Add flags to deal with errors on Cholesky matrices in CPF filter.

src/conditional_filter_proposal.m

-function [ProposalStateVector,Weights] = conditional_filter_proposal(ReducedForm,obs,StateVectors,SampleWeights,Q_lower_triangular_cholesky,H_lower_triangular_cholesky,H,ParticleOptions,ThreadsOptions,normconst2)
+function [ProposalStateVector,Weights,flag] = conditional_filter_proposal(ReducedForm,obs,StateVectors,SampleWeights,Q_lower_triangular_cholesky,H_lower_triangular_cholesky,H,ParticleOptions,ThreadsOptions,normconst2)
 %
 % Computes the proposal for each past particle using Gaussian approximations
 % for the state errors and the Kalman filter
@@ -42,6 +42,7 @@ persistent init_flag2 mf0 mf1
 persistent number_of_state_variables number_of_observed_variables
 persistent number_of_structural_innovations
 
+flag=0 ;
 % Set local state space model (first-order approximation).
 ghx  = ReducedForm.ghx;
 ghu  = ReducedForm.ghu;
@@ -118,15 +119,31 @@ else
     Error = obs - PredictedObservedMean ;
     StateVectorMean = PredictedStateMean + KalmanFilterGain*Error ;
     StateVectorVariance = PredictedStateVariance - KalmanFilterGain*PredictedObservedVariance*KalmanFilterGain';
-    StateVectorVarianceSquareRoot = chol(StateVectorVariance + eye(number_of_state_variables)*1e-6)' ;
+    StateVectorVariance = 0.5*(StateVectorVariance+StateVectorVariance');
+    %StateVectorVarianceSquareRoot = reduced_rank_cholesky(StateVectorVariance)';%chol(StateVectorVariance + eye(number_of_state_variables)*1e-6)' ;
+    [StateVectorVarianceSquareRoot,p] = chol(StateVectorVariance,'lower') ;
+    if p
+        flag=1;
+        ProposalStateVector = zeros(number_of_state_variables,1) ;
+        Weights = 0.0 ;
+        return
+    end 
     if ParticleOptions.cpf_weights_method.amisanotristani
         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
+    PredictedStateVariance = 0.5*(PredictedStateVariance+PredictedStateVariance');
+    %PredictedStateVarianceSquareRoot = reduced_rank_cholesky(PredictedStateVariance)';%chol(PredictedStateVariance + eye(number_of_state_variables)*1e-6)'  ;
+    [PredictedStateVarianceSquareRoot,p] = chol(PredictedStateVariance,'lower')  ;
+    if p
+        flag=1;
+        ProposalStateVector = zeros(number_of_state_variables,1) ;
+        Weights = 0.0 ;
+        return
+    end 
     Prior = probability2(PredictedStateMean,PredictedStateVarianceSquareRoot,ProposalStateVector) ;
     Posterior = probability2(StateVectorMean,StateVectorVarianceSquareRoot,ProposalStateVector) ;
     Likelihood = probability2(obs,H_lower_triangular_cholesky,measurement_equations(ProposalStateVector,ReducedForm,ThreadsOptions)) ;

src/conditional_particle_filter.m

@@ -103,8 +103,13 @@ StateParticles = bsxfun(@plus,StateVectorVarianceSquareRoot*randn(state_variance
 SampleWeights = ones(1,number_of_particles)/number_of_particles ;
 for t=1:sample_size
     for i=1:number_of_particles
-        [StateParticles(:,i),SampleWeights(i)] = ...
+        [StateParticles(:,i),SampleWeights(i),flag] = ...
             conditional_filter_proposal(ReducedForm,Y(:,t),StateParticles(:,i),SampleWeights(i),Q_lower_triangular_cholesky,H_lower_triangular_cholesky,H,ParticleOptions,ThreadsOptions,normconst2) ;
+        if flag==1 
+            LIK=-Inf;
+            lik(t)=-Inf;
+            return 
+        end 
     end
     SumSampleWeights = sum(SampleWeights) ;
     lik(t) = log(SumSampleWeights) ;