Results 301 to 310 of about 144,876 (334)

Constructing the General Markov Chain

1983
In this chapter, I will construct the general Markov chain with continuous time parameter, countable state space, and stationary standard transitions. Let I be the countable state space. Let I n be a sequence of finite subsets of I which swell to I. For each n, suppose X n is Markov with continuous time parameter, has stationary standard transition ...
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On Markov chains generated by Markovian controlled Markov systems— I. ergodic properties

Mathematical Biosciences, 1976
Abstract In this paper a Markovian controlled Markov system M B and⧸or a nonhomogeneous Markov chain { P n } ∞ n=0 =β 0 M B generated by M B are proposed as a mathematical model of time-varying stochastic network flow systems (such as ecological material cycling systems) controlled by Markovian environments, and their ergodic properties ...
Komota, Yasuo, Futatsugi, Kokichi, Kimura, Masayuki   +2 more
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On ergodicity of general Markov chains

Lithuanian Mathematical Journal, 2014
We consider ergodic properties of general Markov chains evolving on a separable measurable space E (with no topological or irreducibility assumptions) and extend some known results in the case of a standard measurable space E to this general framework. We also give simpler proofs of some known results.
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Neural Network Generating Hidden Markov Chain

2005
In this paper we introduce technique how a neural network can generate a Hidden Markov Chain. We use neural network called Temporal Information Categorizing and Learning Map. The network is an enhanced version of standard Categorizing and Learning Module (CALM).
J. Koutník, M. Šnorek
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Hidden Markov chains in generalized linear models

Canadian Journal of Statistics, 1998
AbstractWe show how the concept of hidden Markov model may be accommodated in a setting involving multiple sequences of observations. The resulting class of models allows for both interrelationships between different sequences and serial dependence within sequences. Missing values in the observation sequences may be handled in a straightforward manner.
Turner, T. Rolf, Cameron, Murray A., Thomson, Peter J.   +2 more
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Generalized martingales, generalized Markov chains and generalized harmonic functions

Probability Theory and Related Fields, 1988
From the summary and the introduction: This paper introduces and studies a generalization of the notion of martingale which allows for a generalization of the concept of a Markov chain and a generalization of the concept of harmonic and superharmonic functions. The theory is supported by examples and techniques that suggest the natural character of the
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Markov chain error generator for wireless channel

SPIE Proceedings, 2011
Significant advance in the networks design led to the need of optimizing the future communication protocols with the aim to evaluate the network performance. The most common techniques include simulation, analytical models and analysis of empirical data.
J. Pavlovič, J. Polec, J. Poctavek, K. Kotuliaková   +3 more
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The Berry–Esseen Bound for General Markov Chains

Journal of Mathematical Sciences, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Generating Process Anomalies with Markov Chains: A Pattern-Driven Approach

Generating anomalies for process executions helps to train anomaly detection methods and evaluate their performance. Anomalous behavior tends to be diverse and very infrequent. Generating process anomalies can help compare detection models and select the suited ones.
Jochem Veldman, Xixi Lu, Wouter van der Waal, Marcus Dees, Inge van de Weerd   +4 more
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Applications of generalized inverses to Markov chains

Advances in Applied Probability, 1991
First it is shown that any generalized inverse of the infinitesimal generator of an irreducible Markov chain can be used to compute the exact stationary distribution and all the expected first-passage times of the chain. In the special case of a single-server queue this allows all computations to be done with upper-triangular matrices.Next it is shown ...
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