Inference (infer)

Demand and parameter estimation from measurements: maximum likelihood, fluid likelihood, variational methods and the queue-length reconstruction they consume. More...

Collaboration diagram for Inference (infer):

Files
file	include/line/api/infer/infer_compute_ql_at_arrival.h
	Per-class queue lengths seen by each arriving job, reconstructed from arrival and response time samples.
file	include/line/api/infer/infer_fmlps.h
	Fluid response-time likelihood, and the FMLPS demand estimator built on it.
file	include/line/api/infer/infer_get_qlen_arrival.h
	Per-class queue lengths at arrival for the per-class sample format.
file	include/line/api/infer/infer_gibbs.h
	Gibbs sampling demand estimator for a closed delay-plus-queue model.
file	include/line/api/infer/infer_lqn.h
	Identify hidden LQN parameters from measured performance data.
file	include/line/api/infer/infer_lqn_ekf.h
	Extended Kalman Filter for LQN parameter identification.
file	include/line/api/infer/infer_lqn_findbyname.h
	First element of a named LQN container matching a name.
file	include/line/api/infer/infer_lqn_getobs.h
	Observation vector of a solved LQN, z = h(a).
file	include/line/api/infer/infer_lqn_jacobian.h
	Forward finite-difference sensitivity matrix of an observation map.
file	include/line/api/infer/infer_lqn_setparams.h
	Apply a parameter vector to a LayeredNetworkStruct, and read it back.
file	include/line/api/infer/infer_minps_setup.h
	Turn a raw per-class trace into the sample set MINPS estimates from.
file	include/line/api/infer/infer_mlps.h
	Maximum-likelihood service-demand estimation at a processor-sharing queue.
file	include/line/api/infer/infer_nhpp_ks.h
	Kolmogorov-Smirnov tests for a non-homogeneous Poisson arrival process.
file	include/line/api/infer/infer_qmle.h
	Queue-length-based maximum-likelihood estimator of the service demands of a closed queueing network.
file	include/line/api/infer/infer_rps.h
	Regression for Processor Sharing (RPS) demand estimator.
file	include/line/api/infer/infer_variational.h
	Variational inference for Markovian queueing networks.

Detailed Description

Demand and parameter estimation from measurements: maximum likelihood, fluid likelihood, variational methods and the queue-length reconstruction they consume.