TRANSIENT ANALYSIS OF A MULTI-COMPONENT SYSTEM MODELED BY A GENERAL MARKOV PROCESS

2006 ◽  
Vol 23 (03) ◽  
pp. 311-327 ◽  
Author(s):  
RAFAEL PÉREZ-OCÓN ◽  
DELIA MONTORO-CAZORLA ◽  
JUAN ELOY RUIZ-CASTRO
Keyword(s):  
Markov Process ◽  
Performance Measures ◽  
Transient Analysis ◽  
Transition Probabilities ◽  
Dynamic Environment ◽  
General Interest ◽  
System A ◽  
Phase Type ◽  
Phase Type Distributions ◽  
Reliability Systems

An M-unit system in dynamic environment with operational and repair times following phase-type distributions and incorporating geometrical processes is considered. A general Markov process with vectorial states is the appropriate structure for modeling this system. A transient analysis is performed for this complex system and the transition probabilities are calculated. Some performance measures of general interest in the study of systems are obtained using an algorithmic approach, and applied to G-out-of-M systems. A numerical example is presented and the transient performance measures are calculated and compared with the stationary ones. This paper extends previous reliability systems, that can be considered as particular cases of this one. Throughout the paper, the mathematical expressions are given by algorithmic methods, that emphasized the utility of phase-type distributions in the analysis of lifetime data.

scholarly journals Estimation of aggregate losses of secondary cancer cases using PH Panjer class (a,b,1) distributions,

2021 ◽  
Vol 16 (4) ◽  
pp. 3941-3959
Author(s):  
Cynthia Mwende Mwau ◽  
Patrick Guge Weke ◽  
Bundi Davis Ntwiga ◽  
Joseph Makoteku Ottieno
Keyword(s):  
Transition Probabilities ◽  
Generalized Pareto Distribution ◽  
Kolmogorov Equation ◽  
Claim Amount ◽  
Secondary Cancer ◽  
Class A ◽  
Generalized Pareto ◽  
Phase Type ◽  
Phase Type Distributions ◽  
Aggregate Loss

This research in-cooperates phase type distributions of Panjer class \((a,b,1)\) in estimation of aggregate loss probabilities of secondary cancer. Matrices of the phase type distributions are derived using Chapman-Kolmogorov equation and transition probabilities estimated using modified Kaplan-Meir and consequently the transition intensities and transition probabilities. Stationary probabilities of the Markov chains represents $\vec{\gamma}$. Claim amount are modeled using OPPL, TPPL, Pareto, Generalized Pareto and Wei-bull distributions. PH ZT Poisson with Generalized Pareto distribution provided the best fit.

MAINTENANCE OF SYSTEMS BY MEANS OF REPLACEMENTS AND REPAIRS: THE CASE OF PHASE-TYPE DISTRIBUTIONS

2007 ◽  
Vol 24 (03) ◽  
pp. 421-434 ◽  
Author(s):  
DELIA MONTORO-CAZORLA ◽  
RAFAEL PÉREZ-OCÓN
Keyword(s):  
Performance Measures ◽  
Markov Processes ◽  
Stationary Regime ◽  
Numerical Example ◽  
Imperfect Repair ◽  
Phase Type ◽  
Phase Type Distributions ◽  
Comparison Of The Results ◽  
Imperfect Repairs

We present two models for studying a system maintained by means of imperfect repairs before a replacement or a perfect repair is allowed. The operational and repair times follow phase-type distributions. Imperfect repair means that successive operational times decrease and successive repair times increase. Under these assumptions, models that govern the systems are Markov processes, whose structures are determined, and several performance measures are calculated in transient and stationary regime. These models extend other previously studied in the literature. The incorporation of phase-type distributions allows to apply the model to many other distributions. A numerical example illustrates the calculations and allows a comparison of the results.

Repairable models with operating and repair times governed by phase type distributions

2000 ◽  
Vol 32 (02) ◽  
pp. 468-479 ◽  
Author(s):  
Marcel F. Neuts ◽  
Rafael Pérez-Ocón ◽  
Inmaculada Torres-Castro
Keyword(s):  
Markov Process ◽  
Stationary Distribution ◽  
Operating Time ◽  
Fixed Number ◽  
Geometric Process ◽  
Phase Type ◽  
Phase Type Distributions ◽  
Different Types ◽  
The Times ◽  
Quantities Of Interest

We consider a device that is subject to three types of failures: repairable, non-repairable and failures due to wear-out. This last type is also non-repairable. The times when the system is operative or being repaired follow phase type distributions. When a repairable failure occurs, the operating time of the device decreases, in that the lifetimes between failures are stochastically decreasing according to a geometric process. Following a non-repairable failure or after a previously fixed number of repairs occurs, the device is replaced by a new one. Under these conditions, the functioning of the device can be modelled by a Markov process. We consider two different models depending on whether or not the phase of the operational system at the instants of failure is remembered or not. For both models we derive the stationary distribution of the Markov process, the availability of the device, the rate of occurrence of the different types of failures, and certain quantities of interest.

Repairable models with operating and repair times governed by phase type distributions

2000 ◽  
Vol 32 (2) ◽  
pp. 468-479 ◽  
Author(s):  
Marcel F. Neuts ◽  
Rafael Pérez-Ocón ◽  
Inmaculada Torres-Castro
Keyword(s):  
Markov Process ◽  
Stationary Distribution ◽  
Operating Time ◽  
Fixed Number ◽  
Geometric Process ◽  
Phase Type ◽  
Phase Type Distributions ◽  
Different Types ◽  
The Times ◽  
Quantities Of Interest

We consider a device that is subject to three types of failures: repairable, non-repairable and failures due to wear-out. This last type is also non-repairable. The times when the system is operative or being repaired follow phase type distributions. When a repairable failure occurs, the operating time of the device decreases, in that the lifetimes between failures are stochastically decreasing according to a geometric process. Following a non-repairable failure or after a previously fixed number of repairs occurs, the device is replaced by a new one. Under these conditions, the functioning of the device can be modelled by a Markov process. We consider two different models depending on whether or not the phase of the operational system at the instants of failure is remembered or not. For both models we derive the stationary distribution of the Markov process, the availability of the device, the rate of occurrence of the different types of failures, and certain quantities of interest.

scholarly journals A MARKOV PROCESS OF GENE FREQUENCY CHANGE IN A GEOGRAPHICALLY STRUCTURED POPULATION

Genetics ◽  
1974 ◽  
Vol 76 (2) ◽  
pp. 367-377
Author(s):  
Takeo Maruyama
Keyword(s):  
Genetic Variation ◽  
Markov Process ◽  
Diffusion Process ◽  
Gene Frequency ◽  
Transition Probabilities ◽  
Large Population ◽  
Sample Path ◽  
Time Parameter ◽  
Frequency Change ◽  
Structured Population

ABSTRACT A Markov process (chain) of gene frequency change is derived for a geographically-structured model of a population. The population consists of colonies which are connected by migration. Selection operates in each colony independently. It is shown that there exists a stochastic clock that transforms the originally complicated process of gene frequency change to a random walk which is independent of the geographical structure of the population. The time parameter is a local random time that is dependent on the sample path. In fact, if the alleles are selectively neutral, the time parameter is exactly equal to the sum of the average local genetic variation appearing in the population, and otherwise they are approximately equal. The Kolmogorov forward and backward equations of the process are obtained. As a limit of large population size, a diffusion process is derived. The transition probabilities of the Markov chain and of the diffusion process are obtained explicitly. Certain quantities of biological interest are shown to be independent of the population structure. The quantities are the fixation probability of a mutant, the sum of the average local genetic variation and the variation summed over the generations in which the gene frequency in the whole population assumes a specified value.

Phase Type Distributions with Finite Support

2014 ◽  
Vol 30 (4) ◽  
pp. 576-597 ◽  
Author(s):  
V. Ramaswami ◽  
N. C. Viswanath
Keyword(s):  
Finite Support ◽  
Phase Type ◽  
Phase Type Distributions

Analysis of R-out-of-N repairable systems: the case of phase-type distributions

2004 ◽  
Vol 36 (1) ◽  
pp. 116-138 ◽  
Author(s):  
Yonit Barron ◽  
Esther Frostig ◽  
Benny Levikson
Keyword(s):  
Repairable System ◽  
Repairable Systems ◽  
Regenerative Processes ◽  
Numerical Examples ◽  
Independent Components ◽  
Duality Results ◽  
Phase Type ◽  
Phase Type Distributions ◽  
Two Systems ◽  
Repair Facility

An R-out-of-N repairable system, consisting of N independent components, is operating if at least R components are functioning. The system fails whenever the number of good components decreases from R to R-1. A failed component is sent to a repair facility. After a failed component has been repaired it is as good as new. Formulae for the availability of the system using Markov renewal and semi-regenerative processes are derived. We assume that either the repair times of the components are generally distributed and the components' lifetimes are phase-type distributed or vice versa. Some duality results between the two systems are obtained. Numerical examples are given for several distributions of lifetimes and of repair times.

scholarly journals The Algebraic Degree of Phase-Type Distributions

2010 ◽  
Vol 47 (03) ◽  
pp. 611-629
Author(s):  
Mark Fackrell ◽  
Qi-Ming He ◽  
Peter Taylor ◽  
Hanqin Zhang
Keyword(s):  
Lower Bound ◽  
Upper Bound ◽  
Polynomial Algorithm ◽  
Algebraic Degree ◽  
Nonnegative Matrices ◽  
Stieltjes Transform ◽  
Special Cases ◽  
Phase Type ◽  
Phase Type Distributions ◽  
The Matrix

This paper is concerned with properties of the algebraic degree of the Laplace-Stieltjes transform of phase-type (PH) distributions. The main problem of interest is: given a PH generator, how do we find the maximum and the minimum algebraic degrees of all irreducible PH representations with that PH generator? Based on the matrix exponential (ME) order of ME distributions and the spectral polynomial algorithm, a method for computing the algebraic degree of a PH distribution is developed. The maximum algebraic degree is identified explicitly. Using Perron-Frobenius theory of nonnegative matrices, a lower bound and an upper bound on the minimum algebraic degree are found, subject to some conditions. Explicit results are obtained for special cases.

Efficient fitting of long-tailed data sets into phase-type distributions

2002 ◽  
Vol 30 (3) ◽  
pp. 6-8 ◽  
Author(s):  
Alma Riska ◽  
Vesselin Diev ◽  
Evgenia Smirni
Keyword(s):  
Data Sets ◽  
Phase Type ◽  
Phase Type Distributions
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