Abstract
The Bayesian inference is widely used in many scientific and engineering problems, especially in the linear inverse problems in infinite-dimensional setting where the unknowns are functions. In such problems, choosing an appropriate prior distribution is an important task. Especially when the function to infer has much detail information, such as many sharp jumps, corners, and the discontinuous and nonsmooth oscillation, the so-called total variation-Gaussian (TG) prior is proposed in function space to address it. However, the TG prior is easy to lead the blocky (staircase) effect in numerical results. In this work, we present a fractional order-TG (FTG) hybrid prior to deal with such problems, where the fractional order total variation (FTV) term is used to capture the detail information of the unknowns and simultaneously uses the Gaussian measure to ensure that it results in a well-defined posterior measure. For the numerical implementations of linear inverse problems in function spaces, we also propose an efficient independence sampler based on a transport map, which uses a proposal distribution derived from a diagonal map, and the acceptance probability associated to the proposal is independent of discretization dimensionality. And in order to take full advantage of the transport map, the hierarchical Bayesian framework is applied to flexibly determine the regularization parameter. Finally we provide some numerical examples to demonstrate the performance of the FTG prior and the efficiency and robustness of the proposed independence sampler method.
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References
Adams R (1975) Sobolev Spaces. Academic Press, New York
Babacan S, Molina R, Katsaggelos A (2008) Variational bayesian blind deconvolution using a total variation prior. IEEE Trans Image Process 18(1):12–26
Bonnotte N (2013) From knothe’s rearrangement to brenier’s optimal transport map. SIAM J Math Anal 45:64–87
Byrd R, Gilbert J, Nocedal J (2000) A trust region method based on interior point techniques for nonlinear programming. Math Program 89:149–185
Carlier G, Galichon A, Santambrogio F (2010) From knothe’s transport to brenier’s map and a continuation method for optimal transport. SIAM J Math Anal 41(6):2554–2576
Cotter S, Roberts G, Stuart A, White D (2013) MCMC methods for functions: Modifying old algorithms to make them faster, Stati Sci 28(3)
Cui T, Law K, Marzouk Y (2016) Dimension-independent likelihood-informed mcmc. J Comput Phys 304:109–137
Dashti M, Law K, Stuart A, Voss J (2013) MAP estimators and their consistency in bayesian nonparametric inverse problems. Inverse Prob 29(9):095017
Dashti M, Stuart A (2015) The bayesian approach to inverse problems, In: Handbook of Uncertainty Quantification, Springer International Publishing p 1–118
Feng Z, Li J (2018) An adaptive independence sampler mcmc algorithm for bayesian inferences of functions. SIAM J Sci Comput 40(3):A1301–A1321
Gamerman D, Lopes H (2006) Markov chain Monte Carlo: stochastic simulation for Bayesian inference, CRC Press
Gelman A, Carlin J, Stern H, Dunson D, Vehtari A, Rubin D (2013) Bayesian data analysis, Chapman and Hall/CRC
Jin B, Zou J (2010) Hierarchical bayesian inference for ill-posed problems via variational method. J Comput Phys 229(19):7317–7343
Johansson B, Lesnic D (2007) A variational method for identifying a spacewise-dependent heat source. IMA J Appl Math 72(6):748–760
Johansson T, Lesnic D (2007) Determination of a spacewise dependent heat source. J Comput Appl Math 209(1):66–80
Kaipio J, Somersalo E (2005) Statistical and computational inverse problems, Springer-Verlag
Kass R, Carlin B, Gelman A, Neal R (1998) Markov chain montecarlo in practice: a roundtable discussion. Am Stat 52(2):93–100
Kilbas A, Srivastava H, Trujillo J (2006) Theory and applications of fractional differential equations, Elsevier
Kleywegt A, Shapiro A, Mello T (2002) The sample average approximation method for stochastic discrete optimization. SIAM J Optim 12(2):479–502
Lassas M, Siltanen S (2004) Can one use total variation prior for edge-preserving bayesian inversion? Inverse Prob 20(5):1537
Li L, Jafarpour B (2010) Effective solution of nonlinear subsurface flow inverse problems in sparse bases. Inverse Prob 26(10):105016
Martin J, Wilcox L, Burstedde C, Ghattas O (2012) A stochastic newton mcmc method for large-scale statistical inverse problems with application to seismic inversion. SIAM J Sci Comput 34(3):A1460–A1487
Marzouk Y, Moselhy T, Parno M, Spantini A (2016) Sampling via measure transport: an introduction, In: Handbook of Uncertainty Quantification, Springer International Publishing p 1–41
Moselhy T, Marzouk Y (2012) Bayesian inference with optimal maps. J Comput Phys 231(23):7815–7850
Mueller J, Siltanen S (2012) Linear and nonlinear inverse problems with practical applications. Society for Industrial and Applied Mathematics, Philadelphia, PA
Parno M, Marzouk Y (2018) Transport map accelerated markov chain monte carlo. SIAM/ASA J Uncertain Quantif 6(2):645–682
Peherstorfer B, Marzouk Y (2019) A transport-based multifidelity preconditioner for markov chain monte carlo. Adv Comput Math 45(5–6):2321–2348
Radon J (1986) On the determination of functions from their integral values along certain manifolds. IEEE Trans Med Imaging 5(4):170–176
Robert C, Casella G, Casella G (2004) Monte Carlo statistical methods, vol. 2, Springer
Roberts G, Rosenthal J (2001) Optimal scaling for various metropolis-hastings algorithms. Stat Sci 4:351–67
Rosenblatt M (1952) Remarks on a multivariate transformation. Ann Math Stat 23:470–472
Rudin L, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Phys D 60(1):259–268
Samko S, Kilbas A, Marichev O (1993) Fractional integrals and derivatives: theory and applications, CRC Press
Stuart A (2010) Inverse problems: A bayesian perspective. Acta Numer 19:451–559
Tierney L (1998) A note on metropolis-hastings kernels for general state spaces, Ann Appl Probab, p 1–9
Tierney L (1994) Markov chains for exploring posterior distributions. Annal Stat Pages 1:1701–1728
Vershik A (2013) Long history of the Monge-Kantorovich transportation problem. Math Intell 35(4):1–9
Villani C (2003) Topics in optimal transportation: american mathematical society, Grad Stud Math, 58
Villani C (2009) Optimal transport: old and new, Vol. 338, Springer
Vogel C (2002) Computational methods for inverse problems, Soc Ind Appl Math
Wang Z, Bovik A, Sheikh H, Simoncelli E (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612
Wang L, Ding M, Zheng G. A general fractional total variation-Gaussian (GFTG) prior for Bayesian inverse problems, arXiv:2111.02797
Wang L, Ding M, Zheng G. A Hadamard fractioal total variation-Gaussian (HFTG) prior for Bayesian inverse problems, arXiv:2110.15656
Yan L, Fu C, Dou F (2010) A computational method for identifying a Spacewise-dependent heat source. Int J Numer Methods Biomed Eng 26(5):597–608
Yao Z, Hu Z, Li J (2016) A TV-gaussian prior for infinite-dimensional bayesian inverse problems and its numerical implementations. Inverse Prob 32(7):075006
Zhang J, Chen K (2015) A total fractional-order variation model for image restoration with nonhomogeneous boundary conditions and its numerical solution. SIAM J Imag Sci 8(4):2487–2518
Zhang J, Chen K (2015) Variational image registration by a total fractional-order variation model. J Comput Phys 293:442–461
Acknowledgements
The work described in this paper was supported by the NSF of China (12271151) and NSF of Hunan (2020JJ4166).
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Sun, Z., Zheng, GH. Solving linear Bayesian inverse problems using a fractional total variation-Gaussian (FTG) prior and transport map. Comput Stat 38, 1811–1849 (2023). https://doi.org/10.1007/s00180-023-01332-w
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DOI: https://doi.org/10.1007/s00180-023-01332-w