Recurrence times of draft-patterns from reservoirs

1975 ◽  
Vol 12 (03) ◽  
pp. 647-652 ◽  
Author(s):  
G. G. S. Pegram
Keyword(s):  
Markov Chain ◽  
Discrete Time ◽  
Recurrence Time ◽  
Reservoir Model ◽  
Discrete State ◽  
Discrete Time Markov Chain ◽  
Recurrence Times ◽  
Mean And Variance ◽  
The Mean ◽  
Run Theory

Expressions for the mean and variance of the recurrence time of non-overlapping draft-patterns of draft from a Moran Reservoir Model (discrete-state and discrete-time Markov chain) are derived using Feller's Renewal argument. In addition an expression for the mean recurrence time for self-overlapping patterns of draft is derived using run-theory.

Recurrence times of draft-patterns from reservoirs

1975 ◽  
Vol 12 (3) ◽  
pp. 647-652 ◽  
Author(s):  
G. G. S. Pegram
Keyword(s):  
Markov Chain ◽  
Discrete Time ◽  
Recurrence Time ◽  
Reservoir Model ◽  
Discrete State ◽  
Discrete Time Markov Chain ◽  
Recurrence Times ◽  
Mean And Variance ◽  
The Mean ◽  
Run Theory

Expressions for the mean and variance of the recurrence time of non-overlapping draft-patterns of draft from a Moran Reservoir Model (discrete-state and discrete-time Markov chain) are derived using Feller's Renewal argument. In addition an expression for the mean recurrence time for self-overlapping patterns of draft is derived using run-theory.

scholarly journals ONE DIMENSIONAL ASYNCHRONOUS COOPERATIVE PARRONDO'S GAMES

2003 ◽  
Vol 03 (04) ◽  
pp. L389-L398 ◽  
Author(s):  
ZORAN MIHAILOVIĆ ◽  
MILAN RAJKOVIĆ
Keyword(s):  
Computer Simulation ◽  
Markov Chain ◽  
Discrete Time ◽  
Transition Probabilities ◽  
Mean Field ◽  
Discrete Time Markov Chain ◽  
One Dimensional ◽  
Field Approach ◽  
The Mean ◽  
The One

A discrete-time Markov chain solution with exact rules for general computation of transition probabilities of the one-dimensional cooperative Parrondo's games is presented. We show that winning and the occurrence of the paradox depends on the number of players. Analytical results are compared to the results of the computer simulation and to the results based on the mean-field approach.

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}} .

Some renewal process models for single neuron discharge

1971 ◽  
Vol 8 (4) ◽  
pp. 802-808 ◽  
Author(s):  
Howard G. Hochman ◽  
Stephen E. Fienberg
Keyword(s):  
Single Neuron ◽  
Interspike Interval ◽  
Poisson Processes ◽  
Process Models ◽  
Recurrence Time ◽  
The Laplace Transform ◽  
Mean And Variance ◽  
The Mean ◽  
Interspike Interval Distribution ◽  
Interval Distribution

Leslie (1969) obtained the Laplace transform for the recurrence time of clusters of Poisson processes, which can be thought of as yielding the interspike interval distribution for a neuron that receives Poisson excitatory inputs subject to decay. Here, several extensions of this model are derived, each including Poisson inhibitory inputs. Expressions for the mean and variance are derived for each model, and the results for the different models are compared.

Lumpability for non-irreducible finite markov chains

1984 ◽  
Vol 21 (03) ◽  
pp. 567-574 ◽  
Author(s):  
Atef M. Abdel-Moneim ◽  
Frederick W. Leysieffer
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
State Space ◽  
Discrete Time ◽  
The State ◽  
Discrete Time Markov Chain ◽  
Finite Markov Chains

Conditions under which a function of a finite, discrete-time Markov chain, X(t), is again Markov are given, when X(t) is not irreducible. These conditions are given in terms of an interrelationship between two partitions of the state space of X(t), the partition induced by the minimal essential classes of X(t) and the partition with respect to which lumping is to be considered.

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