Point processes generated by transitions of Markov chains

1973 ◽  
Vol 5 (2) ◽  
pp. 262-286 ◽  
Author(s):  
Mats Rudemo
Keyword(s):  
Markov Chain ◽  
Point Process ◽  
Point Processes ◽  
Queueing System ◽  
Initial Distribution ◽  
Recurrence Time ◽  
Asynchronous Sampling ◽  
Doubly Stochastic ◽  
Special Cases ◽  
Forward Recurrence Time

For a continuous time Markov chain the time points of transitions, belonging to a subset of the set of all transitions, are observed. Special cases include the point process generated by all transitions and doubly stochastic Poisson processes with a Markovian intensity. Equations are derived for the conditional distribution of the state of the Markov chain, given observations of the point process. This distribution may be used for prediction. For the forward recurrence time of the point process, distributions corresponding to synchronous and asynchronous sampling are also derived. The Palm distribution for the point process is specified in terms of the corresponding initial distribution for the Markov chain. In examples the point processes of arrivals and departures in a queueing system are studied. Two biological applications deal with estimation of population size and detection of epidemics.

Point processes generated by transitions of Markov chains

1973 ◽  
Vol 5 (02) ◽  
pp. 262-286 ◽  
Author(s):  
Mats Rudemo
Keyword(s):  
Markov Chain ◽  
Point Process ◽  
Point Processes ◽  
Queueing System ◽  
Initial Distribution ◽  
Recurrence Time ◽  
Asynchronous Sampling ◽  
Doubly Stochastic ◽  
Special Cases ◽  
Forward Recurrence Time

For a continuous time Markov chain the time points of transitions, belonging to a subset of the set of all transitions, are observed. Special cases include the point process generated by all transitions and doubly stochastic Poisson processes with a Markovian intensity. Equations are derived for the conditional distribution of the state of the Markov chain, given observations of the point process. This distribution may be used for prediction. For the forward recurrence time of the point process, distributions corresponding to synchronous and asynchronous sampling are also derived. The Palm distribution for the point process is specified in terms of the corresponding initial distribution for the Markov chain. In examples the point processes of arrivals and departures in a queueing system are studied. Two biological applications deal with estimation of population size and detection of epidemics.

scholarly journals Poisson approximation for some point processes in reliability

2004 ◽  
Vol 36 (2) ◽  
pp. 455-470 ◽  
Author(s):  
Jean-Bernard Gravereaux ◽  
James Ledoux
Keyword(s):  
Poisson Process ◽  
Point Process ◽  
Point Processes ◽  
Poisson Approximation ◽  
Arrival Process ◽  
Homogeneous Poisson Process ◽  
Doubly Stochastic ◽  
Doubly Stochastic Poisson Process ◽  
Finite Markov Process ◽  
Failure Point

In this paper, we consider a failure point process related to the Markovian arrival process defined by Neuts. We show that it converges in distribution to a homogeneous Poisson process. This convergence takes place in the context of rare occurrences of failures. We also provide a convergence rate of the convergence in total variation of this point process using an approach developed by Kabanov, Liptser and Shiryaev for the doubly stochastic Poisson process driven by a finite Markov process.

scholarly journals Poisson approximation for some point processes in reliability

2004 ◽  
Vol 36 (02) ◽  
pp. 455-470
Author(s):  
Jean-Bernard Gravereaux ◽  
James Ledoux
Keyword(s):  
Poisson Process ◽  
Point Process ◽  
Point Processes ◽  
Poisson Approximation ◽  
Arrival Process ◽  
Homogeneous Poisson Process ◽  
Doubly Stochastic ◽  
Doubly Stochastic Poisson Process ◽  
Finite Markov Process ◽  
Failure Point

In this paper, we consider a failure point process related to the Markovian arrival process defined by Neuts. We show that it converges in distribution to a homogeneous Poisson process. This convergence takes place in the context of rare occurrences of failures. We also provide a convergence rate of the convergence in total variation of this point process using an approach developed by Kabanov, Liptser and Shiryaev for the doubly stochastic Poisson process driven by a finite Markov process.

scholarly journals A new look at transient versions of Little's law, and M/G/1 preemptive last-come-first-served queues

2010 ◽  
Vol 47 (2) ◽  
pp. 459-473 ◽  
Author(s):  
Brian H. Fralix ◽  
Germán Riaño
Keyword(s):  
Point Processes ◽  
Queueing System ◽  
Queueing Systems ◽  
Time Dependent ◽  
Structural Form ◽  
Preemptive Resume ◽  
Special Cases ◽  
Number Of Customers ◽  
Regulated Brownian Motion ◽  
New Look

We take a new look at transient, or time-dependent Little laws for queueing systems. Through the use of Palm measures, we show that previous laws (see Bertsimas and Mourtzinou (1997)) can be generalized. Furthermore, within this framework, a new law can be derived as well, which gives higher-moment expressions for very general types of queueing system; in particular, the laws hold for systems that allow customers to overtake one another. What is especially novel about our approach is the use of Palm measures that are induced by nonstationary point processes, as these measures are not commonly found in the queueing literature. This new higher-moment law is then used to provide expressions for all moments of the number of customers in the system in an M/G/1 preemptive last-come-first-served queue at a time t > 0, for any initial condition and any of the more famous preemptive disciplines (i.e. preemptive-resume, and preemptive-repeat with and without resampling) that are analogous to the special cases found in Abate and Whitt (1987c), (1988). These expressions are then used to derive a nice structural form for all of the time-dependent moments of a regulated Brownian motion (see Abate and Whitt (1987a), (1987b)).

On the variance to mean ratio for random variables from Markov chains and point processes

1998 ◽  
Vol 35 (2) ◽  
pp. 303-312 ◽  
Author(s):  
Timothy C. Brown ◽  
Kais Hamza ◽  
Aihua Xia
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Point Process ◽  
Continuous Time ◽  
Infinitesimal Generator ◽  
Point Processes ◽  
Random Variables ◽  
Simple Point ◽  
Conditional Intensity

Criteria are determined for the variance to mean ratio to be greater than one (over-dispersed) or less than one (under-dispersed). This is done for random variables which are functions of a Markov chain in continuous time, and for the counts in a simple point process on the line. The criteria for the Markov chain are in terms of the infinitesimal generator and those for the point process in terms of the conditional intensity. Examples include a conjecture of Faddy (1994). The case of time-reversible point processes is particularly interesting, and here underdispersion is not possible. In particular, point processes which arise from Markov chains which are time-reversible, have finitely many states and are irreducible are always overdispersed.

Some non-stationary point processes with stationary forward recurrence time distribution

1975 ◽  
Vol 12 (1) ◽  
pp. 167-169 ◽  
Author(s):  
Mats Rudemo
Keyword(s):  
Stationary Point ◽  
Time Distribution ◽  
Point Processes ◽  
Recurrence Time ◽  
Renewal Processes ◽  
Forward Recurrence Time ◽  
Stationary Point Processes

Examples are given of point processes that are non-stationary but have stationary forward recurrence time distributions. They are obtained by modification of stationary Poisson and renewal processes.

scholarly journals A new look at transient versions of Little's law, and M/G/1 preemptive last-come-first-served queues

2010 ◽  
Vol 47 (02) ◽  
pp. 459-473 ◽  
Author(s):  
Brian H. Fralix ◽  
Germán Riaño
Keyword(s):  
Point Processes ◽  
Queueing System ◽  
Queueing Systems ◽  
Time Dependent ◽  
Structural Form ◽  
Preemptive Resume ◽  
Special Cases ◽  
Number Of Customers ◽  
Regulated Brownian Motion ◽  
New Look

We take a new look at transient, or time-dependent Little laws for queueing systems. Through the use of Palm measures, we show that previous laws (see Bertsimas and Mourtzinou (1997)) can be generalized. Furthermore, within this framework, a new law can be derived as well, which gives higher-moment expressions for very general types of queueing system; in particular, the laws hold for systems that allow customers to overtake one another. What is especially novel about our approach is the use of Palm measures that are induced by nonstationary point processes, as these measures are not commonly found in the queueing literature. This new higher-moment law is then used to provide expressions for all moments of the number of customers in the system in an M/G/1 preemptive last-come-first-served queue at a time t > 0, for any initial condition and any of the more famous preemptive disciplines (i.e. preemptive-resume, and preemptive-repeat with and without resampling) that are analogous to the special cases found in Abate and Whitt (1987c), (1988). These expressions are then used to derive a nice structural form for all of the time-dependent moments of a regulated Brownian motion (see Abate and Whitt (1987a), (1987b)).

scholarly journals Extensions of Markov Modulated Poisson Processes  and Their Applications to Deep Earthquakes

2021 ◽  
Author(s):  
◽  
Shaochuan Lu
Keyword(s):  
Markov Chain ◽  
New Zealand ◽  
Mutual Information ◽  
Point Process ◽  
Point Processes ◽  
Marked Point Processes ◽  
Information Rate ◽  
The Third ◽  
Deep Earthquakes ◽  
Markov Modulated

<p>The focus of this thesis is on the Markov modulated Poisson process (MMPP) and its extensions, aiming to propose appropriate statistical models for the occurrence patterns of main New Zealand deep earthquakes. Such an attempt might be beyond the scope of the MMPP and its extensions, however we hope its main patterns can be characterized by current models proposed in three parts of the thesis. The first part of the thesis is concerned with introductions and preliminaries of discrete time hidden Markov models (HMMs) and MMPP. The  exibility in model formulation and openness in model framework of HMMs are reviewed in this part, suggesting also possible extensions of MMPP. The second part of the thesis is mainly about several extensions of MMPP. One extension of MMPP is by associating each occurrence of MMPP with a mark. Such an extension is potentially useful for spatial-temporal modelling or other point  processes with marks. A special case of this type of extension is by allowing the multiple observations of MMPP synchronized together under the same Markov chain. This extension opens the possibility of modelling multiple point process observations with weak dependence. The third extension is motivated by the attempt to describe small scale temporal clustering existing in the deep earthquakes via treating the recognized aftershocks as marks which itself forms a finite point process. The rest of the second part focuses on some information theoretical aspects of MMPPs such as the entropy rate of the underlying Markov chain and observed point process respectively and their mutual information rate. A conjecture on the possible links between mutual information rate of MMPP and the Fisher information of the estimated parameters is suggested. The second part on extensions of MMPP is featured by the derivation of the likelihood and complete likelihood, parameter estimation via EM algorithm, state smoothing estimation and model evaluation through systematic applications of rescaling theory of multivariate point processes and marked point processes. The third part of the thesis includes the applications of these methods to the deep earthquakes in New Zealand. We first evaluate the data coverage, catalogue completeness and explore its descriptive characteristics and empirical properties such as epicentral distributions, depth distributions and magnitude distributions.  Clustering behavior is studied via the second order moment analysis of point processes in the chapter 8. We also apply, the stress release models and the ETAS models which are usually used for shallow earthquakes, to the New Zealand deep earthquakes and provide tentative explanations of why they are not satisfactory for the deep earth-quakes. The chapter 9 is on the applications of MMPP and its extensions to the New Zealand deep earthquakes. Conclusions and future studies are presented in chapter 10.</p>

scholarly journals A GENERALIZED CONTAGION PROCESS WITH AN APPLICATION TO CREDIT RISK

2017 ◽  
Vol 20 (01) ◽  
pp. 1750003 ◽  
Author(s):  
ANGELOS DASSIOS ◽  
HONGBIAO ZHAO
Keyword(s):  
Credit Risk ◽  
Point Process ◽  
Point Processes ◽  
Probability Generating Function ◽  
Jump Processes ◽  
Hawkes Process ◽  
Cox Process ◽  
Model Framework ◽  
Special Cases ◽  
The Laplace Transform

We introduce a class of analytically tractable jump processes with contagion effects by generalizing the classical Hawkes process. This model framework combines the characteristics of three popular point processes in the literature: (1) Cox process with CIR intensity; (2) Cox process with Poisson shot-noise intensity; (3) Hawkes process with exponentially decaying intensity. Hence, it can be considered as a self-exciting and externally-exciting point process with mean-reverting stochastic intensity. Essential probabilistic properties such as moments, the Laplace transform of intensity process, and the probability generating function of point process as well as some important asymptotics have been derived. Some special cases and a method for change of measure are discussed. This point process may be applicable to modeling contagious arrivals of events for various circumstances (such as jumps, transactions, losses, defaults, catastrophes) in finance, insurance and economics with both endogenous and exogenous risk factors within one framework. More specifically, these exogenous factors could contain relatively short-lived shocks and long-lasting risk drivers. We make a simple application to calculate the default probability for credit risk and to price defaultable zero-coupon bonds.

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