Class ObservationEquation


  • public class ObservationEquation
    extends Object
    This is the observation equation in a controlled dynamic linear model.
    yt = Ft * xt + vt
    • Constructor Detail

      • ObservationEquation

        public ObservationEquation​(UnivariateRealFunction F,
                                   UnivariateRealFunction V)
        Construct an observation equation.
        Parameters:
        F - the coefficient function of xt, a function of time
        V - the variance function of vt, a function of time
      • ObservationEquation

        public ObservationEquation​(double F,
                                   double V,
                                   RandomStandardNormalGenerator rnorm)
        Construct a time-invariant an observation equation.
        Parameters:
        F - the coefficient of xt
        V - the variance of vt
        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 xt
        V - the variance of vt
      • ObservationEquation

        public ObservationEquation​(ObservationEquation that)
        Copy constructor.
        Parameters:
        that - a ObservationEquation
    • Method Detail

      • dimension

        public int dimension()
        Get the dimension of observation yt.
        Returns:
        the dimension of observations
      • F

        public double F​(int t)
        Get F(t), the coefficient of xt.
        Parameters:
        t - time
        Returns:
        F(t)
      • V

        public double V​(int t)
        Get V(t), the variance of vt.
        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 xt
        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 xt
        Returns:
        the mean observation