Construct prior from calibration model estimates
Description
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
.
Usage
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 = 1e06)

Arguments
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 inversegamma 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 
Value
list containing priors for lambda and phi, consisting of:
mu, a matrix (n x k) containing the prior means for the logchange in each lambda at each time

sigma, a matrix (n x k) containing the prior standard deviations for the logchange in each lambda at each time
a list phi, containing the numeric prior
df
and a numeric vectorscale
of length n
References
A.W. Blocker and E.M. Airoldi. Deconvolution of mixing time series on a graph. Proceedings of the TwentySeventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI11) 5160, 2011.
See Also
Other bayesianDynamicModel:
bayesianDynamicFilter
;
move_step