public class ObservationEquation extends Object
yt = Ft * xt + vt
Constructor and Description |
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ObservationEquation(double F,
double V)
Construct a time-invariant an observation equation.
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ObservationEquation(double F,
double V,
RandomStandardNormalGenerator rnorm)
Construct a time-invariant an observation equation.
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ObservationEquation(ObservationEquation that)
Copy constructor.
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ObservationEquation(UnivariateRealFunction F,
UnivariateRealFunction V)
Construct an observation equation.
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ObservationEquation(UnivariateRealFunction F,
UnivariateRealFunction V,
RandomStandardNormalGenerator rnorm)
Construct an observation equation.
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Modifier and Type | Method and Description |
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int |
dimension()
Get the dimension of observation yt.
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double |
F(int t)
Get F(t), the coefficient of xt.
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double |
V(int t)
Get V(t), the variance of vt.
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double |
yt_mean(int t,
double xt)
Predict the next observation.
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double |
yt_var(int t,
double var_t_tlag)
Get the variance of the apriori prediction for the next observation.
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double |
yt(int t,
double xt)
Evaluate the observation equation.
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public ObservationEquation(UnivariateRealFunction F, UnivariateRealFunction V, RandomStandardNormalGenerator rnorm)
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)public ObservationEquation(UnivariateRealFunction F, UnivariateRealFunction V)
F
- the coefficient function of xt, a function of timeV
- the variance function of vt, a function of timepublic ObservationEquation(double F, double V, RandomStandardNormalGenerator rnorm)
F
- the coefficient of xtV
- the variance of vtrnorm
- a standard Gaussian random number generator (for seeding)public ObservationEquation(double F, double V)
F
- the coefficient of xtV
- the variance of vtpublic ObservationEquation(ObservationEquation that)
that
- a ObservationEquation
public int dimension()
public double F(int t)
t
- timepublic double V(int t)
t
- timepublic double yt_mean(int t, double xt)
E(y_t) = F_t * x_t
t
- timext
- state xtpublic double yt_var(int t, double 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 double yt(int t, double xt)
y_t = F_t * x_t + v_t
t
- timext
- state xtCopyright © 2010-2020 NM FinTech Ltd.. All Rights Reserved.