public class DLM extends Object
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 and Description |
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DLM(DLM that)
Copy constructor.
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DLM(double m0,
double C0,
ObservationEquation Yt,
StateEquation Xt)
Construct a univariate controlled dynamic linear model.
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Modifier and Type | Method and Description |
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double |
C0()
Get the variance of x0.
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int |
getObsDimension()
Get the dimension of observations.
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ObservationEquation |
getObservationModel()
Get the observation model.
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int |
getStateDimension()
Get the dimension of states.
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StateEquation |
getStateModel()
Get the state model.
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double |
m0()
Get the the mean of x0.
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public DLM(double m0, double C0, ObservationEquation Yt, StateEquation Xt)
m0
- the mean of x0C0
- the variance of x0Yt
- the observation equation for the modelXt
- the state equation for the modelpublic DLM(DLM that)
that
- a univariate controlled dynamic linear modelpublic double m0()
public double C0()
public int getObsDimension()
public int getStateDimension()
public ObservationEquation getObservationModel()
public StateEquation getStateModel()
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