Description Usage Arguments Value References See Also
Evaluates marginal log-likelihood for calibration SSM of
Blocker & Airoldi (2011) using Kalman filtering. This is
very fast and numerically stable, using the univariate
Kalman filtering and smoothing functions of KFAS
with Fortran implementations.
1 2 | llCalibration(theta, Ft, yt, Zt, Rt, k = ncol(Ft), tau = 2,
initScale = 1/(1 - diag(Ft)^2), nugget = sqrt(.Machine$double.eps))
|
theta |
numeric vector (length k+1) of parameters. theta[-1] = log(lambda), and theta[1] = log(phi) |
Ft |
evolution matrix (k x k) for OD flows; include fixed |
yt |
matrix (k x n) of observed link loads, one observation per column |
Zt |
observation matrix for system; should be routing matrix A |
Rt |
covariance matrix for observation equation; typically small and fixed |
k |
integer number of OD flows to infer |
tau |
numeric power parameter for mean-variance relationship |
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 |
numeric marginal log-likelihood obtained via Kalman smoothing
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: calibration_ssm
;
mle_filter
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