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Operations Research, 1974
Markov duels are a general class of stochastic duels in which each weapon has Markov-dependent fire, that is, the outcomes of shots by each weapon form a Markov process. This paper develops duel models for the situation in which the outcomes form a finite stationary Markov chain and both weapons have an unlimited supply of ammunition, fire at constant
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Markov duels are a general class of stochastic duels in which each weapon has Markov-dependent fire, that is, the outcomes of shots by each weapon form a Markov process. This paper develops duel models for the situation in which the outcomes form a finite stationary Markov chain and both weapons have an unlimited supply of ammunition, fire at constant
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Markov or not Markov - this should be a question [PDF]
, 2001 Although it is well known that Markov process theory, frequently applied in the literature on income convergence, imposes some very restrictive assumptions upon the data generating process, these assumptions have generally been taken for granted so far. The present paper proposes, resp.
Bode, Eckhardt, Bickenbach, Frank
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Markov Measures and Markov Extensions
Theory of Probability & Its Applications, 1963Let ${\bf \mathfrak{K}}$ be a complex with the set of vertices M and A, B and R three subsets of M. R is said to be separating A and B in ${\bf \mathfrak{K}}$ (notation: $(A\mathop |\limits_R B)\mathfrak{K}$ if any $a \in A$ and $b \in B$ are not connected in $ \mathfrak{K} - \cup _{r \in R} O_\mathfrak{K} r$ is the star of r in $\mathfrak{K}$.Let $S_a
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Markov Functionals of an Ergodic Markov Process
Theory of Probability & Its Applications, 1995Let \((X(t), t \geq 0)\) be a homogeneous Markov process. The author calls a random process \(\xi(t)\) the Markovian functional if the pair \((X(t), \xi(t))\) is a homogeneous Markov process. Let \(\xi_n (t)\) be a sequence of Markovian functionals with finite state space \(I = \{1,2,\dots, d\}\) for all \(n \geq 1\) and such that the following ...
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Markov and Semi-Markov Processes
2018This chapter is devoted to jump Markov processes and finite semi-Markov processes. In both cases, the index is considered as the calender time, continuously counted over the positive real line. Markov processes are continuous-time processes that share the Markov property with the discrete-time Markov chains.
Valérie Girardin, Nikolaos Limnios
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Nature Methods, 2019
You can look back there to explain things, but the explanation disappears. You’ll never find it there. Things are not explained by the past. They’re explained by what happens now.
Jasleen K. Grewal +2 more
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You can look back there to explain things, but the explanation disappears. You’ll never find it there. Things are not explained by the past. They’re explained by what happens now.
Jasleen K. Grewal +2 more
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Markov property of Markov chains and its test
2010 International Conference on Machine Learning and Cybernetics, 2010Markov chains, with Markov property as its essence, are widely used in the fields such as information theory, automatic control, communication techniques, genetics, computer sciences, economic administration, education administration, and market forecasts.
Yu-Fen Zhang, Qun-Feng Zhang, Rui-Hua Yu
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On the relationship of the Markov mesh to the NSHP Markov chain
Pattern Recognition Letters, 1987Abstract Two definitions of causal 2-D Markov chains are widely used in image processing. They are the Markov mesh and the nonsymmetric half-plane (NSHP) Markov chain. These two models differ in their choice of ‘past’ and also local state. In this letter we point out their relationship.
Fure-Ching Jeng, John W. Woods
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1992
Abstract In Section 7.3, we briefly introduced models for Markov chains in the simple case where there were only two possible responses: an event occurs or not. However, such models have much wider application. In continuous time models, where each subject is in one of several possible states at any given point, they are often called ...
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Abstract In Section 7.3, we briefly introduced models for Markov chains in the simple case where there were only two possible responses: an event occurs or not. However, such models have much wider application. In continuous time models, where each subject is in one of several possible states at any given point, they are often called ...
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Markov Processes and Markov Families
2012In this section we shall use intuitive arguments in order to find the distribution of M T . Rigorous arguments will be provided later in this chapter, after we introduce the notion of a strong Markov family. Thus, the problem at hand may serve as a simple example motivating the study of the strong Markov property.
Leonid Koralov, Yakov G. Sinai
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