CUED Publications database

Multilevel monte carlo for smoothing via transport methods

Houssineau, J and Jasra, A and Singh, SS (2018) Multilevel monte carlo for smoothing via transport methods. SIAM Journal on Scientific Computing, 40. A2315-A2335. ISSN 1064-8275

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© 2018 Society for Industrial and Applied Mathematics. In this article we consider recursive approximations of the smoothing distribution associated to partially observed stochastic differential equations (SDEs), which are observed discretely in time. Such models appear in a wide variety of applications including econometrics, finance, and engineering. This problem is notoriously challenging, as the smoother is not available analytically and hence requires numerical approximation. This usually consists of applying a time-discretization to the SDE, for instance the Euler method, and then applying a numerical (e.g., Monte Carlo) method to approximate the smoother. This has led to a vast literature on methodology for solving such problems, perhaps the most popular of which is based upon the particle filter (PF), e.g., [A. Doucet and A. Johansen, Handbook of Nonlinear Filtering, Oxford University Press, 2011]. In the context of filtering for this class of problems, it is well known that the particle filter can be improved upon in terms of cost to achieve a given mean squared error (MSE) for estimates. This is in the sense that the computational effort can be reduced to achieve this target MSE by using multilevel methods [M. B. Giles, Oper. Res., 56 (2008), pp. 607–617; M. B. Giles, Acta Numer., 24 (2015), pp. 259–328; S. Heinrich, in Large-Scale Scientific Computing, Springer, New York, 2001] via the multilevel particle filter (MLPF) [A. Gregory, C. Cotter, and S. Reich, SIAM J. Sci. Comp., 38 (2016), pp. A1317–A1338; A. Jasra, K. Kamatani, K. J. Law, and Y. Zhou, SIAM J. Numer. Anal., 55 (2017), pp. 3068–3096; A. Jasra, K. Kamatani, P. Osei, and Y. Zhou, Stat. Comp., 28 (2018), pp. 47–60]. For instance, to obtain a MSE of O(2) for some > 0 when approximating filtering distributions associated with Euler-discretized diffusions with constant diffusion coefficients, the cost of the PF is O(−3) while the cost of the MLPF is O(−2 log()2). In this article we consider a new approach to replace the PF, using transport methods in [A. Spantini, D. Bigoni, and Y. Marzouk, Inference via Low-Dimensional Couplings, preprint, arXiv:1703.06131, 2017]. In the context of filtering, one expects that the proposed method improves upon the MLPF by yielding, under assumptions, a MSE of O(2) for a cost of O(−2). This is established theoretically in an “ideal” example and numerically in numerous examples.

Item Type: Article
Uncontrolled Keywords: transport map stochastic differential equation multilevel Monte Carlo
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 16 Jun 2018 20:08
Last Modified: 27 Jun 2019 02:32
DOI: 10.1137/17M1156071