public class MultivariateObservationEquation extends Object
yt = Ft * xt + vt
| Constructor and Description |
|---|
MultivariateObservationEquation(Matrix F,
Matrix V)
Constructs a time-invariant an observation equation.
|
MultivariateObservationEquation(Matrix F,
Matrix V,
NormalRVG rmvnorm)
Constructs a time-invariant an observation equation.
|
MultivariateObservationEquation(MultivariateObservationEquation that)
Copy constructor.
|
MultivariateObservationEquation(ObservationEquation obs)
Constructs a multivariate observation equation from a univariate observation equation.
|
MultivariateObservationEquation(R1toMatrix F,
R1toMatrix V)
Constructs an observation equation.
|
MultivariateObservationEquation(R1toMatrix F,
R1toMatrix V,
NormalRVG rmvnorm)
Constructs an observation equation.
|
| Modifier and Type | Method and Description |
|---|---|
int |
dimension()
Gets the dimension of observation yt.
|
ImmutableMatrix |
F(int t)
Gets F(t), the coefficient matrix of xt.
|
ImmutableMatrix |
V(int t)
Gets V(t), the covariance matrix of vt.
|
ImmutableVector |
yt_mean(int t,
Vector xt)
Predicts the next observation.
|
ImmutableMatrix |
yt_var(int t,
Matrix var_t_tlag)
Gets the covariance of the apriori prediction for the next observation.
|
ImmutableVector |
yt(int t,
Vector xt)
Evaluates the observation equation.
|
public MultivariateObservationEquation(R1toMatrix F, R1toMatrix V, NormalRVG rmvnorm)
F - the coefficient matrix function of xt, a function of timeV - the covariance matrix function of vt, a function of timermvnorm - a d-dimensional standard multivariate Gaussian random vector generator
(for seeding); d = the dimension of V or ytpublic MultivariateObservationEquation(R1toMatrix F, R1toMatrix V)
F - the coefficient matrix function of xt, a function of timeV - the covariance matrix function of vt, a function of timepublic MultivariateObservationEquation(Matrix F, Matrix V, NormalRVG rmvnorm)
F - the coefficient matrix of xtV - the covariance matrix of vtrmvnorm - a d-dimensional standard multivariate Gaussian random vector generator
(for seeding); d = the dimension of V or ytpublic MultivariateObservationEquation(Matrix F, Matrix V)
F - the coefficient matrix of xtV - the covariance matrix of vtpublic MultivariateObservationEquation(ObservationEquation obs)
obs - a univariate observation equationpublic MultivariateObservationEquation(MultivariateObservationEquation that)
that - a ObservationEquationpublic int dimension()
public ImmutableMatrix F(int t)
t - timepublic ImmutableMatrix V(int t)
t - timepublic ImmutableVector yt_mean(int t, Vector xt)
E(y_t) = F_t * x_t
t - timext - state xtpublic ImmutableMatrix yt_var(int t, Matrix var_t_tlag)
Var(y_{t | t - 1}) = F_t * Var(x_{t | t - 1}) * F_t' + V_t
t - timevar_t_tlag - Var(y_{t | t - 1}), the variance of the apriori predictionpublic ImmutableVector yt(int t, Vector xt)
y_t = F_t * x_t + v_t
t - timext - state xtCopyright © 2010-2020 NM FinTech Ltd.. All Rights Reserved.