Occupation times for countable Markov chains. I. Chains with discrete time

1984 ◽  
Vol 27 (5) ◽  
pp. 3022-3038 ◽  
Author(s):  
S. S. Vallander
Keyword(s):  
Markov Chains ◽  
Discrete Time ◽  
Occupation Times

Occupation times for nonhomogeneous Markov chains with discrete time

1992 ◽  
Vol 61 (1) ◽  
pp. 1846-1852
Author(s):  
S. S. Vallander
Keyword(s):  
Markov Chains ◽  
Discrete Time ◽  
Occupation Times ◽  
Nonhomogeneous Markov Chains

Occupation times for two state Markov chains

1971 ◽  
Vol 8 (02) ◽  
pp. 381-390 ◽  
Author(s):  
P. J. Pedler
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Discrete Time ◽  
Transition Probabilities ◽  
Random Variables ◽  
Time Parameter ◽  
Conditional Probabilities ◽  
Occupation Times ◽  
The Matrix ◽  
Image Position

Consider first a Markov chain with two ergodic states E 1 and E 2, and discrete time parameter set {0, 1, 2, ···, n}. Define the random variables Z 0, Z 1, Z 2, ···, Zn by then the conditional probabilities for k = 1,2,···, n, are independent of k. Thus the matrix of transition probabilities is

Occupation times for two state Markov chains

1971 ◽  
Vol 8 (2) ◽  
pp. 381-390 ◽  
Author(s):  
P. J. Pedler
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Discrete Time ◽  
Transition Probabilities ◽  
Random Variables ◽  
Time Parameter ◽  
Conditional Probabilities ◽  
Occupation Times ◽  
The Matrix ◽  
Image Position

Consider first a Markov chain with two ergodic states E1 and E2, and discrete time parameter set {0, 1, 2, ···, n}. Define the random variables Z0, Z1, Z2, ···, Znby then the conditional probabilities for k = 1,2,···, n, are independent of k. Thus the matrix of transition probabilities is

Discrete-time Markov chains

2011 ◽  
pp. 1-184
Author(s):  
Yuri Suhov ◽  
Mark Kelbert
Keyword(s):  
Markov Chains ◽  
Discrete Time

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}} .

On quasi-stationary distributions in absorbing continuous-time finite Markov chains

1967 ◽  
Vol 4 (1) ◽  
pp. 192-196 ◽  
Author(s):  
J. N. Darroch ◽  
E. Seneta
Keyword(s):  
Markov Chains ◽  
Discrete Time ◽  
Continuous Time ◽  
Present Note ◽  
Time Parameter ◽  
Stationary Distributions ◽  
Finite State ◽  
Absorbing Markov Chains ◽  
Finite Markov Chains ◽  
Finite State Space

In a recent paper, the authors have discussed the concept of quasi-stationary distributions for absorbing Markov chains having a finite state space, with the further restriction of discrete time. The purpose of the present note is to summarize the analogous results when the time parameter is continuous.

Discrete-Time Markov Chains [Book Review]

2005 ◽  
Vol 25 (6) ◽  
pp. 106-108
Keyword(s):  
Markov Chains ◽  
Discrete Time

scholarly journals Ruin probability with certain stationary stable claims generated by conservative flows

2007 ◽  
Vol 39 (02) ◽  
pp. 360-384 ◽  
Author(s):  
Uğur Tuncay Alparslan ◽  
Gennady Samorodnitsky
Keyword(s):  
Markov Chains ◽  
Discrete Time ◽  
Continuous Time ◽  
Ruin Probability ◽  
Stable Process ◽  
Independent Interest ◽  
Stable Processes ◽  
Auxiliary Result ◽  
Initial Capital ◽  
Order Of Magnitude

We study the ruin probability where the claim sizes are modeled by a stationary ergodic symmetric α-stable process. We exploit the flow representation of such processes, and we consider the processes generated by conservative flows. We focus on two classes of conservative α-stable processes (one discrete-time and one continuous-time), and give results for the order of magnitude of the ruin probability as the initial capital goes to infinity. We also prove a solidarity property for null-recurrent Markov chains as an auxiliary result, which might be of independent interest.

Discrete-time ‘inversion’ and derivative estimation for Markov chains

1990 ◽  
Vol 9 (5) ◽  
pp. 305-313 ◽  
Author(s):  
Paul Glasserman
Keyword(s):  
Markov Chains ◽  
Discrete Time ◽  
Time Inversion ◽  
Derivative Estimation
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