Sequential Kalman Monte Carlo for gradient-free inference in Bayesian inverse problems

• URL: http://arxiv.org/abs/2407.07781v1
• Date: Wed, 10 Jul 2024 15:56:30 GMT
• Title: Sequential Kalman Monte Carlo for gradient-free inference in Bayesian inverse problems
• Authors: Richard D. P. Grumitt, Minas Karamanis, Uroš Seljak,
• Abstract summary: We introduce Sequential Kalman Monte Carlo samplers to perform gradient-free inference in inverse problems. FAKI employs normalizing flows to relax the Gaussian ansatz of the target measures in EKI. FAKI alone is not able to correct for the model linearity assumptions in EKI.
• Score: 1.3654846342364308
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ensemble Kalman Inversion (EKI) has been proposed as an efficient method for solving inverse problems with expensive forward models. However, the method is based on the assumption that we proceed through a sequence of Gaussian measures in moving from the prior to the posterior, and that the forward model is linear. In this work, we introduce Sequential Kalman Monte Carlo (SKMC) samplers, where we exploit EKI and Flow Annealed Kalman Inversion (FAKI) within a Sequential Monte Carlo (SMC) sampling scheme to perform efficient gradient-free inference in Bayesian inverse problems. FAKI employs normalizing flows (NF) to relax the Gaussian ansatz of the target measures in EKI. NFs are able to learn invertible maps between a Gaussian latent space and the original data space, allowing us to perform EKI updates in the Gaussianized NF latent space. However, FAKI alone is not able to correct for the model linearity assumptions in EKI. Errors in the particle distribution as we move through the sequence of target measures can therefore compound to give incorrect posterior moment estimates. In this work we consider the use of EKI and FAKI to initialize the particle distribution for each target in an adaptive SMC annealing scheme, before performing t-preconditioned Crank-Nicolson (tpCN) updates to distribute particles according to the target. We demonstrate the performance of these SKMC samplers on three challenging numerical benchmarks, showing significant improvements in the rate of convergence compared to standard SMC with importance weighted resampling at each temperature level. Code implementing the SKMC samplers is available at https://github.com/RichardGrumitt/KalmanMC.

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