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Approximating a diffusion by a finite-state hidden Markov model

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  • Kontoyiannis, I.
  • Meyn, S.P.

Abstract

For a wide class of continuous-time Markov processes evolving on an open, connected subset of Rd, the following are shown to be equivalent: (i)The process satisfies (a slightly weaker version of) the classical Donsker–Varadhan conditions;(ii)The transition semigroup of the process can be approximated by a finite-state hidden Markov model, in a strong sense in terms of an associated operator norm;(iii)The resolvent kernel of the process is ‘v-separable’, that is, it can be approximated arbitrarily well in operator norm by finite-rank kernels. Under any (hence all) of the above conditions, the Markov process is shown to have a purely discrete spectrum on a naturally associated weighted L∞ space.

Suggested Citation

  • Kontoyiannis, I. & Meyn, S.P., 2017. "Approximating a diffusion by a finite-state hidden Markov model," Stochastic Processes and their Applications, Elsevier, vol. 127(8), pages 2482-2507.
    • Handle: RePEc:eee:spapps:v:127:y:2017:i:8:p:2482-2507
    DOI: 10.1016/j.spa.2016.11.004
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    References listed on IDEAS

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    1. Ana Bušić & Ingrid Vliegen & Alan Scheller-Wolf, 2012. "Comparing Markov Chains: Aggregation and Precedence Relations Applied to Sets of States, with Applications to Assemble-to-Order Systems," Mathematics of Operations Research, INFORMS, vol. 37(2), pages 259-287, May.
    2. Balaji, S. & Meyn, S. P., 2000. "Multiplicative ergodicity and large deviations for an irreducible Markov chain," Stochastic Processes and their Applications, Elsevier, vol. 90(1), pages 123-144, November.
    3. Feng, Jin, 1999. "Martingale problems for large deviations of Markov processes," Stochastic Processes and their Applications, Elsevier, vol. 81(2), pages 165-216, June.
    4. Wu, Liming, 2001. "Large and moderate deviations and exponential convergence for stochastic damping Hamiltonian systems," Stochastic Processes and their Applications, Elsevier, vol. 91(2), pages 205-238, February.
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