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83
Practical drift conditions for subgeometric rates of convergence
"... 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 (2002) for polynomial convergence rates, tu ..."
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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 (2002) for polynomial convergence rates
Moralizing Perfect Sampling
, 2000
"... Let be an irreducible, aperiodic, Harris recurrent Markov chain with invariant probability measure . We show that if a minorization condition can be established for , then can be represented as an innite mixture from which it may be possible to sample. Making exact draws from this mixture (and hen ..."
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Cited by 3 (0 self)
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Let be an irreducible, aperiodic, Harris recurrent Markov chain with invariant probability measure . We show that if a minorization condition can be established for , then can be represented as an innite mixture from which it may be possible to sample. Making exact draws from this mixture (and
A Proofs and Mathematical Arguments for Section 4: LongRun Behavior and the Invariant Industry Distribution
, 2008
"... Proof of Lemma A.3. Let Assumptions 3.2 and 3.3 hold. Assume that firms follow a common oblivious strategy µ ∈ ˜ M, the expected entry rate is λ ∈ ˜ Λ, and the expected time that each firm spends in the industry is finite. Let {Zx: x ∈ N} be a sequence of independent Poisson random variables with m ..."
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with means {˜sµ,λ(x) : x ∈ N}, and let Z be a Poisson random variable with mean ∑ x∈N ˜sµ,λ(x). Then, (a) {st: t ≥ 0} is an irreducible, aperiodic, and positive recurrent Markov process; (b) the invariant distribution of st is a product form of Poisson random variables; (c) for all x, st(x) ⇒ Zx; (d) nt ⇒ Z
On the Limiting Shape of Markovian Random
, 2008
"... Let (Xn)n≥0 be an irreducible, aperiodic, homogeneous Markov chain, with state space an ordered finite alphabet of size m. Using combinatorial constructions and weak invariance principles, we obtain the limiting shape of the associated Young tableau as a multidimensional Brownian functional. Since t ..."
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Let (Xn)n≥0 be an irreducible, aperiodic, homogeneous Markov chain, with state space an ordered finite alphabet of size m. Using combinatorial constructions and weak invariance principles, we obtain the limiting shape of the associated Young tableau as a multidimensional Brownian functional. Since
x y Figure 5.1: Coupling
"... Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only with the permission of the Instructor. 5.1 Coupling for bounding the mixing time Consider as usual an ergodic (i.e., irreducible, aperiodic) Markov ..."
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Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only with the permission of the Instructor. 5.1 Coupling for bounding the mixing time Consider as usual an ergodic (i.e., irreducible, aperiodic) Markov
Complex Objects in the Polytopes of the Linear StateSpace Process
, 2014
"... A simple object (one point in mdimensional space) is the resultant of the evolving matrix polynomial of walks in the irreducible aperiodic network structure of the first order DeGroot (weighted averaging) statespace process. This paper draws on a second order generalization the DeGroot model that ..."
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A simple object (one point in mdimensional space) is the resultant of the evolving matrix polynomial of walks in the irreducible aperiodic network structure of the first order DeGroot (weighted averaging) statespace process. This paper draws on a second order generalization the DeGroot model
Exponential and Uniform Ergodicity of Markov Processes
 Ann. Probab
, 1995
"... Geometric convergence of Markov chains in discrete time on a general state has been studied in detail in [15]. Here we develop a similar theory for 'irreducible continuous time processes, and consider the following types of criteria for geometric convergence: (a) the existence of exponentially ..."
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Cited by 77 (12 self)
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Geometric convergence of Markov chains in discrete time on a general state has been studied in detail in [15]. Here we develop a similar theory for 'irreducible continuous time processes, and consider the following types of criteria for geometric convergence: (a) the existence
Stability of the Tail Markov Chain and the Evaluation of Improper Priors for an Exponential Rate Parameter
"... Let Z be a continuous random variable with a lower semicontinuous density f that is positive on (0; 1) and 0 elsewhere. Put G(x) = f(z) dz. We study the tail Markov chain generated by Z de ned as the Markov chain = (n ) n=0 with state space [0; 1) and Markov transition density k(yjx) = f(y + ..."
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Cited by 3 (3 self)
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(y + x)=G(x). This chain is irreducible, aperiodic and reversible with respect to G. It follows that is positive recurrent if and only if Z has a nite expectation. We prove (under regularity conditions) that if EZ = 1, then is null recurrent if and only if dz = 1. Furthermore, we describe
An Analysis of TemporalDifference Learning with Function Approximation
"... Abstract — We discuss the temporaldifference learning algorithm, as applied to approximating the costtogo function of an infinitehorizon discounted Markov chain. The algorithm we analyze updates parameters of a linear function approximator online during a single endless trajectory of an irreduci ..."
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of an irreducible aperiodic Markov chain with a finite or infinite state space. We present a proof of convergence (with probability one), a characterization of the limit of convergence, and a bound on the resulting approximation error. Furthermore, our analysis is based on a new line of reasoning that provides new
On Markov Chains with Sluggish Transients
, 1997
"... In this note it is shown how to construct a Markov chain whose subdominant eigenvalue does not predict the decay of its transient. This report is available by anonymous ftp from thales.cs.umd.edu in the directory pub/reports. y Department of Computer Science and Institute for Advanced Computer ..."
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not predict the decay of its transient. 1. Introduction Let P be the transition matrix of an finite, irreducible, aperiodic Markov chain, and let e be the vector whose components all are one. Since the row sums of P are one, Pe = e; i.e., e is a right eigenvector of P corresponding to the eigenvalue one
Results 11  20
of
83