Package jline.api.lossn

Class Lossn_mci

java.lang.Object

jline.api.lossn.Lossn_mci

public final class Lossn_mci
extends Object

Monte Carlo importance-sampling summation for loss networks, after Ross and Wang, "Monte Carlo Summation Applied to Product-Form Loss Networks", Probability in the Engineering and Informational Sciences, 6 (1992), 323-348. Estimates the product-form normalization constant g(C) and per-class blocking probabilities with confidence intervals. Unlike the Erlang fixed point, no link-independence assumption is made and the estimator is consistent.

Method Summary

Modifier and Type	Method	Description
static Ret.lossnMCI	lossn_mci(Matrix nuVec, Matrix Amat, Matrix cVec, int samples, Matrix gammaVec, long seed, double alpha)	Monte Carlo importance-sampling summation for loss networks.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Details

lossn_mci

public static Ret.lossnMCI lossn_mci(Matrix nuVec,
 Matrix Amat,
 Matrix cVec,
 int samples,
 Matrix gammaVec,
 long seed,
 double alpha)

Monte Carlo importance-sampling summation for loss networks.

Parameters:

nuVec - Offered load per class (1xR).

Amat - Circuit requirement of link j for class r (JxR).

cVec - Link capacity (Jx1).

samples - Number of Monte Carlo samples.

gammaVec - Importance-sampling parameters (1xR), or null for the Section 3.4 heuristic.

seed - RNG seed; use a negative value for a nondeterministic seed.

alpha - Confidence-interval significance level (e.g. 0.05).