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• W2920853828 abstract "We consider posterior consistency for a Markov model with a novel class of nonparametric prior. In this model, the transition density is parameterized via a mixing distribution function. Therefore, the Wasserstein distance between mixing measures can be used to construct neighborhoods of a transition density. The Wasserstein distance is sufficiently strong, for example, if the mixing distributions are compactly supported, it dominates the sup-$L_{1}$ metric. We provide sufficient conditions for posterior consistency with respect to the Wasserstein metric provided that the true transition density is also parametrized via a mixing distribution. In general, when it is not be parameterized by a mixing distribution, we show the posterior distribution is consistent with respect to the average $L_{1}$ metric. Also, we provide a prior whose support is sufficiently large to contain most smooth transition densities." @default.
• W2920853828 created "2019-03-22" @default.
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• W2920853828 date "2019-05-01" @default.
• W2920853828 modified "2023-10-18" @default.
• W2920853828 title "Bayesian consistency for a nonparametric stationary Markov model" @default.
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• W2920853828 doi "https://doi.org/10.3150/17-bej1007" @default.
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