Maximum likelihood estimation of the parameters of the
calibration model from Blocker & Airoldi (2011) via direct
numerical maximization of the marginal log-likelihood. This
relies upon efficient Kalman smoothing to evaluate the
marginal likelihood, which is provided here by the
`KFAS`

package.

1 2 3 4 |

`tme` |
integer time at which to center moving window for estimation |

`y` |
matrix (n x m) of observed link loads from all times (not just the window used for estimation; one observation per row |

`A` |
routing matrix (m x k) for network; should be full row rank |

`Ft` |
matrix (k x k) containing fixed autoregressive parameters for state evolution equation; upper-left block of overall matrix for expanded state |

`Rt` |
covariance matrix for observation equation; typically small and fixed |

`lambda0` |
matrix (n x k) of initial estimates for lambda (e.g. obtained via IPFP) |

`phihat0` |
numeric vector (length n) of initial estimates for phi |

`tau` |
numeric power parameter for mean-variance relationship |

`w` |
number of observations to use for rolling-window estimation; handles boundary cases cleanly |

`initScale` |
numeric inflation factor for time-zero state covariance; defaults to steady-state variance setting |

`nugget` |
small positive value to add to diagonal of state evolution covariance matrix to ensure numerical stability |

`verbose` |
logical to select verbose output from algorithm |

`logTrans` |
logical whether to log-transform parameters for optimization. If FALSE, sets method to "L-BFGS-B". |

`method` |
optimization method to use (in optim calls) |

`optimArgs` |
list of arguments to append to control argument for optim. Can include all arguments except for fnscale, which is automatically set |

list containing `lambdahat`

, a numeric vector (length
k) containing the MLE for lambda; `phihat`

, the MLE
for phi; `xhat`

, the smoothed estimates of the OD
flows for the window used as a k x w matrix; and
`varhat`

, a k x w matrix containing the diagonal of
the estimated covariance for each OD flow in the window

A.W. Blocker and E.M. Airoldi. Deconvolution of mixing time series on a graph. Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11) 51-60, 2011.

Other calibrationModel: `llCalibration`

;
`mle_filter`

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
data(bell.labs)
lambda0 <- matrix(1, nrow(bell.labs$Y), ncol(bell.labs$A))
lambda0[100,] <- ipfp(y=bell.labs$Y[100,], A=bell.labs$A,
x0=rep(1, ncol(bell.labs$A)))
phihat0 <- rep(1, nrow(bell.labs$Y))
Ft <- 0.5 * diag_mat(rep(1, ncol(bell.labs$A)))
Rt <- 0.01 * diag_mat(rep(1, nrow(bell.labs$A)))
# Not run
#fit.calibration <- calibration_ssm(tme=100, y=bell.labs$Y, A=bell.labs$A,
# Ft=Ft, Rt=Rt, lambda0=lambda0,
# phihat0=phihat0, w=23)
``` |

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