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Hypotheses testing and posterior concentration rates for semi-Markov processes

Abstract : In this paper, we adopt a nonparametric Bayesian approach and investigate the asymptotic behavior of the posterior distribution in continuous time and general state space semi-Markov processes. In particular, we obtain posterior concentration rates for semi-Markov kernels. For the purposes of this study, we construct robust statistical tests between Hellinger balls around semi-Markov kernels and present some specifications to particular cases, including discrete-time semi-Markov processes and finite state space Markov processes. The objective of this paper is to provide sufficient conditions on priors and semi-Markov kernels that enable us to establish posterior concentration rates.
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Contributor : Ghislaine Gayraud <>
Submitted on : Wednesday, June 12, 2019 - 10:56:46 AM
Last modification on : Tuesday, January 14, 2020 - 3:36:02 AM


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  • HAL Id : hal-02153384, version 1
  • ARXIV : 1906.05566


V Barbu, Ghislaine Gayraud, N. Limnios, I. Votsi. Hypotheses testing and posterior concentration rates for semi-Markov processes. 2019. ⟨hal-02153384⟩



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