Class 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).
    See Also:
    • 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