Auger-Me´the´ M, Field C, Albertsen CM, Derocher AE, Lewis MA, Jonsen ID, et al. State-space mod- els' dirty little secrets: even simple linear Gaussian models can have estimation problems. Sci Rep. 2016; 6: 26677. https://doi.org/10.1038/srep26677 PMID: 27220686...
The dssm function returns a dssm object specifying the functional form and storing the parameter values of a diffuse linear Gaussian state-space model for a latent state process xt possibly imperfectly observed through the variable yt. The variables xt and yt can be univariate or multivariate and...
The ssm function returns an ssm object specifying the functional form and storing the parameter values of a standard linear Gaussian state-space model for a latent state process xt possibly imperfectly observed through the variable yt.
State space modelScore vectorScore testInitial conditionA recursive formula for computing the exact value of score vectors is proposed for a general form of the linear Gaussian state space model, which is more desirable than approximate values in some statistical analyses. Unlike most extant methods,...
Particle Metropolis-Hastings algorithm for a linear Gaussian state space modelJohan Dahlin
We consider Particle Gibbs (PG) as a tool for Bayesian analysis of non-linear non-Gaussian state-space models. PG is a Monte Carlo (MC) approximation of the standard Gibbs procedure which uses sequential MC (SMC) importance sampling inside the Gibbs procedure to update the latent and ...
This MATLAB function computes an optimal linear-quadratic-Gaussian (LQG) regulator reg given a state-space model sys of the plant and weighting matrices QXU and QWV.
This MATLAB function computes an optimal linear-quadratic-Gaussian (LQG) regulator reg given a state-space model sys of the plant and weighting matrices QXU and QWV.
Linear-quadratic-Gaussian (LQG) control is a state-space technique that allows you to trade off regulation/tracker performance and control effort, and to take into account process disturbances and measurement noise.
Equations (2) and (3) describe the state/source and observation spaces, respectively. The parameters of the former are time-varying, indexed by the block index n, while the latter noisy mixing process is stationary. The randomness of the model is enabled by i.i.d. zero mean Gaussian varia...