com.numericalmethod.suanshu.stats.dlm.univariate

Class ObservationEquation

java.lang.Object

com.numericalmethod.suanshu.stats.dlm.univariate.ObservationEquation

All Implemented Interfaces:

java.io.Serializable

public class ObservationEquation
extends java.lang.Object
implements java.io.Serializable

This is the observation equation in a controlled dynamic linear model.

y_t = F_t * x_t + v_t

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
ObservationEquation(double F, double V) Construct a time-invariant an observation equation.
ObservationEquation(double F, double V, NormalRng rnorm) Construct a time-invariant an observation equation.
ObservationEquation(ObservationEquation that) Copy constructor.
ObservationEquation(UnivariateRealFunction F, UnivariateRealFunction V) Construct an observation equation.
ObservationEquation(UnivariateRealFunction F, UnivariateRealFunction V, NormalRng rnorm) Construct an observation equation.

Method Summary

Modifier and Type	Method and 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_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.
double	yt(int t, double xt) Evaluate the observation equation.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

ObservationEquation

public ObservationEquation(UnivariateRealFunction F,
                           UnivariateRealFunction V,
                           NormalRng rnorm)

Construct an observation equation.

Parameters:

F - the coefficient function of x_t, a function of time

V - the variance function of v_t, a function of time

rnorm - a standard Gaussian random number generator (for seeding)

ObservationEquation

public ObservationEquation(UnivariateRealFunction F,
                           UnivariateRealFunction V)

Construct an observation equation.

Parameters:

F - the coefficient function of x_t, a function of time

V - the variance function of v_t, a function of time

ObservationEquation

public ObservationEquation(double F,
                           double V,
                           NormalRng rnorm)

Construct a time-invariant an observation equation.

Parameters:

F - the coefficient of x_t

V - the variance of v_t

rnorm - a standard Gaussian random number generator (for seeding)

ObservationEquation

public ObservationEquation(double F,
                           double V)

Construct a time-invariant an observation equation.

Parameters:

F - the coefficient of x_t

V - the variance of v_t

ObservationEquation

public ObservationEquation(ObservationEquation that)

Copy constructor.

Parameters:

that - a ObservationEquation

Method Detail

dimension

public int dimension()

Get the dimension of observation y_t.

Returns:

the dimension of observations

F

public double F(int t)

Get F(t), the coefficient of x_t.

Parameters:

t - time

Returns:

F(t)

V

public double V(int t)

Get V(t), the variance of v_t.

Parameters:

t - time

Returns:

V(t)

yt_mean

public double yt_mean(int t,
                      double xt)

Predict the next observation.

E(y_t) = F_t * x_t

Parameters:

t - time

xt - state x_t

Returns:

the mean observation

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 - time

var_t_tlag - Var(y_{t | t - 1}), the variance of the apriori prediction

Returns:

Var(y_{t | t - 1})

yt

public double yt(int t,
                 double xt)

Evaluate the observation equation.

y_t = F_t * x_t + v_t

Parameters:

t - time

xt - state x_t

Returns:

the mean observation