Results 281 to 290 of about 29,628 (317)

∞-Generalized Fibonacci Sequences and Markov Chains

The Fibonacci Quarterly, 2000
The authors study the convergence of generalized Fibonacci sequences defined by the recursion \[ V_{n+1}= a_0V_n+ a_1V_{n-1}+\cdots+ a_{s-1}V_{n-s+1}, \] with non-negative coefficients \(a_0,\dots, a_{s-1}\), by first reducing to the case where \(\sum_i a_i=1\) and then using ideas from the theory of Markov chains.
Mouline, Mehdi, Rachidi, Mustapha
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A Markov Chain Sequence Generator for Power Macromodeling

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2002
In this paper, we present a novel sequence generator based on a Markov chain model. Specifically, we formulate the problem of generating a sequence of vectors with given average input probability p, average transition density d, and spatial correlation s as a transition matrix computation problem, in which the matrix elements are subject to constraints
Xun Liu, Marios C. Papaefthymiou
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Generating Markov-Chain Transitions Quickly: II

ORSA Journal on Computing, 1991
A predecessor to this paper gives a way to generate transitions in continuous-time Markov chains. It is fast when a “similarity” condition holds. Exploiting a balanced binary search tree, we reduce the computational complexity of that method. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 ...
Bennett L. Fox, Andrew R. Young
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Generators of Markov Chains

2020
Elementary treatments of Markov chains, especially those devoted to discrete-time and finite state-space theory, leave the impression that everything is smooth and easy to understand. This exposition of the works of Kolmogorov, Feller, Chung, Kato, and other mathematical luminaries, which focuses on time-continuous chains but is not so far from being ...
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Generalized Jarzynski’s equality in inhomogeneous Markov chains

Journal of Mathematical Physics, 2007
A rigorous mathematical theory of generalized Jarzynski’s equality in inhomogeneous Markov chains is given. Then, we explain its physical meaning and applications through several previous work including the original works of Jarzynski [Phys. Rev. Lett. 78, 2690 (1997); Phys. Rev. E 56, 5018 (1997); J. Stat. Phys. 96, 415 (1999); J. Stat. Phys.
Ge, Hao, Qian, Min
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Generating Markov-Chain Transitions Quickly: I

ORSA Journal on Computing, 1990
We give a way to generate transitions directly from a compact representation of the generator of a continuous-time Markov chain corresponding to a class that includes many queueing networks and reliability problems. Under specified conditions, this is (provably) faster than generating these transitions via a future-event schedule.
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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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Martingale generating functions for Markov chains

Journal of Statistical Planning and Inference, 2002
Let \(X=(X(t))_{t\in T}\) be a Markov chain with either discrete \([T=\{0,1,2, \dots,\}]\) or continuous parameter \((T=[0,\infty))\). The authors are concerned with martingales of the form \(\psi(X)\) or \(\varphi(X,\tau)\), where \(\tau\) is a stopping time for \(X\).
Williams, E. J., Watson, R. K.
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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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Sets generated by stochastic automata of Markov’s chain type

Fundamenta Informaticae, 1980
In the earlier paper of the author [2] it has been introduced the concept of the generability for stochastic automaton. Here we give new necessary and sufficient conditions for the generability of the set of infinite sequences of automaton states.
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