Results 251 to 260 of about 899,657 (289)
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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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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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2005
Abstract Useful models of the real world have to satisfy two conflicting requirements: they must be sufficiently complicated to describe complex systems, but they must also be sufficiently simple for us to analyse them. This chapter introduces Markov chains, which have successfully modelled a huge range of scientific and social phenomena,
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Abstract Useful models of the real world have to satisfy two conflicting requirements: they must be sufficiently complicated to describe complex systems, but they must also be sufficiently simple for us to analyse them. This chapter introduces Markov chains, which have successfully modelled a huge range of scientific and social phenomena,
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1997
Markov chains are central to the understanding of random processes. This is not only because they pervade the applications of random processes, but also because one can calculate explicitly many quantities of interest. This textbook, aimed at advanced undergraduate or MSc students with some background in basic probability theory, focuses on Markov ...
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Markov chains are central to the understanding of random processes. This is not only because they pervade the applications of random processes, but also because one can calculate explicitly many quantities of interest. This textbook, aimed at advanced undergraduate or MSc students with some background in basic probability theory, focuses on Markov ...
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Entropy Maximization for Markov and Semi-Markov Processes
Methodology And Computing In Applied Probability, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Strong Markov Properties for Markov Random Fields
Journal of Theoretical Probability, 2000Markov properties for random fields are established. The author presents a multidimensional extension of stopping times by introducing random membranes. A special case of the random membrane is considered to obtain strong Markov property for a point process under Evstigneev's nonanticipating sufficient conditions.
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Markov blanket and Markov boundary of multiple variables
J. Mach. Learn. Res., 2018Summary: Markov blanket (Mb) and Markov boundary (MB) are two key concepts in Bayesian networks (BNs). In this paper, we study the problem of Mb and MB for multiple variables. First, we show that Mb possesses the additivity property under the local intersection assumption, that is, an Mb of multiple targets can be constructed by simply taking the union
Xu-Qing Liu, Xin-sheng Liu
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Markov properties of a Markov process
Zeitschrift f�r Wahrscheinlichkeitstheorie und Verwandte Gebiete, 1981Getoor, R. K., Sharpe, M. J.
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1990
Markovian processes (semi-Markov and Markov) are processes included in a wider class of processes where one has an explicit time dependence (the dynamic aspect) as well as the stochastic character of the states evolution (therefore probabilistic). They are part of dynamic probabilistic systems.
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Markovian processes (semi-Markov and Markov) are processes included in a wider class of processes where one has an explicit time dependence (the dynamic aspect) as well as the stochastic character of the states evolution (therefore probabilistic). They are part of dynamic probabilistic systems.
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Stability and Control of Fuzzy Semi-Markov Jump Systems Under Unknown Semi-Markov Kernel
IEEE Transactions on Fuzzy Systems, 2022Zepeng Ning, Bo Cai, Rui Weng
exaly