move_step: Move step of sample-resample-move algorithm for multilevel...
In networkTomography: Tools for network tomography

Description Usage Arguments Value References See Also

Function to execute single MCMC-based move step for bayesianDynamicFilter. This can use two types of stopping rules: number of iterations or number of accepted moves for the X particles. The former is used by default, but the latter adapts better to low acceptance rates (sometimes with substantial computational cost). Most updates in this algorithm are Metropolis-Hastings with customized proposals.

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move_step(y, X, tme, lambda, phi, lambdatm1, phitm1, prior, A, A1_inv, A2, rho,
  tau, m = ncol(X), l = nrow(A1_inv), k = length(lambda), ndraws = 10,
  minAccepts = 0, verbose = FALSE)

`y`	numeric vector (length l) of observed link loads
`X`	matrix (m x k) of particles for OD flows, one particle per row, in pivoted order
`tme`	integer time index currently used in estimation
`lambda`	matrix (m x k) of particles for OD intensities, one particle per row, in pivoted order
`phi`	numeric vector (length m) of particles for phi
`lambdatm1`	lambda matrix (m x k) of particles for OD intensities from previous time, one particle per row, in pivoted order
`phitm1`	numeric vector (length m) of particles for phi from previous time
`prior`	list containing priors for hyperparameters; see `bayesianDynamicFilter` for details
`A`	routing matrix (l x k) for network
`A1_inv`	inverse of full-rank portion of routing matrix (l x l)
`A2`	remainder of routing matrix (l x k-l)
`rho`	numeric fixed autoregressive parameter for dynamics on lambda; see reference for details
`tau`	numeric fixed power parameter for variance structure on truncated normal noise; see reference for details
`m`	integer number of particles
`l`	integer number of observed link loads
`k`	integer number of OD flows to infer
`ndraws`	integer number of draws to perform (can be overriden by minAccepts)
`minAccepts`	integer minimum number of acceptances before results are returned; activates alternative stopping rule if >= 1
`verbose`	logical activates verbose diagnostic output

list containing updated values of X, lambda, and phi

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; buildPrior

networkTomography documentation built on May 2, 2019, 3:28 a.m.