compute_kalman_filter - Calculates the marginal posteriors of an LDS


#include "sequential/sequential_lds.h"

Example compile flags (system dependent):
   -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


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.


NO_ERROR on success, and ERROR on failure, with an appropriate error message being set.


This software is not adequatedly tested. It is recomended that results are checked independantly where appropriate.


Ernesto Brau


Ernesto Brau


sample_from_LDS , sample_from_LDS_2 , compute_kalman_filter_stable , compute_kalman_filter_2 , compute_kalman_filter_2_stable