In the need for low assumption inferential methods in infinite-dimensional settings, Bayesian adaptive estimation via a prior distribution that does not depend on the regularity of the function to be estimated nor on the sample size is valuable. We elucidate relationships among the main approaches followed to design priors for minimax-optimal rate-adaptive estimation meanwhile shedding light on the underlying ideas.

Bayesian adaptation

Scricciolo, Catia
2015-01-01

Abstract

In the need for low assumption inferential methods in infinite-dimensional settings, Bayesian adaptive estimation via a prior distribution that does not depend on the regularity of the function to be estimated nor on the sample size is valuable. We elucidate relationships among the main approaches followed to design priors for minimax-optimal rate-adaptive estimation meanwhile shedding light on the underlying ideas.
Adaptive estimation
Empirical Bayes
Gaussian process priors
Kernel mixture priors
Nonparametric credibility regions
Posterior distributions
Rates of convergence
Sieve priors
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/927896
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