Description Usage Arguments Value References See Also
Builds prior from appropriately structured output of the
calibration model from Blocker & Airoldi (2011). Handles
all formatting so result can be fed directly to
bayesianDynamicFilter
.
1 2 | buildPrior(xHat, varHat, phiHat, Y, A, rho = 0.9, phiPriorDf = ncol(A)/2,
backward = FALSE, lambdaMin = 1, ipfp.maxit = 1e+06, ipfp.tol = 1e-06)
|
xHat |
matrix (n x k) of estimates for OD flows from calibration model, one time point per row |
varHat |
matrix (n x k) of estimated variances for OD flows from calibration, one time point per row |
phiHat |
numeric vector (length n) of estimates for phi from calibration model |
Y |
matrix (n x l) of observed link loads, one time point per row |
A |
routing matrix (l x k) for network; must be of full row rank |
phiPriorDf |
numeric prior convolution parameter for independent inverse-gamma priors on phi_t |
rho |
numeric fixed autoregressive parameter for dynamics on lambda; see reference for details |
backward |
logical to activate construction of reversed prior (for smoothing applications) |
lambdaMin |
numeric value at which to floor estimated OD flows for prior construction |
ipfp.maxit |
integer maximum number of iterations for IPFP |
ipfp.tol |
numeric tolerance for convergence of IPFP iterations |
list containing priors for lambda and phi, consisting of:
mu, a matrix (n x k) containing the prior means for the log-change in each lambda at each time
sigma, a matrix (n x k) containing the prior standard deviations for the log-change in each lambda at each time
a list phi, containing the numeric prior df
and a numeric vector scale
of length n
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 bayesianDynamicModel:
bayesianDynamicFilter
;
move_step
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