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September  2020, 2(3): 333-349. doi: 10.3934/fods.2020016

Posterior contraction rates for non-parametric state and drift estimation

Sebastian Reich , and  Paul J. Rozdeba

Institute of Mathematics, University of Potsdam, Karl-Liebknecht-Str. 24/25, D-14476 Potsdam, Germany

* Corresponding author: Sebastian Reich

Received  March 2020 Revised  August 2020 Published  October 2020

We consider a combined state and drift estimation problem for the linear stochastic heat equation. The infinite-dimensional Bayesian inference problem is formulated in terms of the Kalman–Bucy filter over an extended state space, and its long-time asymptotic properties are studied. Asymptotic posterior contraction rates in the unknown drift function are the main contribution of this paper. Such rates have been studied before for stationary non-parametric Bayesian inverse problems, and here we demonstrate the consistency of our time-dependent formulation with these previous results building upon scale separation and a slow manifold approximation.

Keywords: State and parameter estimation, Bayesian inference, posterior contraction rates, stochastic partial differential equations, Kalman–Bucy filter.
Mathematics Subject Classification: Primary: 62G20, 62G05, 62F15; Secondary: 60H15, 62M20.
Citation: Sebastian Reich, Paul J. Rozdeba. Posterior contraction rates for non-parametric state and drift estimation. Foundations of Data Science, 2020, 2 (3) : 333-349. doi: 10.3934/fods.2020016
References:
[1]

R. Altmeyer and M. Reiß, Nonparametric estimation for linear SPDEs from local measurements, Annals of Appl. Probability, preprint, arXiv: 1903.06984. Google Scholar

[2]

K. Bergemann and S. Reich, An ensemble Kalman–Bucy filter for continuous data assimilation, Meteorolog. Zeitschrift, 21 (2012), 213-219.  doi: 10.1127/0941-2948/2012/0307.  Google Scholar

[3]

I. Cialenco, Statistical inference for SPDEs: An overview, Stat. Inference Stoch. Process., 21 (2018), 309-329.  doi: 10.1007/s11203-018-9177-9.  Google Scholar

[4]

R. Curtain, A survey of infinite-dimensional filtering, SIAM Review, 17 (1975), 395-411.  doi: 10.1137/1017041.  Google Scholar

[5]

G. Da Prato and J. Zabczyk, Stochastic Equations in Infinite Dimensions, 1st edition, Encyclopedia of Mathematics and its Applications, 45, Cambridge University Press, Cambridge, 1992. doi: 10.1017/CBO9780511666223.  Google Scholar

[6]

J. de Wiljes, S. Reich and W. Stannat, Long-time stability and accuracy of the ensemble Kalman–Bucy filter for fully observed processes and small measurement noise, SIAM J. Appl. Dyn. Syst., 17 (2018), 1152-1181.  doi: 10.1137/17M1119056.  Google Scholar

[7]

S. Ghosal and A. van der Vaart, Fundamentals of Nonparametric Bayesian Inference, Cambridge Series in Statistical and Probabilistic Mathematics, 44, Cambridge University Press, Cambridge, 2017. doi: 10.1017/9781139029834.  Google Scholar

[8]

E. Giné and R. Nickl, Mathematical Foundations of Infinite-Dimensional Statistical Models, Cambridge Series in Statistical and Probabilistic Mathematics, 40, Cambridge University Press, Cambridge, 2015. doi: 10.1017/CBO9781107337862.  Google Scholar

[9]

F. Götze, A. Naumov, V. Spokoiny and V. Ulyanov, Large ball probabilities, Gaussian comparison and anti-concentration, Bernoulli, 25 (2019), 2538-2563.  doi: 10.3150/18-BEJ1062.  Google Scholar

[10]

M. Hairer, An introduction to stochastic PDEs, unpublished lecture notes, arXiv: 0907.4178. Google Scholar

[11]

A. Jazwinski, Stochastic Processes and Filtering Theory, Mathematics in Science and Engineering, 64, Academic Press, New York, 1970. Google Scholar

[12]

B. T. Knapik, B. T. Szabó, A. W. van der Vaart and J. H. van Zanten, Bayes procedures for adaptive inference in inverse problems for the white noise model, Probab. Theory Relat. Fields, 164 (2016), 771-813.  doi: 10.1007/s00440-015-0619-7.  Google Scholar

[13]

B. T. Knapik, A. W. van der Vaart and J. H. van Zanten, Bayesian inverse problems with Gaussian priors, Annals of Statistics, 39 (2011), 2626-2657.  doi: 10.1214/11-AOS920.  Google Scholar

[14]

G. J. Lord, C. E. Powell and T. Shardlow, An Introduction to Computational Stochastic PDEs, 1st edition, Cambridge Texts in Applied Mathematics, 50, Cambridge University Press, Cambridge, 2014. doi: 10.1017/CBO9781139017329.  Google Scholar

[15]

N. Nüsken, S. Reich and P. J. Rozdeba, State and parameter estimation from observed signal increments, Entropy, 21 (2019), 505. doi: 10.3390/e21050505.  Google Scholar

[16]

E. Shchepakina, V. Sobolev and M. P. Mortell, Singular Perturbations: Introduction to System Order Reduction Methods with Applications, Lecture Notes in Mathematics, 2114, Springer International Publishing, 2014. doi: 10.1007/978-3-319-09570-7.  Google Scholar

[17]

D. Simon, Optimal State Estimation, Wiley, Hoboken, 2006. doi: 10.1002/0470045345.  Google Scholar

[18]

A. Taghvaei, J. de Wiljes, P. Mehta and S. Reich, Kalman filter and its modern extensions for the continuous-time nonlinear filtering problem, J. Dyn. Sys., Meas., Control., 140 (2017), 030904. doi: 10.1115/1.4037780.  Google Scholar

[19]

F. Verhulst, Singular perturbation methods for slow-fast dynamics, Nonlinear Dynamics, 50 (2007), 747-753.  doi: 10.1007/s11071-007-9236-z.  Google Scholar

[20]

D. Yan, Bayesian inference for Gaussian models: Inverse problems and evolution equations, PhD thesis, Universiteit Leiden, Leiden, 2020. doi: 1887/86070.  Google Scholar

[21]

T. Yang, P. Mehta and S. Meyn, Feedback particle filter, IEEE Trans. Automatic Control, 58 (2013), 2465-2480.  doi: 10.1109/TAC.2013.2258825.  Google Scholar

show all references

References:
[1]

R. Altmeyer and M. Reiß, Nonparametric estimation for linear SPDEs from local measurements, Annals of Appl. Probability, preprint, arXiv: 1903.06984. Google Scholar

[2]

K. Bergemann and S. Reich, An ensemble Kalman–Bucy filter for continuous data assimilation, Meteorolog. Zeitschrift, 21 (2012), 213-219.  doi: 10.1127/0941-2948/2012/0307.  Google Scholar

[3]

I. Cialenco, Statistical inference for SPDEs: An overview, Stat. Inference Stoch. Process., 21 (2018), 309-329.  doi: 10.1007/s11203-018-9177-9.  Google Scholar

[4]

R. Curtain, A survey of infinite-dimensional filtering, SIAM Review, 17 (1975), 395-411.  doi: 10.1137/1017041.  Google Scholar

[5]

G. Da Prato and J. Zabczyk, Stochastic Equations in Infinite Dimensions, 1st edition, Encyclopedia of Mathematics and its Applications, 45, Cambridge University Press, Cambridge, 1992. doi: 10.1017/CBO9780511666223.  Google Scholar

[6]

J. de Wiljes, S. Reich and W. Stannat, Long-time stability and accuracy of the ensemble Kalman–Bucy filter for fully observed processes and small measurement noise, SIAM J. Appl. Dyn. Syst., 17 (2018), 1152-1181.  doi: 10.1137/17M1119056.  Google Scholar

[7]

S. Ghosal and A. van der Vaart, Fundamentals of Nonparametric Bayesian Inference, Cambridge Series in Statistical and Probabilistic Mathematics, 44, Cambridge University Press, Cambridge, 2017. doi: 10.1017/9781139029834.  Google Scholar

[8]

E. Giné and R. Nickl, Mathematical Foundations of Infinite-Dimensional Statistical Models, Cambridge Series in Statistical and Probabilistic Mathematics, 40, Cambridge University Press, Cambridge, 2015. doi: 10.1017/CBO9781107337862.  Google Scholar

[9]

F. Götze, A. Naumov, V. Spokoiny and V. Ulyanov, Large ball probabilities, Gaussian comparison and anti-concentration, Bernoulli, 25 (2019), 2538-2563.  doi: 10.3150/18-BEJ1062.  Google Scholar

[10]

M. Hairer, An introduction to stochastic PDEs, unpublished lecture notes, arXiv: 0907.4178. Google Scholar

[11]

A. Jazwinski, Stochastic Processes and Filtering Theory, Mathematics in Science and Engineering, 64, Academic Press, New York, 1970. Google Scholar

[12]

B. T. Knapik, B. T. Szabó, A. W. van der Vaart and J. H. van Zanten, Bayes procedures for adaptive inference in inverse problems for the white noise model, Probab. Theory Relat. Fields, 164 (2016), 771-813.  doi: 10.1007/s00440-015-0619-7.  Google Scholar

[13]

B. T. Knapik, A. W. van der Vaart and J. H. van Zanten, Bayesian inverse problems with Gaussian priors, Annals of Statistics, 39 (2011), 2626-2657.  doi: 10.1214/11-AOS920.  Google Scholar

[14]

G. J. Lord, C. E. Powell and T. Shardlow, An Introduction to Computational Stochastic PDEs, 1st edition, Cambridge Texts in Applied Mathematics, 50, Cambridge University Press, Cambridge, 2014. doi: 10.1017/CBO9781139017329.  Google Scholar

[15]

N. Nüsken, S. Reich and P. J. Rozdeba, State and parameter estimation from observed signal increments, Entropy, 21 (2019), 505. doi: 10.3390/e21050505.  Google Scholar

[16]

E. Shchepakina, V. Sobolev and M. P. Mortell, Singular Perturbations: Introduction to System Order Reduction Methods with Applications, Lecture Notes in Mathematics, 2114, Springer International Publishing, 2014. doi: 10.1007/978-3-319-09570-7.  Google Scholar

[17]

D. Simon, Optimal State Estimation, Wiley, Hoboken, 2006. doi: 10.1002/0470045345.  Google Scholar

[18]

A. Taghvaei, J. de Wiljes, P. Mehta and S. Reich, Kalman filter and its modern extensions for the continuous-time nonlinear filtering problem, J. Dyn. Sys., Meas., Control., 140 (2017), 030904. doi: 10.1115/1.4037780.  Google Scholar

[19]

F. Verhulst, Singular perturbation methods for slow-fast dynamics, Nonlinear Dynamics, 50 (2007), 747-753.  doi: 10.1007/s11071-007-9236-z.  Google Scholar

[20]

D. Yan, Bayesian inference for Gaussian models: Inverse problems and evolution equations, PhD thesis, Universiteit Leiden, Leiden, 2020. doi: 1887/86070.  Google Scholar

[21]

T. Yang, P. Mehta and S. Meyn, Feedback particle filter, IEEE Trans. Automatic Control, 58 (2013), 2465-2480.  doi: 10.1109/TAC.2013.2258825.  Google Scholar

Figure 1.  We display the time evolution of the bias $ |m_t^f(k)-\mathcal{F}^\ast(k)| $ and the variances $ \sigma_t^f(k) $ and $ p_t^f(k) $ for increasing values of $ k $. We compare the results from the combined state and parameter estimation problem (labelled 'dynamic') with those from the associated direct inference problem in the parameter alone (labelled 'stationary'). The delay in the onset of the asymptotic $ t^{-1} $ regime as $ k $ increases can be clearly seen as well as that $ p_t^f(k) < \sigma_t^f(k) $ for all $ t>0 $. Furthermore, as theoretically predicted, the dynamic and stationary problem formulations behave almost identically in terms of their drift estimates
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