scholarly journals Elementary methods for failure due to a sequence of Markovian events

1998 ◽  
Vol 11 (3) ◽  
pp. 311-318
Author(s):  
J. Gani
Keyword(s):  
Markov Chain ◽  
Generating Function ◽  
Continuous Time ◽  
Probability Generating Function ◽  
System Failure ◽  
Specific Sequence ◽  
Sequence Of Events ◽  
Elementary Methods

This paper is concerned with elementary methods for evaluating the distribution of the time to system failure, following a particular sequence of events from a Markov chain. After discussing a simple example in which a specific sequence from a two-state Markov chain leads to failure, the method is generalized to a sequence from a (k>2)-state chain. The expectation and variance of the time T to failure can be obtained from the probability generating function (p.g.f.) of T. The method can be extended to the case of continuous time.

A carrier-borne epidemic with multiple stages of infection

1991 ◽  
Vol 28 (01) ◽  
pp. 1-8 ◽  
Author(s):  
J. Gani ◽  
Gy. Michaletzky
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Markov Chain ◽  
Generating Function ◽  
Continuous Time ◽  
Probability Generating Function ◽  
Death Process ◽  
Partial Differential ◽  
Multiple Stages

This paper considers a carrier-borne epidemic in continuous time with m + 1 > 2 stages of infection. The carriers U(t) follow a pure death process, mixing homogeneously with susceptibles X 0(t), and infectives Xi (t) in stages 1≦i≦m of infection. The infectives progress through consecutive stages of infection after each contact with the carriers. It is shown that under certain conditions {X 0(t), X 1(t), · ··, Xm (t) U(t); t≧0} is an (m + 2)-variate Markov chain, and the partial differential equation for its probability generating function derived. This can be solved after a transfomation of variables, and the probability of survivors at the end of the epidemic found.

A carrier-borne epidemic with multiple stages of infection

1991 ◽  
Vol 28 (1) ◽  
pp. 1-8 ◽  
Author(s):  
J. Gani ◽  
Gy. Michaletzky
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Markov Chain ◽  
Generating Function ◽  
Continuous Time ◽  
Probability Generating Function ◽  
Death Process ◽  
Partial Differential ◽  
Multiple Stages

This paper considers a carrier-borne epidemic in continuous time with m + 1 > 2 stages of infection. The carriers U(t) follow a pure death process, mixing homogeneously with susceptibles X0(t), and infectives Xi(t) in stages 1≦i≦m of infection. The infectives progress through consecutive stages of infection after each contact with the carriers. It is shown that under certain conditions {X0(t), X1(t), · ··, Xm(t) U(t); t≧0} is an (m + 2)-variate Markov chain, and the partial differential equation for its probability generating function derived. This can be solved after a transfomation of variables, and the probability of survivors at the end of the epidemic found.

On the absorption probabilities and mean time for absorption for discrete Markov chains

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nikolaos Halidias
Keyword(s):  
Markov Chain ◽  
Random Walk ◽  
Markov Chains ◽  
Generating Function ◽  
Discrete Time ◽  
Probability Generating Function ◽  
The Mean ◽  
Mean Time ◽  
Absorption Probabilities

Abstract In this note we study the probability and the mean time for absorption for discrete time Markov chains. In particular, we are interested in estimating the mean time for absorption when absorption is not certain and connect it with some other known results. Computing a suitable probability generating function, we are able to estimate the mean time for absorption when absorption is not certain giving some applications concerning the random walk. Furthermore, we investigate the probability for a Markov chain to reach a set A before reach B generalizing this result for a sequence of sets A 1 , A 2 , … , A k {A_{1},A_{2},\dots,A_{k}} .

scholarly journals Period-Life of a Branching Process with Migration and Continuous Time

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 868
Author(s):  
Khrystyna Prysyazhnyk ◽  
Iryna Bazylevych ◽  
Ludmila Mitkova ◽  
Iryna Ivanochko
Keyword(s):  
Random Process ◽  
Generating Function ◽  
Continuous Time ◽  
Branching Process ◽  
Probability Generating Function ◽  
Time Interval ◽  
The Moment ◽  
First Time ◽  
Critical Branching Process ◽  
Number Of Particles

The homogeneous branching process with migration and continuous time is considered. We investigated the distribution of the period-life τ, i.e., the length of the time interval between the moment when the process is initiated by a positive number of particles and the moment when there are no individuals in the population for the first time. The probability generating function of the random process, which describes the behavior of the process within the period-life, was obtained. The boundary theorem for the period-life of the subcritical or critical branching process with migration was found.

On the probability generating function of the sum of Markov Bernoulli random variables

1982 ◽  
Vol 19 (A) ◽  
pp. 321-326 ◽  
Author(s):  
J. Gani
Keyword(s):  
Markov Chain ◽  
Generating Function ◽  
Transition Probability ◽  
Probability Generating Function ◽  
Random Variables ◽  
Direct Proof ◽  
Transition Probability Matrix ◽  
Bernoulli Random Variables ◽  
Limit Probability

A direct proof of the expression for the limit probability generating function (p.g.f.) of the sum of Markov Bernoulli random variables is outlined. This depends on the larger eigenvalue of the transition probability matrix of their Markov chain.

The number of individuals in a cascade process

1949 ◽  
Vol 45 (3) ◽  
pp. 360-363 ◽  
Author(s):  
I. J. Good
Keyword(s):  
Generating Function ◽  
Continuous Time ◽  
Probability Generating Function ◽  
Time Parameter ◽  
Cascade Process ◽  
Discrete Process ◽  
Number Of Children ◽  
Image Position ◽  
New Generation ◽  
Number Of Individuals

A number of important Markoff processes, with a continuous time parameter, can be represented approximately by a discrete process, interesting in its own right, of the following type. A class of individuals gives rise seasonally (in January say) to a number of new individuals (children), the probabilities of an individual having 0, 1, 2, … children being p0, p1, p2, …. These probabilities are the same for all individuals and are independent. The individuals formed each January are regarded as a new generation, and only this generation is capable of reproducing in the next January. Letso that F(x) is the probability generating function (p.g.f.) of the number of children of an individual. Clearly the series for F(x) is absolutely convergent when |x| < |1.

A continuous-time population model with poisson recruitment

1976 ◽  
Vol 13 (2) ◽  
pp. 348-354 ◽  
Author(s):  
Sally I. McClean
Keyword(s):  
Generating Function ◽  
Continuous Time ◽  
Population Model ◽  
Probability Generating Function ◽  
Joint Probability ◽  
Limiting Distribution ◽  
Continuous Time Model ◽  
Time Model ◽  
Second Moments ◽  
Poisson Arrivals

A continuous-time model of a multigrade system is developed, which includes Poisson arrivals, interaction between grades and a leaving process. It therefore constitutes a continuous-time analogue of Pollard's hierarchical population model with Poisson recruitment. An expression is found for the first and second moments of grade size at any time. A general formulation of the joint probability generating function of the numbers in each grade is given, and the limiting distribution of grade size is shown to be Poisson.

On the probability generating function of the sum of Markov Bernoulli random variables

1982 ◽  
Vol 19 (A) ◽  
pp. 321-326 ◽  
Author(s):  
J. Gani
Keyword(s):  
Markov Chain ◽  
Generating Function ◽  
Transition Probability ◽  
Probability Generating Function ◽  
Random Variables ◽  
Direct Proof ◽  
Transition Probability Matrix ◽  
Bernoulli Random Variables ◽  
Limit Probability

A direct proof of the expression for the limit probability generating function (p.g.f.) of the sum of Markov Bernoulli random variables is outlined. This depends on the larger eigenvalue of the transition probability matrix of their Markov chain.

Stochastic Models for Type Counts in a Literary Text

1975 ◽  
Vol 12 (S1) ◽  
pp. 313-323
Author(s):  
J. Gani
Keyword(s):  
Markov Chain ◽  
Discrete Time ◽  
Stochastic Models ◽  
Continuous Time ◽  
Markov Chain Model ◽  
Probability Generating Function ◽  
Literary Text ◽  
Homogeneous Markov Chain ◽  
Chain Model ◽  
Continuous Time Markov Chain

This paper studies a Markov chain model for type counts {Xn} in a literary text. First, a homogeneous Markov chain in discrete time is considered. This is then embedded in a continuous time Poisson process; the probability generating function for the resulting continuous time Markov chain is obtained. Expectations and variances of type counts are found for different values of the token count and various sizes M of an author's vocabulary; these results are finally tested against known data for three of Shakespeare's plays.

A continuous-time population model with poisson recruitment

1976 ◽  
Vol 13 (02) ◽  
pp. 348-354 ◽  
Author(s):  
Sally I. McClean
Keyword(s):  
Generating Function ◽  
Continuous Time ◽  
Population Model ◽  
Probability Generating Function ◽  
Joint Probability ◽  
Limiting Distribution ◽  
Continuous Time Model ◽  
Time Model ◽  
Second Moments ◽  
Poisson Arrivals

A continuous-time model of a multigrade system is developed, which includes Poisson arrivals, interaction between grades and a leaving process. It therefore constitutes a continuous-time analogue of Pollard's hierarchical population model with Poisson recruitment. An expression is found for the first and second moments of grade size at any time. A general formulation of the joint probability generating function of the numbers in each grade is given, and the limiting distribution of grade size is shown to be Poisson.

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