# SuanShu, a Java numerical and statistical library

com.numericalmethod.suanshu.stats.dlm.multivariate

## Class MultivariateDLM

• java.lang.Object
• com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLM

• public class MultivariateDLM
extends Object
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:

yt = Ft * xt + vt,
State Equation:
xt = Gt * xt-1 + Ht * ut + wt,
{yt} are the observation vectors; {xt} are the state vectors. Ft and Gt are known matrices of dimension (number of observations * number of states) and (number of states * number of states) respectively. {vt} and {wt} are two independent sequences of independent normal random vectors with mean zero and known variance matrices {Vt} and {Wt}, respectively; Furthermore, it is assumed that x0 is independent of {vt} and {wt} 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); ut 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 xt. Ht is a known matrix of coefficients, with dimension of (number of states * m).
• ### Constructor Summary

Constructors
Constructor and Description
MultivariateDLM(MultivariateDLM that)
Copy constructor.
MultivariateDLM(Vector m0, Matrix C0, MultivariateObservationEquation Yt, MultivariateStateEquation Xt)
Construct a (multivariate) controlled dynamic linear model.
• ### Method Summary

All Methods
Modifier and Type Method and Description
ImmutableMatrix C0()
Get the covariance matrix of x0.
int getObsDimension()
Get the dimension of the observations.
MultivariateObservationEquation getObservationModel()
Get the observation model.
int getStateDimension()
Get the dimension of states.
MultivariateStateEquation 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

• #### MultivariateDLM

public MultivariateDLM(Vector m0,
Matrix C0,
MultivariateObservationEquation Yt,
MultivariateStateEquation 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
• #### MultivariateDLM

public MultivariateDLM(MultivariateDLM 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 MultivariateObservationEquation getObservationModel()
Get the observation model.
Returns:
the observation model
• #### getStateModel

public MultivariateStateEquation getStateModel()
Get the state model.
Returns:
the state model