Package dev.nm.stat.dlm.univariate
Class ObservationEquation
- java.lang.Object
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- dev.nm.stat.dlm.univariate.ObservationEquation
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public class ObservationEquation 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 ObservationEquation(double F, double V)
Construct a time-invariant an observation equation.ObservationEquation(double F, double V, RandomStandardNormalGenerator rnorm)
Construct a time-invariant an observation equation.ObservationEquation(UnivariateRealFunction F, UnivariateRealFunction V)
Construct an observation equation.ObservationEquation(UnivariateRealFunction F, UnivariateRealFunction V, RandomStandardNormalGenerator rnorm)
Construct an observation equation.ObservationEquation(ObservationEquation that)
Copy constructor.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description int
dimension()
Get the dimension of observation yt.double
F(int t)
Get F(t), the coefficient of xt.double
V(int t)
Get V(t), the variance of vt.double
yt(int t, double xt)
Evaluate the observation equation.double
yt_mean(int t, double xt)
Predict the next observation.double
yt_var(int t, double var_t_tlag)
Get the variance of the apriori prediction for the next observation.
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Constructor Detail
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ObservationEquation
public ObservationEquation(UnivariateRealFunction F, UnivariateRealFunction V, RandomStandardNormalGenerator rnorm)
Construct an observation equation.- Parameters:
F
- the coefficient function of xt, a function of timeV
- the variance function of vt, a function of timernorm
- a standard Gaussian random number generator (for seeding)
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ObservationEquation
public ObservationEquation(UnivariateRealFunction F, UnivariateRealFunction V)
Construct an observation equation.- Parameters:
F
- the coefficient function of xt, a function of timeV
- the variance function of vt, a function of time
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ObservationEquation
public ObservationEquation(double F, double V, RandomStandardNormalGenerator rnorm)
Construct a time-invariant an observation equation.- Parameters:
F
- the coefficient of xtV
- the variance of vtrnorm
- a standard Gaussian random number generator (for seeding)
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ObservationEquation
public ObservationEquation(double F, double V)
Construct a time-invariant an observation equation.- Parameters:
F
- the coefficient of xtV
- the variance of vt
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ObservationEquation
public ObservationEquation(ObservationEquation that)
Copy constructor.- Parameters:
that
- aObservationEquation
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Method Detail
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dimension
public int dimension()
Get the dimension of observation yt.- Returns:
- the dimension of observations
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F
public double F(int t)
Get F(t), the coefficient of xt.- Parameters:
t
- time- Returns:
- F(t)
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V
public double V(int t)
Get V(t), the variance of vt.- Parameters:
t
- time- Returns:
- V(t)
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yt_mean
public double yt_mean(int t, double xt)
Predict 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 double yt_var(int t, double var_t_tlag)
Get the variance 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 double yt(int t, double xt)
Evaluate the observation equation.y_t = F_t * x_t + v_t
- Parameters:
t
- timext
- state xt- Returns:
- the mean observation
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