# 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 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