A Gibbs Sampling-based approach for parameter estimation of the EGK distribution

We present a novel approach for estimating the parameters of the extended generalized-K (EGK) distri-bution commonly used as a fading model in wireless and optical communications links. The proposed method is based on the Gibbs sampling technique and does not require solving nonlinear equations nor performing numerical integrations. Numerical and simulation results are presented showing that the es-timated and original distributions are virtually indistinguishable and formal metrics like Kullback-Leibler (KL) divergence, the mean integrated squared bias (MISB), the mean integrated variance (MIV) and the mean integrated squared error (MISE) all show excellent agreement between the two as well. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

A novel approach for estimating parameters of the extended generalized-K (EGK) distribution is proposed based on Gibbs sampling technique. Results show that estimated and original distributions are virtually indistinguishable, and formal metrics like Kullback-Leibler (KL) divergence, mean integrated squared bias (MISB), mean integrated variance (MIV), and mean integrated squared error (MISE) all exhibit excellent agreement between the two.