4,213 research outputs found
Optimal Transport-based Nonlinear Filtering in High-dimensional Settings
This paper addresses the problem of nonlinear filtering, i.e., computing the
conditional distribution of the state of a stochastic dynamical system given a
history of noisy partial observations. The primary focus is on scenarios
involving degenerate likelihoods or high-dimensional states, where traditional
sequential importance resampling (SIR) particle filters face the weight
degeneracy issue. Our proposed method builds on an optimal transport
interpretation of nonlinear filtering, leading to a simulation-based and
likelihood-free algorithm that estimates the Brenier optimal transport map from
the current distribution of the state to the distribution at the next time
step. Our formulation allows us to harness the approximation power of neural
networks to model complex and multi-modal distributions and employ stochastic
optimization algorithms to enhance scalability. Extensive numerical experiments
are presented that compare our method to the SIR particle filter and the
ensemble Kalman filter, demonstrating the superior performance of our method in
terms of sample efficiency, high-dimensional scalability, and the ability to
capture complex and multi-modal distributions.Comment: 24 pages, 15 figure
Transform-based particle filtering for elliptic Bayesian inverse problems
We introduce optimal transport based resampling in adaptive SMC. We consider
elliptic inverse problems of inferring hydraulic conductivity from pressure
measurements. We consider two parametrizations of hydraulic conductivity: by
Gaussian random field, and by a set of scalar (non-)Gaussian distributed
parameters and Gaussian random fields. We show that for scalar parameters
optimal transport based SMC performs comparably to monomial based SMC but for
Gaussian high-dimensional random fields optimal transport based SMC outperforms
monomial based SMC. When comparing to ensemble Kalman inversion with mutation
(EKI), we observe that for Gaussian random fields, optimal transport based SMC
gives comparable or worse performance than EKI depending on the complexity of
the parametrization. For non-Gaussian distributed parameters optimal transport
based SMC outperforms EKI
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