May  2012, 6(2): 183-200. doi: 10.3934/ipi.2012.6.183

Besov priors for Bayesian inverse problems

1. 

Department of Mathematics, University of Sussex, Brighton BN1 5DJ, United Kingdom

2. 

School of Mathematics, University of Edinburgh, Edinburgh EH9 3JZ, United Kingdom

3. 

Mathematics Institute, University of Warwick, Coventry CV4 7AL, United Kingdom

Received  May 2011 Revised  March 2012 Published  May 2012

We consider the inverse problem of estimating a function $u$ from noisy, possibly nonlinear, observations. We adopt a Bayesian approach to the problem. This approach has a long history for inversion, dating back to 1970, and has, over the last decade, gained importance as a practical tool. However most of the existing theory has been developed for Gaussian prior measures. Recently Lassas, Saksman and Siltanen (Inv. Prob. Imag. 2009) showed how to construct Besov prior measures, based on wavelet expansions with random coefficients, and used these prior measures to study linear inverse problems. In this paper we build on this development of Besov priors to include the case of nonlinear measurements. In doing so a key technical tool, established here, is a Fernique-like theorem for Besov measures. This theorem enables us to identify appropriate conditions on the forward solution operator which, when matched to properties of the prior Besov measure, imply the well-definedness and well-posedness of the posterior measure. We then consider the application of these results to the inverse problem of finding the diffusion coefficient of an elliptic partial differential equation, given noisy measurements of its solution.
Citation: Masoumeh Dashti, Stephen Harris, Andrew Stuart. Besov priors for Bayesian inverse problems. Inverse Problems & Imaging, 2012, 6 (2) : 183-200. doi: 10.3934/ipi.2012.6.183
References:
[1]

F. Abramovich and B. W. Silverman, Wavelet decomposition approaches to statistical inverse problems,, Biometrika, 85 (1998), 115. doi: 10.1093/biomet/85.1.115. Google Scholar

[2]

F. Abramovich, T. Sapatinas and B. W. Silverman, Wavelet thresholding via a Bayesian approach,, J. R. Stat. Soc. Ser. B Stat. Methodol., 60 (1998), 725. doi: 10.1111/1467-9868.00151. Google Scholar

[3]

A. Beskos, G. O. Roberts, A. M. Stuart and J. Voss, MCMC methods for diffusion bridges,, Stochastic Dynamics, 8 (2008), 319. doi: 10.1142/S0219493708002378. Google Scholar

[4]

A. Beskos, G. O. Roberts and A. M. Stuart, Optimal scalings for local Metropolis-Hastings chains on nonproduct targets in high dimensions,, Ann. Appl. Prob., 19 (2009), 863. doi: 10.1214/08-AAP563. Google Scholar

[5]

R. E. Caflisch, Monte Carlo and quasi-Monte Carlo methods,, in, 7 (1998), 1. Google Scholar

[6]

A. Chambolle, R. A. DeVore, N. Lee and B. J. Lucier, Nonlinear wavelet image processing: Variational problems, compression, and noise removal through wavelet shrinkage,, IEEE Trans. Image Process., 7 (1998), 319. doi: 10.1109/83.661182. Google Scholar

[7]

S. L. Cotter, M. Dashti, J. C. Robinson and A. M. Stuart, Bayesian inverse problems for functions and applications to fluid mechanics,, Inverse Problems, 25 (2009). Google Scholar

[8]

S. L. Cotter, M. Dashti and A. M. Stuart, Approximation of Bayesian inverse problems for PDEs,, SIAM J. Num. Anal., 48 (2010), 322. doi: 10.1137/090770734. Google Scholar

[9]

S. L.Cotter, M. Dashti and A. M.Stuart, Variational data assimilation using targetted random walks,, Int. J. Num. Meth. Fluids, (2011). Google Scholar

[10]

G. Da Prato and J. Zabczyk, "Stochastic Equations in Infinite Dimensions,", Encyclopedia of Mathematics and its Applications, 44 (1992). Google Scholar

[11]

M. Dashti and A. Stuart, Uncertainty quantificationand weak approximation of an elliptic inverse problem,, SIAM J. Num.Anal., (2011). Google Scholar

[12]

I. Daubechies, "Ten Lectures on Wavelets," CBMS-NSF Regional Conference Series in Applied Mathematics, 61,, Society for Industrial and Applied Mathematics (SIAM), (1992). Google Scholar

[13]

I. Daubechies, M. Defrise and C. De Mol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,, Comm. Pure Appl. Math., 57 (2004), 1413. doi: 10.1002/cpa.20042. Google Scholar

[14]

D. L. Donoho and I. M. Johnstone, Ideal spatial adaptation by wavelet shrinkage,, Biometrika, 81 (1994), 425. doi: 10.1093/biomet/81.3.425. Google Scholar

[15]

H. W. Engl, M. Hanke and A. Neubauer, "Regularization of Inverse Problems,", Mathematics and its Applications, 375 (1996). doi: 10.1007/978-94-009-1740-8. Google Scholar

[16]

J. N. Franklin, Well-posed stochastic extensions of ill-posed linear problems,, J. Math. Anal. Appl., 31 (1970), 682. doi: 10.1016/0022-247X(70)90017-X. Google Scholar

[17]

T. Helin and M. Lassas, Hierarchical models in statistical inverse problems and the Mumuford-Shah functional,, Inverse Problems, 27 (2011). doi: 10.1088/0266-5611/27/1/015008. Google Scholar

[18]

M. Hairer, A. M. Stuart and J. Voss, Analysis of SPDEs arising in path sampling. II. The nonlinear case,, Annals of Applied Probability, 17 (2007), 1657. doi: 10.1214/07-AAP441. Google Scholar

[19]

J. Kaipio and E. Somersalo, "Statistical and Computational Inverse Problems," Applied Mathematical Sciences, 160,, Springer-Verlag, (2005). Google Scholar

[20]

J.-P. Kahane, "Some Random Series of Functions,", Cambridge Studies in Advanced Mathematics, 5 (1985). Google Scholar

[21]

S. Lasanen, Discretizations of generalized random variables with applications to inverse problems,, Dissertation, (2002). Google Scholar

[22]

S. Lasanen, Measurements and infinite-dimensional statistical inverse theory,, PAMM, 7 (2007), 1080101. doi: 10.1002/pamm.200700068. Google Scholar

[23]

M. Lassas, E. Saksman and S. Siltanen, Discretization invariant Bayesian inversion and Besov space priors,, Inverse Problems and Imaging, 3 (2009), 87. doi: 10.3934/ipi.2009.3.87. Google Scholar

[24]

M. S. Lehtinen, L. Päivärinta and E. Somersalo, Linear inverse problems for generalized random variables,, Inverse Problems, 5 (1989), 599. doi: 10.1088/0266-5611/5/4/011. Google Scholar

[25]

A. Mandelbaum, Linear estimators and measurable linear transformations on a Hilbert space,, Z. Wahrsch. Verw. Gebiete, 65 (1984), 385. Google Scholar

[26]

Y. Meyer, "Wavelets and Operators," Translated from the 1990 French original by D. H. Salinger,, Cambridge Studies in Advanced Mathematics, 37 (1992). Google Scholar

[27]

P. Piiroinen, "Statistical Measruements, Experiments and Applications,", Dissertation, (2005). Google Scholar

[28]

Ch. Schwab and A. M. Stuart, Sparse deterministic approximation of Bayesian inverse problems,, submitted, (2011). Google Scholar

[29]

P. D. Spanos and R. Ghanem, Stochastic finite element expansion for random media,, J. Eng. Mech., 115 (1989), 1035. doi: 10.1061/(ASCE)0733-9399(1989)115:5(1035). Google Scholar

[30]

A. M. Stuart, Inverse problems: A Bayesian approach,, Acta Numerica, (2010). doi: 10.1017/S0962492910000061. Google Scholar

[31]

H. Triebel, "Theory of Function Spaces,", Monographs in Mathematics, 78 (1983). Google Scholar

[32]

P. Wojtaszczyk, "A Mathematical Introduction to Wavelets," London Mathematical Society Student Texts, 37,, Cambridge University Press, (1997). Google Scholar

show all references

References:
[1]

F. Abramovich and B. W. Silverman, Wavelet decomposition approaches to statistical inverse problems,, Biometrika, 85 (1998), 115. doi: 10.1093/biomet/85.1.115. Google Scholar

[2]

F. Abramovich, T. Sapatinas and B. W. Silverman, Wavelet thresholding via a Bayesian approach,, J. R. Stat. Soc. Ser. B Stat. Methodol., 60 (1998), 725. doi: 10.1111/1467-9868.00151. Google Scholar

[3]

A. Beskos, G. O. Roberts, A. M. Stuart and J. Voss, MCMC methods for diffusion bridges,, Stochastic Dynamics, 8 (2008), 319. doi: 10.1142/S0219493708002378. Google Scholar

[4]

A. Beskos, G. O. Roberts and A. M. Stuart, Optimal scalings for local Metropolis-Hastings chains on nonproduct targets in high dimensions,, Ann. Appl. Prob., 19 (2009), 863. doi: 10.1214/08-AAP563. Google Scholar

[5]

R. E. Caflisch, Monte Carlo and quasi-Monte Carlo methods,, in, 7 (1998), 1. Google Scholar

[6]

A. Chambolle, R. A. DeVore, N. Lee and B. J. Lucier, Nonlinear wavelet image processing: Variational problems, compression, and noise removal through wavelet shrinkage,, IEEE Trans. Image Process., 7 (1998), 319. doi: 10.1109/83.661182. Google Scholar

[7]

S. L. Cotter, M. Dashti, J. C. Robinson and A. M. Stuart, Bayesian inverse problems for functions and applications to fluid mechanics,, Inverse Problems, 25 (2009). Google Scholar

[8]

S. L. Cotter, M. Dashti and A. M. Stuart, Approximation of Bayesian inverse problems for PDEs,, SIAM J. Num. Anal., 48 (2010), 322. doi: 10.1137/090770734. Google Scholar

[9]

S. L.Cotter, M. Dashti and A. M.Stuart, Variational data assimilation using targetted random walks,, Int. J. Num. Meth. Fluids, (2011). Google Scholar

[10]

G. Da Prato and J. Zabczyk, "Stochastic Equations in Infinite Dimensions,", Encyclopedia of Mathematics and its Applications, 44 (1992). Google Scholar

[11]

M. Dashti and A. Stuart, Uncertainty quantificationand weak approximation of an elliptic inverse problem,, SIAM J. Num.Anal., (2011). Google Scholar

[12]

I. Daubechies, "Ten Lectures on Wavelets," CBMS-NSF Regional Conference Series in Applied Mathematics, 61,, Society for Industrial and Applied Mathematics (SIAM), (1992). Google Scholar

[13]

I. Daubechies, M. Defrise and C. De Mol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,, Comm. Pure Appl. Math., 57 (2004), 1413. doi: 10.1002/cpa.20042. Google Scholar

[14]

D. L. Donoho and I. M. Johnstone, Ideal spatial adaptation by wavelet shrinkage,, Biometrika, 81 (1994), 425. doi: 10.1093/biomet/81.3.425. Google Scholar

[15]

H. W. Engl, M. Hanke and A. Neubauer, "Regularization of Inverse Problems,", Mathematics and its Applications, 375 (1996). doi: 10.1007/978-94-009-1740-8. Google Scholar

[16]

J. N. Franklin, Well-posed stochastic extensions of ill-posed linear problems,, J. Math. Anal. Appl., 31 (1970), 682. doi: 10.1016/0022-247X(70)90017-X. Google Scholar

[17]

T. Helin and M. Lassas, Hierarchical models in statistical inverse problems and the Mumuford-Shah functional,, Inverse Problems, 27 (2011). doi: 10.1088/0266-5611/27/1/015008. Google Scholar

[18]

M. Hairer, A. M. Stuart and J. Voss, Analysis of SPDEs arising in path sampling. II. The nonlinear case,, Annals of Applied Probability, 17 (2007), 1657. doi: 10.1214/07-AAP441. Google Scholar

[19]

J. Kaipio and E. Somersalo, "Statistical and Computational Inverse Problems," Applied Mathematical Sciences, 160,, Springer-Verlag, (2005). Google Scholar

[20]

J.-P. Kahane, "Some Random Series of Functions,", Cambridge Studies in Advanced Mathematics, 5 (1985). Google Scholar

[21]

S. Lasanen, Discretizations of generalized random variables with applications to inverse problems,, Dissertation, (2002). Google Scholar

[22]

S. Lasanen, Measurements and infinite-dimensional statistical inverse theory,, PAMM, 7 (2007), 1080101. doi: 10.1002/pamm.200700068. Google Scholar

[23]

M. Lassas, E. Saksman and S. Siltanen, Discretization invariant Bayesian inversion and Besov space priors,, Inverse Problems and Imaging, 3 (2009), 87. doi: 10.3934/ipi.2009.3.87. Google Scholar

[24]

M. S. Lehtinen, L. Päivärinta and E. Somersalo, Linear inverse problems for generalized random variables,, Inverse Problems, 5 (1989), 599. doi: 10.1088/0266-5611/5/4/011. Google Scholar

[25]

A. Mandelbaum, Linear estimators and measurable linear transformations on a Hilbert space,, Z. Wahrsch. Verw. Gebiete, 65 (1984), 385. Google Scholar

[26]

Y. Meyer, "Wavelets and Operators," Translated from the 1990 French original by D. H. Salinger,, Cambridge Studies in Advanced Mathematics, 37 (1992). Google Scholar

[27]

P. Piiroinen, "Statistical Measruements, Experiments and Applications,", Dissertation, (2005). Google Scholar

[28]

Ch. Schwab and A. M. Stuart, Sparse deterministic approximation of Bayesian inverse problems,, submitted, (2011). Google Scholar

[29]

P. D. Spanos and R. Ghanem, Stochastic finite element expansion for random media,, J. Eng. Mech., 115 (1989), 1035. doi: 10.1061/(ASCE)0733-9399(1989)115:5(1035). Google Scholar

[30]

A. M. Stuart, Inverse problems: A Bayesian approach,, Acta Numerica, (2010). doi: 10.1017/S0962492910000061. Google Scholar

[31]

H. Triebel, "Theory of Function Spaces,", Monographs in Mathematics, 78 (1983). Google Scholar

[32]

P. Wojtaszczyk, "A Mathematical Introduction to Wavelets," London Mathematical Society Student Texts, 37,, Cambridge University Press, (1997). Google Scholar

[1]

Tan Bui-Thanh, Omar Ghattas. A scalable algorithm for MAP estimators in Bayesian inverse problems with Besov priors. Inverse Problems & Imaging, 2015, 9 (1) : 27-53. doi: 10.3934/ipi.2015.9.27

[2]

Guillaume Bal, Ian Langmore, Youssef Marzouk. Bayesian inverse problems with Monte Carlo forward models. Inverse Problems & Imaging, 2013, 7 (1) : 81-105. doi: 10.3934/ipi.2013.7.81

[3]

Kui Lin, Shuai Lu, Peter Mathé. Oracle-type posterior contraction rates in Bayesian inverse problems. Inverse Problems & Imaging, 2015, 9 (3) : 895-915. doi: 10.3934/ipi.2015.9.895

[4]

T. J. Sullivan. Well-posed Bayesian inverse problems and heavy-tailed stable quasi-Banach space priors. Inverse Problems & Imaging, 2017, 11 (5) : 857-874. doi: 10.3934/ipi.2017040

[5]

Tan Bui-Thanh, Quoc P. Nguyen. FEM-based discretization-invariant MCMC methods for PDE-constrained Bayesian inverse problems. Inverse Problems & Imaging, 2016, 10 (4) : 943-975. doi: 10.3934/ipi.2016028

[6]

Colin Guillarmou, Antônio Sá Barreto. Inverse problems for Einstein manifolds. Inverse Problems & Imaging, 2009, 3 (1) : 1-15. doi: 10.3934/ipi.2009.3.1

[7]

Sergei Avdonin, Pavel Kurasov. Inverse problems for quantum trees. Inverse Problems & Imaging, 2008, 2 (1) : 1-21. doi: 10.3934/ipi.2008.2.1

[8]

Maciej Zworski. A remark on inverse problems for resonances. Inverse Problems & Imaging, 2007, 1 (1) : 225-227. doi: 10.3934/ipi.2007.1.225

[9]

Guanghui Hu, Peijun Li, Xiaodong Liu, Yue Zhao. Inverse source problems in electrodynamics. Inverse Problems & Imaging, 2018, 12 (6) : 1411-1428. doi: 10.3934/ipi.2018059

[10]

Michael Herty, Giuseppe Visconti. Kinetic methods for inverse problems. Kinetic & Related Models, 2019, 12 (5) : 1109-1130. doi: 10.3934/krm.2019042

[11]

Shiyun Wang, Yong-Jin Liu, Yong Jiang. A majorized penalty approach to inverse linear second order cone programming problems. Journal of Industrial & Management Optimization, 2014, 10 (3) : 965-976. doi: 10.3934/jimo.2014.10.965

[12]

Janne M.J. Huttunen, J. P. Kaipio. Approximation errors in nonstationary inverse problems. Inverse Problems & Imaging, 2007, 1 (1) : 77-93. doi: 10.3934/ipi.2007.1.77

[13]

Xiaosheng Li, Gunther Uhlmann. Inverse problems with partial data in a slab. Inverse Problems & Imaging, 2010, 4 (3) : 449-462. doi: 10.3934/ipi.2010.4.449

[14]

Sergei A. Avdonin, Sergei A. Ivanov, Jun-Min Wang. Inverse problems for the heat equation with memory. Inverse Problems & Imaging, 2019, 13 (1) : 31-38. doi: 10.3934/ipi.2019002

[15]

Fabrizio Colombo, Irene Sabadini, Frank Sommen. The inverse Fueter mapping theorem. Communications on Pure & Applied Analysis, 2011, 10 (4) : 1165-1181. doi: 10.3934/cpaa.2011.10.1165

[16]

Johannes Elschner, Guanghui Hu. Uniqueness in inverse transmission scattering problems for multilayered obstacles. Inverse Problems & Imaging, 2011, 5 (4) : 793-813. doi: 10.3934/ipi.2011.5.793

[17]

François Monard, Guillaume Bal. Inverse diffusion problems with redundant internal information. Inverse Problems & Imaging, 2012, 6 (2) : 289-313. doi: 10.3934/ipi.2012.6.289

[18]

Gabriel Peyré, Sébastien Bougleux, Laurent Cohen. Non-local regularization of inverse problems. Inverse Problems & Imaging, 2011, 5 (2) : 511-530. doi: 10.3934/ipi.2011.5.511

[19]

Simopekka Vänskä. Stationary waves method for inverse scattering problems. Inverse Problems & Imaging, 2008, 2 (4) : 577-586. doi: 10.3934/ipi.2008.2.577

[20]

Victor Isakov, Shuai Lu. Inverse source problems without (pseudo) convexity assumptions. Inverse Problems & Imaging, 2018, 12 (4) : 955-970. doi: 10.3934/ipi.2018040

2018 Impact Factor: 1.469

Metrics

  • PDF downloads (13)
  • HTML views (0)
  • Cited by (18)

Other articles
by authors

[Back to Top]