Package dev.nm.stat.dlm.multivariate
Class MultivariateObservationEquation
- java.lang.Object
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- dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
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public class MultivariateObservationEquation extends Object
This is the observation equation in a controlled dynamic linear model.yt = Ft * xt + vt
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Constructor Summary
Constructors Constructor 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(R1toMatrix F, R1toMatrix V)
Constructs an observation equation.MultivariateObservationEquation(R1toMatrix F, R1toMatrix V, NormalRVG rmvnorm)
Constructs an observation equation.MultivariateObservationEquation(MultivariateObservationEquation that)
Copy constructor.MultivariateObservationEquation(ObservationEquation obs)
Constructs a multivariate observation equation from a univariate observation equation.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method 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(int t, Vector xt)
Evaluates the observation equation.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.
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Constructor Detail
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MultivariateObservationEquation
public MultivariateObservationEquation(R1toMatrix F, R1toMatrix V, NormalRVG rmvnorm)
Constructs an observation equation.- Parameters:
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 yt
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MultivariateObservationEquation
public MultivariateObservationEquation(R1toMatrix F, R1toMatrix V)
Constructs an observation equation.- Parameters:
F
- the coefficient matrix function of xt, a function of timeV
- the covariance matrix function of vt, a function of time
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MultivariateObservationEquation
public MultivariateObservationEquation(Matrix F, Matrix V, NormalRVG rmvnorm)
Constructs a time-invariant an observation equation.- Parameters:
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 yt
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MultivariateObservationEquation
public MultivariateObservationEquation(Matrix F, Matrix V)
Constructs a time-invariant an observation equation.- Parameters:
F
- the coefficient matrix of xtV
- the covariance matrix of vt
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MultivariateObservationEquation
public MultivariateObservationEquation(ObservationEquation obs)
Constructs a multivariate observation equation from a univariate observation equation.- Parameters:
obs
- a univariate observation equation
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MultivariateObservationEquation
public MultivariateObservationEquation(MultivariateObservationEquation that)
Copy constructor.- Parameters:
that
- aObservationEquation
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Method Detail
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dimension
public int dimension()
Gets the dimension of observation yt.- Returns:
- the dimension of observations
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F
public ImmutableMatrix F(int t)
Gets F(t), the coefficient matrix of xt.- Parameters:
t
- time- Returns:
- F(t)
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V
public ImmutableMatrix V(int t)
Gets V(t), the covariance matrix of vt.- Parameters:
t
- time- Returns:
- V(t)
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yt_mean
public ImmutableVector yt_mean(int t, Vector xt)
Predicts the next observation.E(y_t) = F_t * x_t
- Parameters:
t
- timext
- state xt- Returns:
- the mean observation
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yt_var
public ImmutableMatrix yt_var(int t, Matrix var_t_tlag)
Gets the covariance of the apriori prediction for the next observation.Var(y_{t | t - 1}) = F_t * Var(x_{t | t - 1}) * F_t' + V_t
- Parameters:
t
- timevar_t_tlag
- Var(y_{t | t - 1}), the variance of the apriori prediction- Returns:
- Var(y_{t | t - 1})
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yt
public ImmutableVector yt(int t, Vector xt)
Evaluates the observation equation.y_t = F_t * x_t + v_t
- Parameters:
t
- timext
- state xt- Returns:
- the mean observation
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