NAME
compute_kalman_filter - Calculates the marginal posteriors of an LDS
SYNOPSIS
#include "sequential/sequential_lds.h"
Example compile flags (system dependent):
-DLINUX_X86_64 -DLINUX_X86_64_OPTERON -DGNU_COMPILER
-I/home/kobus/include
-L/home/kobus/misc/load/linux_x86_64_opteron -L/usr/lib/x86_64-linux-gnu
-lKJB -lfftw3 -lgsl -lgslcblas -ljpeg -lSVM -lstdc++ -lpthread -lSLATEC -lg2c -lacml -lacml_mv -lblas -lg2c -lncursesw
int compute_kalman_filter
(
Vector_vector **means,
Matrix_vector **covariances,
double *likelihood,
const Vector_vector *y,
const Matrix *A,
const Matrix *Q,
const Matrix *H,
const Matrix *R,
const Vector *mu_0,
const Matrix *S_0
);
DESCRIPTION
This routine calculates the marginal posterior distributions for a linear
dynamical system model. Here, y is the set of observations and let x be
the set of (latent) state variables. This routine computes the distributions
p(x_k | y_k, ..., y_k)
for k = 1, ..., N, where the x_k are n-vectors and the y_k are m-vectors.
Since these distributions are normal, it suffices to compute
their means and covariances, which this routine puts in *means
and *covariances. The rest of the parameters are best explained by
seeing the equations of motion of the LDS:
x_1 = mu_0 + u,
x_k = A * x_{k-1} + w
y_k = H * z_k + v
where w ~ N(0, Q) and v ~ N(0, R) and u ~ N(0, S_0), and the A
and Q are nxn matrices, the H is an mxn matrix and R is an mxm matrix.
Finally, this routine also computes the incomplete-data log-likelihood (LOG!)
of the LDS, i.e.,
log p(y_1, y_2, ..., y_N | A, H, Q, R, mu_0, S_0).
As usual, *means and *covariances are reused if possible and created
if needed, according to the KJB allocation semantics. Any result that is
not desired can be omitted by passing NULL to the rouitne.
RETURNS
NO_ERROR on success, and ERROR on failure, with an appropriate error
message being set.
DISCLAIMER
This software is not adequatedly tested. It is recomended that
results are checked independantly where appropriate.
AUTHOR
Ernesto Brau
DOCUMENTER
Ernesto Brau
SEE ALSO
sample_from_LDS
,
sample_from_LDS_2
,
compute_kalman_filter_stable
,
compute_kalman_filter_2
,
compute_kalman_filter_2_stable