scholarly journals Sojourn time distributions in a Markovian G-queue with batch arrival and batch removal

1999 ◽  
Vol 12 (4) ◽  
pp. 339-356 ◽  
Author(s):  
Yang Woo Shin
Keyword(s):  
Markov Chains ◽  
Sojourn Time ◽  
First Passage Time ◽  
Passage Time ◽  
Batch Arrival ◽  
Single Server ◽  
Negative Customers ◽  
First Passage ◽  
Markovian Queue ◽  
Sojourn Time Distributions

We consider a single server Markovian queue with two types of customers; positive and negative, where positive customers arrive in batches and arrivals of negative customers remove positive customers in batches. Only positive customers form a queue and negative customers just reduce the system congestion by removing positive ones upon their arrivals. We derive the LSTs of sojourn time distributions for a single server Markovian queue with positive customers and negative customers by using the first passage time arguments for Markov chains.

Spectral Theory for Skip-Free Markov Chains

1989 ◽  
Vol 3 (1) ◽  
pp. 77-88 ◽  
Author(s):  
Joseph Abate ◽  
Ward Whitt
Keyword(s):  
Markov Chains ◽  
Spectral Theory ◽  
First Passage Time ◽  
Passage Time ◽  
First Passage Times ◽  
Simple Relationship ◽  
First Passage ◽  
Birth And Death Processes ◽  
The Laplace Transform ◽  
Passage Times

The distribution of upward first passage times in skip-free Markov chains can be expressed solely in terms of the eigenvalues in the spectral representation, without performing a separate calculation to determine the eigenvectors. We provide insight into this result and skip-free Markov chains more generally by showing that part of the spectral theory developed for birth-and-death processes extends to skip-free chains. We show that the eigenvalues and eigenvectors of skip-free chains can be characterized in terms of recursively defined polynomials. Moreover, the Laplace transform of the upward first passage time from 0 to n is the reciprocal of the nth polynomial. This simple relationship holds because the Laplace transforms of the first passage times satisfy the same recursion as the polynomials except for a normalization.

scholarly journals On the First Passage Time Distribution for a Class of Markov Chains

1983 ◽  
Vol 11 (4) ◽  
pp. 1000-1008 ◽  
Author(s):  
Mark Brown ◽  
Narasinga R. Chaganty
Keyword(s):  
Markov Chains ◽  
Time Distribution ◽  
First Passage Time ◽  
Passage Time ◽  
First Passage

Spectral structure of the first-passage-time densities for classes of Markov chains

1987 ◽  
Vol 24 (03) ◽  
pp. 631-643 ◽  
Author(s):  
Masaaki Kijima
Keyword(s):  
Markov Chains ◽  
First Passage Time ◽  
Passage Time ◽  
Death Process ◽  
Spectral Structure ◽  
First Passage ◽  
Completely Monotone ◽  
Spectral Representations ◽  
Absorbing States ◽  
Birth Death

Keilson [7] showed that for a birth-death process defined on non-negative integers with reflecting barrier at 0 the first-passage-time density from 0 to N (N to N + 1) has Pólya frequency of order infinity (is completely monotone). Brown and Chaganty [3] and Assaf et al. [1] studied the first-passage-time distribution for classes of discrete-time Markov chains and then produced the essentially same results as these through a uniformization. This paper addresses itself to an extension of Keilson's results to classes of Markov chains such as time-reversible Markov chains, skip-free Markov chains and birth-death processes with absorbing states. The extensions are due to the spectral representations of the infinitesimal generators governing these Markov chains. Explicit densities for those first-passage times are also given.

Spectral structure of the first-passage-time densities for classes of Markov chains

1987 ◽  
Vol 24 (3) ◽  
pp. 631-643 ◽  
Author(s):  
Masaaki Kijima
Keyword(s):  
Markov Chains ◽  
First Passage Time ◽  
Passage Time ◽  
Death Process ◽  
Spectral Structure ◽  
First Passage ◽  
Completely Monotone ◽  
Spectral Representations ◽  
Absorbing States ◽  
Birth Death

Keilson [7] showed that for a birth-death process defined on non-negative integers with reflecting barrier at 0 the first-passage-time density from 0 to N (N to N + 1) has Pólya frequency of order infinity (is completely monotone). Brown and Chaganty [3] and Assaf et al. [1] studied the first-passage-time distribution for classes of discrete-time Markov chains and then produced the essentially same results as these through a uniformization. This paper addresses itself to an extension of Keilson's results to classes of Markov chains such as time-reversible Markov chains, skip-free Markov chains and birth-death processes with absorbing states. The extensions are due to the spectral representations of the infinitesimal generators governing these Markov chains. Explicit densities for those first-passage times are also given.

Spectral Analysis, without Eigenvectors, for Markov Chains

1991 ◽  
Vol 5 (2) ◽  
pp. 131-144 ◽  
Author(s):  
Mark Brown
Keyword(s):  
Spectral Analysis ◽  
Markov Chains ◽  
Transition Probabilities ◽  
First Passage Time ◽  
Passage Time ◽  
Interpolation Polynomial ◽  
Simple Eigenvalue ◽  
First Passage ◽  
Free Representation ◽  
The Right

The Lagrange-Sylvester interpolation polynomial approach provides a simple, eigenvector-free representation for finite diagonalizable matrices. This paper discusses the Lagrange-Sylvester methodology and applies it to skip free to the right Markov chains. It leads to relatively simple, eigenvalue-based expressions for first passage time distributions and transition probabilities.

scholarly journals On State Occupancies, First Passage Times and Duration in Non-Homogeneous Semi-Markov Chains

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1745
Author(s):  
Andreas C. Georgiou ◽  
Alexandra Papadopoulou ◽  
Pavlos Kolias ◽  
Haris Palikrousis ◽  
Evanthia Farmakioti
Keyword(s):  
Markov Chains ◽  
Dna Sequences ◽  
First Passage Time ◽  
Passage Time ◽  
Markov Modeling ◽  
First Passage ◽  
State Duration ◽  
Time Required ◽  
Semi Markov Processes ◽  
Stochastic Phenomena

Semi-Markov processes generalize the Markov chains framework by utilizing abstract sojourn time distributions. They are widely known for offering enhanced accuracy in modeling stochastic phenomena. The aim of this paper is to provide closed analytic forms for three types of probabilities which describe attributes of considerable research interest in semi-Markov modeling: (a) the number of transitions to a state through time (Occupancy), (b) the number of transitions or the amount of time required to observe the first passage to a state (First passage time) and (c) the number of transitions or the amount of time required after a state is entered before the first real transition is made to another state (Duration). The non-homogeneous in time recursive relations of the above probabilities are developed and a description of the corresponding geometric transforms is produced. By applying appropriate properties, the closed analytic forms of the above probabilities are provided. Finally, data from human DNA sequences are used to illustrate the theoretical results of the paper.

On passage and conditional passage times for Markov chains in continuous time

1988 ◽  
Vol 25 (02) ◽  
pp. 279-290 ◽  
Author(s):  
Masaaki Kijima
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Continuous Time ◽  
First Passage Time ◽  
Passage Time ◽  
Death Process ◽  
First Passage ◽  
Reversible Markov Chain ◽  
Irreducible Markov Chain ◽  
Passage Times

Let X(t) be a temporally homogeneous irreducible Markov chain in continuous time defined on . For k < i < j, let H = {k + 1, ···, j − 1} and let kTij ( jTik ) be the upward (downward) conditional first-passage time of X(t) from i to j(k) given no visit to . These conditional passage times are studied through first-passage times of a modified chain HX(t) constructed by making the set of states absorbing. It will be shown that the densities of kTij and jTik for any birth-death process are unimodal and the modes kmij ( jmik ) of the unimodal densities are non-increasing (non-decreasing) with respect to i. Some distribution properties of kTij and jTik for a time-reversible Markov chain are presented. Symmetry among kTij, jTik , and is also discussed, where , and are conditional passage times of the reversed process of X(t).

First-passage-time moments of Markov processes

1985 ◽  
Vol 22 (4) ◽  
pp. 939-945 ◽  
Author(s):  
David D. Yao
Keyword(s):  
Markov Chains ◽  
Markov Processes ◽  
Continuous Time ◽  
Fundamental Matrix ◽  
Generalized Inverse ◽  
First Passage Time ◽  
Passage Time ◽  
First Passage ◽  
Continuous Time Markov Chains ◽  
Passage Times

We consider the first-passage times of continuous-time Markov chains. Based on the approach of generalized inverse, moments of all orders are derived and expressed in simple, explicit forms in terms of the ‘fundamental matrix'. The formulas are new and are also efficient for computation.

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