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Dynare
particles
Commits
04ad104b
Commit
04ad104b
authored
Oct 02, 2015
by
Frédéric Karamé
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Fix a bug in likelihood calculation.
parent
cdc7f6dd
Changes
6
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6 changed files
with
30 additions
and
28 deletions
+30
-28
src/auxiliary_particle_filter.m
src/auxiliary_particle_filter.m
+10
-10
src/conditional_filter_proposal.m
src/conditional_filter_proposal.m
+1
-1
src/conditional_particle_filter.m
src/conditional_particle_filter.m
+6
-6
src/gaussian_densities.m
src/gaussian_densities.m
+4
-3
src/gaussian_filter_bank.m
src/gaussian_filter_bank.m
+1
-1
src/sequential_importance_particle_filter.m
src/sequential_importance_particle_filter.m
+8
-7
No files found.
src/auxiliary_particle_filter.m
View file @
04ad104b
...
...
@@ -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
View file @
04ad104b
...
...
@@ -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
View file @
04ad104b
...
...
@@ -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
mf
0
mf
1
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
View file @
04ad104b
...
...
@@ -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
View file @
04ad104b
...
...
@@ -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
View file @
04ad104b
...
...
@@ -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
...
...
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