On sums of random variables defined on a two-state Markov chain

1970 ◽  
Vol 7 (3) ◽  
pp. 761-765 ◽  
Author(s):  
H. J. Helgert
Keyword(s):  
Markov Chain ◽  
Transition Probabilities ◽  
Random Variables ◽  
Homogeneous Markov Chain ◽  
Sums Of Random Variables ◽  
Image Position

Assume the sequence of random variables x0, x1, x2, ··· forms a two-state, homogeneous Markov chain with transition probabilities and initial probabilities

On sums of random variables defined on a two-state Markov chain

1970 ◽  
Vol 7 (03) ◽  
pp. 761-765 ◽  
Author(s):  
H. J. Helgert
Keyword(s):  
Markov Chain ◽  
Transition Probabilities ◽  
Random Variables ◽  
Homogeneous Markov Chain ◽  
Sums Of Random Variables ◽  
Image Position

Assume the sequence of random variables x 0, x 1, x 2, ··· forms a two-state, homogeneous Markov chain with transition probabilities and initial probabilities

Absorption Probabilities for Sums of Random Variables Defined on a Finite Markov Chain

1962 ◽  
Vol 58 (2) ◽  
pp. 286-298 ◽  
Author(s):  
H. D. Miller
Keyword(s):  
Markov Chain ◽  
Asymptotic Expression ◽  
Random Variables ◽  
Partial Sums ◽  
Finite Markov Chain ◽  
Initial State ◽  
Main Result Theorem ◽  
Sums Of Random Variables ◽  
Result Theorem ◽  
Image Position

SummaryThis paper is essentially a continuation of the previous one (5) and the notation established therein will be freely repeated. The sequence {ξr} of random variables is defined on a positively regular finite Markov chain {kr} as in (5) and the partial sums and are considered. Let ζn be the first positive ζr and let πjk(y), the ‘ruin’ function or absorption probability, be defined by The main result (Theorem 1) is an asymptotic expression for πjk(y) for large y in the case when , the expectation of ξ1 being computed under the unique stationary distribution for k0, the initial state of the chain, and unconditional on k1.

On a class of non-homogeneous Markov chains

1982 ◽  
Vol 92 (3) ◽  
pp. 527-534 ◽  
Author(s):  
Harry Cohn
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Transition Probabilities ◽  
Homogeneous Markov Chain ◽  
Image Position

AbstractSuppose that {Xn} is a countable non-homogeneous Markov chain andIf converges for any i, l, m, j with , thenwhenever lim , whereas if converges, thenwhere and . The behaviour of transition probabilities between various groups of states is studied and criteria for recurrence and transience are given.

scholarly journals Sequential Tests for Normal Markov Sequence

1971 ◽  
Vol 12 (4) ◽  
pp. 433-440 ◽  
Author(s):  
R. M. Phatarfod
Keyword(s):  
Markov Chain ◽  
Finite Number ◽  
Transition Probabilities ◽  
Random Variables ◽  
Important Case ◽  
Fundamental Identity ◽  
Markov Sequence ◽  
Sequential Tests ◽  
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This is a sequel to the author's (Phatarfod [9]) paper in which an analogue of Wald's Fundamental Identity (F.I.) for random variables defined on a Markov chain with a finite number of states was derived. From it the sampling properties of sequential tests of simple hypotheses about the parameters occurring in the transition probabilities were obtained. In this paper we consider the case of continuous Markovian variables. We restrict our attention to the practically important case of a Normal Markov sequence X0,X1,X2,… such that the Yr being independent normal variables with mean zero and variance σ2.

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

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