Electronic Journal of Statistics

Bayesian inverse problems with non-conjugate priors

Kolyan Ray

Full-text: Open access

Abstract

We investigate the frequentist posterior contraction rate of nonparametric Bayesian procedures in linear inverse problems in both the mildly and severely ill-posed cases. A theorem is proved in a general Hilbert space setting under approximation-theoretic assumptions on the prior. The result is applied to non-conjugate priors, notably sieve and wavelet series priors, as well as in the conjugate setting. In the mildly ill-posed setting minimax optimal rates are obtained, with sieve priors being rate adaptive over Sobolev classes. In the severely ill-posed setting, oversmoothing the prior yields minimax rates. Previously established results in the conjugate setting are obtained using this method. Examples of applications include deconvolution, recovering the initial condition in the heat equation and the Radon transform.

Article information

Source
Electron. J. Statist., Volume 7 (2013), 2516-2549.

Dates
First available in Project Euclid: 8 October 2013

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1381239960

Digital Object Identifier
doi:10.1214/13-EJS851

Mathematical Reviews number (MathSciNet)
MR3117105

Zentralblatt MATH identifier
1294.62107

Subjects
Primary: 62G20: Asymptotic properties
Secondary: 62G05: Estimation 62G08: Nonparametric regression

Keywords
Rate of contraction posterior distribution nonparametric hypothesis testing

Citation

Ray, Kolyan. Bayesian inverse problems with non-conjugate priors. Electron. J. Statist. 7 (2013), 2516--2549. doi:10.1214/13-EJS851. https://projecteuclid.org/euclid.ejs/1381239960


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