dlm.lpl {DGM} | R Documentation |

## Calculate the log predictive likelihood for a specified set of parents and a fixed delta.

### Description

Calculate the log predictive likelihood for a specified set of parents and a fixed delta.

### Usage

```
dlm.lpl(Yt, Ft, delta, priors = priors.spec())
```

### Arguments

`Yt` |
the vector of observed time series, length |

`Ft` |
the matrix of covariates, dim = number of thetas ( |

`delta` |
discount factor (scalar). |

`priors` |
list with prior hyperparameters. |

### Value

`mt` |
the vector or matrix of the posterior mean (location parameter), dim = |

`Ct` |
and |

`Rt` |
and |

`nt` |
and |

`S` |
the vector of the point estimate for the observation variance |

`ft` |
the vector of the one-step forecast location parameter with length |

`Qt` |
the vector of the one-step forecast scale parameter with length |

`ets` |
the vector of the standardised forecast residuals with length |

`lpl` |
the vector of the Log Predictive Likelihood with length |

### References

West, M. & Harrison, J., 1997. Bayesian Forecasting and Dynamic Models. Springer New York.

### Examples

```
data("utestdata")
Yt = myts[,1]
Ft = t(cbind(1,myts[,2:5]))
m = dlm.lpl(Yt, Ft, 0.7)
```

*DGM*version 1.7.4 Index]