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Outperforming the Gibbs sampler empirical estimator for nearest-neighbor random fields. (English) Zbl 0871.62083

Summary: Given a Markov chain sampling scheme, does the standard empirical estimator make best use of the data? We show that this is not so and construct better estimators. We restrict attention to nearest-neighbor random fields and to Gibbs samplers with deterministic sweep, but our approach applies to any sampler that uses reversible variable-at-a-time updating with deterministic sweep. The structure of the transition distribution of the sampler is exploited to construct further empirical estimators that are combined with the standard empirical estimator to reduce asymptotic variance. The extra computational cost is negligible. When the random field is spatially homogeneous, symmetrizations of our estimator lead to further variance reduction. The performance of the estimators is evaluated in a simulation study of the Ising model.

MSC:

62M40 Random fields; image analysis
60J05 Discrete-time Markov processes on general state spaces
65C99 Probabilistic methods, stochastic differential equations
62G20 Asymptotic properties of nonparametric inference
62M05 Markov processes: estimation; hidden Markov models
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