English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

For many scientific inverse problems we are required to evaluate an expensiveforward model. Moreover, the model is often given in such a form that it isunrealistic to access its gradients. In such a scenario, standard Markov ChainMonte Carlo algorithms quickly become impractical, requiring a large number ofserial model evaluations to converge on the target distribution. In this paperwe introduce Flow Annealed Kalman Inversion (FAKI). This is a generalization ofEnsemble Kalman Inversion (EKI), where we embed the Kalman filter updates in atemperature annealing scheme, and use normalizing flows (NF) to map theintermediate measures corresponding to each temperature level to the standardGaussian. In doing so, we relax the Gaussian ansatz for the intermediatemeasures used in standard EKI, allowing us to achieve higher fidelityapproximations to non-Gaussian targets. We demonstrate the performance of FAKIon two numerical benchmarks, showing dramatic improvements over standard EKI interms of accuracy whilst accelerating its already rapid convergence properties(typically in $\mathcal{O}(10)$ steps).

Flow Annealed Kalman Inversion for Gradient-Free Inference in Bayesian Inverse Problems