Results 261 to 270 of about 451,378 (309)

Markov Duels

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 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, 1963
Let ${\bf \mathfrak{K}}$ 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 $\mathfrak{K}$.
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Markov Functionals of an Ergodic Markov Process

Theory of Probability & Its Applications, 1995
We say that a process $(\xi (t))_{t \geqq 0} $ is a Markov functional of a basic homogeneous Markov process $(X(t))_{t \geqq 0} $ if the pair $(X(t),\xi (t))_{t \geqq 0} $ is a Markov process. In the paper a sequence of Markov functionals $(\xi_n (t))_{t \geqq 0} $ of the basic process $(X(t))_{t \geqq 0} $, which is degenerate in the limit, is ...
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Markov and Semi-Markov Processes

2018
This 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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Markov models—Markov chains

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, Martin Krzywinski, Naomi Altman   +2 more
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Markov Processes and Markov Families

2012
In 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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Markov property of Markov chains and its test

2010 International Conference on Machine Learning and Cybernetics, 2010
Markov 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, 1987
Abstract 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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Markov Sampling

Australian & New Zealand Journal of Statistics, 2000
A discrete parameter stochastic process is observed at epochs of visits to a specified state in an independent two‐state Markov chain. It is established that the family of finite dimensional distributions of the process derived in this way, referred to as Markov sampling, uniquely determines the stochastic structure of the original process.
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