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

Simulation models of complex dynamics in the natural and social sciencescommonly lack a tractable likelihood function, rendering traditionallikelihood-based statistical inference impossible. Recent advances in machinelearning have introduced novel algorithms for estimating otherwise intractablelikelihood functions using a likelihood ratio trick based on binaryclassifiers. Consequently, efficient likelihood approximations can be obtainedwhenever good probabilistic classifiers can be constructed. We propose a kernelclassifier for sequential data using path signatures based on the recentlyintroduced signature kernel. We demonstrate that the representative power ofsignatures yields a highly performant classifier, even in the cruciallyimportant case where sample numbers are low. In such scenarios, our approachcan outperform sophisticated neural networks for common posterior inferencetasks.

Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation