ;
+%yt_t_1 = sum(yt_t_1_i*weigths1,2) ;
+%tmp = bsxfun(@minus,yt_t_1_i,yt_t_1) ;
+%Pyy = bsxfun(@times,weigths2',tmp)*tmp' + H ;
+Pyy = 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 ;			

src/gaussian_filter_bank.m

@@ -118,5 +118,5 @@ else
     StateVectorMean = PredictedStateMean + KalmanFilterGain*PredictionError;
     StateVectorVariance = PredictedStateVariance - KalmanFilterGain*PredictedObservedVariance*KalmanFilterGain';
     StateVectorVariance = .5*(StateVectorVariance+StateVectorVariance');
-    StateVectorVarianceSquareRoot = reduced_rank_cholesky(StateVectorVariance)';
+    StateVectorVarianceSquareRoot = chol(StateVectorVariance)'; %reduced_rank_cholesky(StateVectorVariance)';
 end
\ No newline at end of file

src/sequential_importance_particle_filter.m

@@ -66,12 +66,12 @@ if isempty(H)
 end
 
 % Initialization of the likelihood.
-const_lik = log(2*pi)*number_of_observed_variables;
+const_lik = log(2*pi)*number_of_observed_variables +log(det(H)) ;
 lik  = NaN(sample_size,1);
 
 % Get initial condition for the state vector.
 StateVectorMean = ReducedForm.StateVectorMean;
-StateVectorVarianceSquareRoot = reduced_rank_cholesky(ReducedForm.StateVectorVariance)';
+StateVectorVarianceSquareRoot = chol(ReducedForm.StateVectorVariance)';%reduced_rank_cholesky(ReducedForm.StateVectorVariance)';
 if pruning
     StateVectorMean_ = StateVectorMean;
     StateVectorVarianceSquareRoot_ = StateVectorVarianceSquareRoot;
@@ -103,12 +103,13 @@ for t=1:sample_size
     else
         tmp = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
     end
-    PredictedObservedMean = tmp(mf1,:)*transpose(weights);
+    %PredictedObservedMean = tmp(mf1,:)*transpose(weights);
     PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
-    dPredictedObservedMean = bsxfun(@minus,tmp(mf1,:),PredictedObservedMean);
-    PredictedObservedVariance = bsxfun(@times,dPredictedObservedMean,weights)*dPredictedObservedMean' + H;
-    if rcond(PredictedObservedVariance) > 1e-16
-        lnw = -.5*(const_lik+log(det(PredictedObservedVariance))+sum(PredictionError.*(PredictedObservedVariance\PredictionError),1));
+    %dPredictedObservedMean = bsxfun(@minus,tmp(mf1,:),PredictedObservedMean);
+    %PredictedObservedVariance = bsxfun(@times,dPredictedObservedMean,weights)*dPredictedObservedMean' + H;
+    %PredictedObservedVariance = H;
+    if rcond(H) > 1e-16
+        lnw = -.5*(const_lik+sum(PredictionError.*(H\PredictionError),1));
     else
         LIK = NaN;
         return