Results 1  10
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83
Exact Sampling with Coupled Markov Chains and Applications to Statistical Mechanics
, 1996
"... For many applications it is useful to sample from a finite set of objects in accordance with some particular distribution. One approach is to run an ergodic (i.e., irreducible aperiodic) Markov chain whose stationary distribution is the desired distribution on this set; after the Markov chain has ..."
Abstract

Cited by 543 (13 self)
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For many applications it is useful to sample from a finite set of objects in accordance with some particular distribution. One approach is to run an ergodic (i.e., irreducible aperiodic) Markov chain whose stationary distribution is the desired distribution on this set; after the Markov chain
An analysis of temporaldifference learning with function approximation
 IEEE Transactions on Automatic Control
, 1997
"... We discuss the temporaldifference learning algorithm, as applied to approximating the costtogo function of an infinitehorizon discounted Markov chain. The algorithm weanalyze updates parameters of a linear function approximator online, duringasingle endless trajectory of an irreducible aperiodi ..."
Abstract

Cited by 313 (8 self)
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We discuss the temporaldifference learning algorithm, as applied to approximating the costtogo function of an infinitehorizon discounted Markov chain. The algorithm weanalyze updates parameters of a linear function approximator online, duringasingle endless trajectory of an irreducible
Characterizing the Aperiodicity of Irreducible Markov Chains by Using P Systems
"... Summary. It is well known that any irreducible and aperiodic Markov chain has exactly one stationary distribution, and for any arbitrary initial distribution, the sequence of distributions at time n converges to the stationary distribution, that is, the Markov chain is approaching equilibrium as n → ..."
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Cited by 2 (2 self)
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Summary. It is well known that any irreducible and aperiodic Markov chain has exactly one stationary distribution, and for any arbitrary initial distribution, the sequence of distributions at time n converges to the stationary distribution, that is, the Markov chain is approaching equilibrium as n
Irreducibility of Certain Pseudovarieties
"... We prove that the pseudovarieties of all finite semigroups, and of all aperiodic finite semigroups are irreducible for join, for semidirect product and for Mal'cev product. In particular, these pseudovarieties do not admit maximal proper subpseudovarieties. More generally, analogous results ..."
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Cited by 11 (1 self)
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We prove that the pseudovarieties of all finite semigroups, and of all aperiodic finite semigroups are irreducible for join, for semidirect product and for Mal'cev product. In particular, these pseudovarieties do not admit maximal proper subpseudovarieties. More generally, analogous results
Practical drift conditions for subgeometric rates of convergence,”
 The Annals of Applied Probability,
, 2004
"... Abstract We present a new drift condition which implies rates of convergence to the stationary distribution of the iterates of a ψirreducible aperiodic and positive recurrent transition kernel. This condition, extending a condition introduced by Jarner and Roberts Abbreviated title Subgeometric r ..."
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Cited by 46 (11 self)
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Abstract We present a new drift condition which implies rates of convergence to the stationary distribution of the iterates of a ψirreducible aperiodic and positive recurrent transition kernel. This condition, extending a condition introduced by Jarner and Roberts Abbreviated title Subgeometric
CONTINUUM SCHRÖDINGER OPERATORS ASSOCIATED WITH APERIODIC SUBSHIFTS
"... Abstract. We study Schrödinger operators on the real line whose potentials are generated by an underlying ergodic subshift over a finite alphabet and a rule that replaces symbols by compactly supported potential pieces. We first develop the standard theory that shows that the spectrum and the spect ..."
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Cited by 2 (1 self)
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Lebesgue measure if the subhift satisfies the Boshernitzan condition and the potentials are aperiodic and irreducible. We then study the case of the Fibonacci subshift in detail and prove results for the local Hausdorff dimension of the spectrum at a given energy in terms of the value of the associated
Multiplicative Ergodicity for an Irreducible Markov Chain
, 1999
"... The paper examines multiplicative ergodic theorems and the related multiplicative Poisson equation for an irreducible Markov chain on a countable state space. The partial products are considered for a positivevalued function on the state space. Provided that the sublevel sets of the function are sui ..."
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Cited by 2 (0 self)
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Theory, Harmonic functions, Large Deviations 1991 AMS Subject Classification: 60J10, 60J27, 60F10, 58F11 1 Introduction and Main Results Consider a recurrent, aperiodic, and irreducible Markov chain \Phi = f\Phi 0 ; \Phi 1 ; : : :g with transition probability P on a countably infinite state space X. We
PROBABILITY LETTERS Verifying irreducibility and continuity of a nonlinear time series
, 1997
"... When considering the stability of a nonlinear time series, verifying aperiodicity, irreducibility and smoothness of the transitions for the corresponding Markov chain is often the first step. Here, we provide reasonably general conditions applicable to nonlinear autoregressive time series, including ..."
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When considering the stability of a nonlinear time series, verifying aperiodicity, irreducibility and smoothness of the transitions for the corresponding Markov chain is often the first step. Here, we provide reasonably general conditions applicable to nonlinear autoregressive time series
Average cost temporaldifference learning
, 1999
"... We propose a variant of temporaldifference learning that approximates average and differential costs of an irreducible aperiodic Markov chain. Approximations are comprised of linear combinations of fixed basis functions whose weights are incrementally updated during a single endless trajectory of t ..."
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Cited by 27 (4 self)
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We propose a variant of temporaldifference learning that approximates average and differential costs of an irreducible aperiodic Markov chain. Approximations are comprised of linear combinations of fixed basis functions whose weights are incrementally updated during a single endless trajectory
Results 1  10
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83