scholarly journals Double‐observer line transect surveys with Markov‐modulated Poisson process models for animal availability

Biometrics ◽  
2015 ◽  
Vol 71 (4) ◽  
pp. 1060-1069 ◽  
Author(s):  
D. L. Borchers ◽  
R. Langrock
Keyword(s):  
Poisson Process ◽  
Process Models ◽  
Line Transect ◽  
Markov Modulated Poisson Process ◽  
Markov Modulated ◽  
Double Observer

scholarly journals A bivariate two-state Markov modulated Poisson process for failure modeling

2021 ◽  
Vol 208 ◽  
pp. 107318
Author(s):  
Yoel G. Yera ◽  
Rosa E. Lillo ◽  
Bo F. Nielsen ◽  
Pepa Ramírez-Cobo ◽  
Fabrizio Ruggeri
Keyword(s):  
Poisson Process ◽  
Failure Modeling ◽  
Markov Modulated Poisson Process ◽  
Markov Modulated

scholarly journals Estimating the parameters of a seasonal Markov-modulated Poisson process

2015 ◽  
Vol 26 ◽  
pp. 103-123 ◽  
Author(s):  
Armelle Guillou ◽  
Stéphane Loisel ◽  
Gilles Stupfler
Keyword(s):  
Poisson Process ◽  
Markov Modulated Poisson Process ◽  
Markov Modulated

A monotonicity result for a single-server queue subject to a Markov-modulated Poisson process

1995 ◽  
Vol 32 (4) ◽  
pp. 1103-1111 ◽  
Author(s):  
Qing Du
Keyword(s):  
Poisson Process ◽  
Arrival Rate ◽  
Loss Probability ◽  
Arrival Process ◽  
Single Server ◽  
Single Server Queue ◽  
Markov Modulated Poisson Process ◽  
Server Queue ◽  
Monotonicity Result ◽  
Markov Modulated

Consider a single-server queue with zero buffer. The arrival process is a three-level Markov modulated Poisson process with an arbitrary transition matrix. The time the system remains at level i (i = 1, 2, 3) is exponentially distributed with rate cα i. The arrival rate at level i is λ i and the service time is exponentially distributed with rate μ i. In this paper we first derive an explicit formula for the loss probability and then prove that it is decreasing in the parameter c. This proves a conjecture of Ross and Rolski's for a single-server queue with zero buffer.

scholarly journals A simple multifractal model for self-similar traffic flows in high-speed computer networks

2021 ◽  
Author(s):  
Ginno Millán
Keyword(s):  
High Speed ◽  
Process Models ◽  
Traffic Modeling ◽  
Traffic Flows ◽  
Markov Modulated Poisson Process ◽  
Traffic Trace ◽  
Self Similar ◽  
Markov Modulated ◽  
Speed Computer ◽  
Fitting Model

This paper presents a simple and fast technique of multifractal traffic modeling. It proposes a method of fitting model to a given traffic trace. A comparison of simulation results obtained for an exemplary trace, multifractal model and Markov Modulated Poisson Process models has been performed.

A monotonicity result for a single-server queue subject to a Markov-modulated Poisson process

1995 ◽  
Vol 32 (04) ◽  
pp. 1103-1111 ◽  
Author(s):  
Qing Du
Keyword(s):  
Poisson Process ◽  
Arrival Rate ◽  
Loss Probability ◽  
Arrival Process ◽  
Single Server ◽  
Single Server Queue ◽  
Markov Modulated Poisson Process ◽  
Server Queue ◽  
Monotonicity Result ◽  
Markov Modulated

Consider a single-server queue with zero buffer. The arrival process is a three-level Markov modulated Poisson process with an arbitrary transition matrix. The time the system remains at level i (i = 1, 2, 3) is exponentially distributed with rate cα i . The arrival rate at level i is λ i and the service time is exponentially distributed with rate μ i . In this paper we first derive an explicit formula for the loss probability and then prove that it is decreasing in the parameter c. This proves a conjecture of Ross and Rolski's for a single-server queue with zero buffer.

scholarly journals Renewal characterization of Markov modulated Poisson processes

1989 ◽  
Vol 2 (1) ◽  
pp. 53-70 ◽  
Author(s):  
Marcel F. Neuts ◽  
Ushio Sumita ◽  
Yoshitaka Takahashi
Keyword(s):  
Markov Chain ◽  
Poisson Process ◽  
Renewal Process ◽  
Jump Process ◽  
Probability Vector ◽  
Markov Modulated Poisson Process ◽  
Markov Modulated ◽  
Bivariate Markov Chain ◽  
Characterization Theorems

A Markov Modulated Poisson Process (MMPP) M(t) defined on a Markov chain J(t) is a pure jump process where jumps of M(t) occur according to a Poisson process with intensity λi whenever the Markov chain J(t) is in state i. M(t) is called strongly renewal (SR) if M(t) is a renewal process for an arbitrary initial probability vector of J(t) with full support on P={i:λi>0}. M(t) is called weakly renewal (WR) if there exists an initial probability vector of J(t) such that the resulting MMPP is a renewal process. The purpose of this paper is to develop general characterization theorems for the class SR and some sufficiency theorems for the class WR in terms of the first passage times of the bivariate Markov chain [J(t),M(t)]. Relevance to the lumpability of J(t) is also studied.

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