com.numericalmethod.suanshu.stats.dlm.multivariate

Class DLM

java.lang.Object

com.numericalmethod.suanshu.stats.dlm.multivariate.DLM

All Implemented Interfaces:

java.io.Serializable

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

This is the multivariate controlled DLM (controlled Dynamic Linear Model) specification. A controlled DLM, for (t ≥ 1), is described by two equations: the observation and state equations.

Observation Equation:

y_t = F_t * x_t + v_t,

State Equation:

x_t = G_t * x_t-1 + H_t * u_t + w_t,

{y_t} are the observation vectors; {x_t} are the state vectors. F_t and G_t are known matrices of dimension (number of observations * number of states) and (number of states * number of states) respectively. {v_t} and {w_t} are two independent sequences of independent normal random vectors with mean zero and known variance matrices {V_t} and {W_t}, respectively; Furthermore, it is assumed that x₀ is independent of {v_t} and {w_t} and is normally distributed with mean m₀ and covariance matrix C₀, where m₀ is a vector of length the same as the number of states and C₀ is a matrix of dimension (number of states * number of states); u_t is an m-dimensional vector of control variables, i.e., the variables whose values can be regulated by the user, in order to obtain a desired level of the state x_t. H_t is a known matrix of coefficients, with dimension of (number of states * m).

See Also:

• G. Petris et al., "ch. 2, pp. 31-84," Dynamic Linear Models with R, New York, Springer, 2009.
• Wikipedia: Kalman filter - Underlying dynamic system model

, Serialized Form

Constructor Summary

Constructors

Constructor and Description
DLM(DLM that) Copy constructor.
DLM(Vector m0, Matrix C0, ObservationEquation Yt, StateEquation Xt) Construct a (multivariate) controlled dynamic linear model.

Method Summary

Modifier and Type	Method and Description
ImmutableMatrix	C0() Get the covariance matrix of x0.
int	getObsDimension() Get the dimension of the observations.
ObservationEquation	getObservationModel() Get the observation model.
int	getStateDimension() Get the dimension of states.
StateEquation	getStateModel() Get the state model.
ImmutableVector	m0() Get the the mean of x0.

Methods inherited from class java.lang.Object

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

Constructor Detail

DLM

public DLM(Vector m0,
           Matrix C0,
           ObservationEquation Yt,
           StateEquation Xt)

Construct a (multivariate) controlled dynamic linear model.

Parameters:

m0 - the mean of x₀

C0 - the covariance matrix of x₀

Yt - the observation equation for the model

Xt - the state equation for the model

DLM

public DLM(DLM that)

Copy constructor.

Parameters:

that - a (multivariate) controlled dynamic linear model

Method Detail

m0

public ImmutableVector m0()

Get the the mean of x₀.

Returns:

m0, the mean of x₀

C0

public ImmutableMatrix C0()

Get the covariance matrix of x₀.

Returns:

C0, the covariance matrix of x₀

getObsDimension

public int getObsDimension()

Get the dimension of the observations.

Returns:

the dimension of the observations

getStateDimension

public int getStateDimension()

Get the dimension of states.

Returns:

the dimension of states

getObservationModel

public ObservationEquation getObservationModel()

Get the observation model.

Returns:

the observation model

getStateModel

public StateEquation getStateModel()

Get the state model.

Returns:

the state model