Findings about the BMMPP for modeling dependent and simultaneous data in reliability and queueing systems

  The batch Markov‐modulated Poisson process (BMMPP) is a subclass of the versatile batch Markovian arrival process (BMAP), which has been widely used for the modeling of dependent and correlated simultaneous events (as arrivals, failures, or risk events). Both theoretical and applied aspects are ex...

Full description

Saved in:
Bibliographic Details
Published in:Applied stochastic models in business and industry 2019-03, Vol.35 (2), p.177-190
Main Authors: Yera, Yoel G., Lillo, Rosa E., Ramírez‐Cobo, Pepa
Format: Article
Language:English
Subjects:
batch Markovian arrival process (BMAP)
Consumer goods
correlation structure
identifiability
Markov analysis
Markov chains
Markov‐modulated poisson process (MMPP)
Modelling
Poisson density functions
Queues
Queuing theory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:  The batch Markov‐modulated Poisson process (BMMPP) is a subclass of the versatile batch Markovian arrival process (BMAP), which has been widely used for the modeling of dependent and correlated simultaneous events (as arrivals, failures, or risk events). Both theoretical and applied aspects are examined in this paper. On one hand, the identifiability of the stationary BMMPPm(K ) is proven, where K is the maximum batch size and m is the number of states of the underlying Markov chain. This is a powerful result for inferential issues. On the other hand, some novelties related to the correlation and autocorrelation structures are provided.
ISSN:1524-1904
1526-4025
DOI:10.1002/asmb.2327