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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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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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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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An EM Algorithm for Markov Modulated Markov Processes
IEEE Transactions on Signal Processing, 2009An expectation-maximization (EM) algorithm for estimating the parameter of a Markov modulated Markov process in the maximum likelihood sense is developed. This is a doubly stochastic random process with an underlying continuous-time finite-state homogeneous Markov chain.
Yariv Ephraim, W J J Roberts
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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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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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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 ...
openaire +1 more source