Search results for "controlled Markov chain"

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On the stability of some controlled Markov chains and its applications to stochastic approximation with Markovian dynamic

2015

We develop a practical approach to establish the stability, that is, the recurrence in a given set, of a large class of controlled Markov chains. These processes arise in various areas of applied science and encompass important numerical methods. We show in particular how individual Lyapunov functions and associated drift conditions for the parametrized family of Markov transition probabilities and the parameter update can be combined to form Lyapunov functions for the joint process, leading to the proof of the desired stability property. Of particular interest is the fact that the approach applies even in situations where the two components of the process present a time-scale separation, w…

65C05FOS: Computer and information sciencesStatistics and ProbabilityLyapunov functionStability (learning theory)Markov processContext (language use)Mathematics - Statistics Theorycontrolled Markov chainsStatistics Theory (math.ST)Stochastic approximation01 natural sciencesMethodology (stat.ME)010104 statistics & probabilitysymbols.namesake60J05stochastic approximationFOS: MathematicsComputational statisticsApplied mathematics60J220101 mathematicsStatistics - MethodologyMathematicsSequenceMarkov chain010102 general mathematicsStability Markov chainssymbolsStatistics Probability and Uncertaintyadaptive Markov chain Monte Carlo
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Convergence of Markovian Stochastic Approximation with discontinuous dynamics

2016

This paper is devoted to the convergence analysis of stochastic approximation algorithms of the form $\theta_{n+1} = \theta_n + \gamma_{n+1} H_{\theta_n}({X_{n+1}})$, where ${\left\{ {\theta}_n, n \in {\mathbb{N}} \right\}}$ is an ${\mathbb{R}}^d$-valued sequence, ${\left\{ {\gamma}_n, n \in {\mathbb{N}} \right\}}$ is a deterministic stepsize sequence, and ${\left\{ {X}_n, n \in {\mathbb{N}} \right\}}$ is a controlled Markov chain. We study the convergence under weak assumptions on smoothness-in-$\theta$ of the function $\theta \mapsto H_{\theta}({x})$. It is usually assumed that this function is continuous for any $x$; in this work, we relax this condition. Our results are illustrated by c…

Control and OptimizationStochastic approximationMarkov processMathematics - Statistics Theorydiscontinuous dynamicsStatistics Theory (math.ST)Stochastic approximation01 natural sciencesCombinatorics010104 statistics & probabilitysymbols.namesake[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]Convergence (routing)FOS: Mathematics0101 mathematics62L20state-dependent noiseComputingMilieux_MISCELLANEOUSMathematicsta112SequenceconvergenceApplied Mathematicsta111010102 general mathematicsFunction (mathematics)[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]16. Peace & justice[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulationcontrolled Markov chainMarkovian stochastic approximationsymbolsStochastic approximat
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