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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In likelihood-free settings where likelihood evaluations are intractable,approximate Bayesian computation (ABC) addresses the formidable inference taskto discover plausible parameters of simulation programs that explain theobservations. However, they demand large quantities of simulation calls.Critically, hyperparameters that determine measures of simulation discrepancycrucially balance inference accuracy and sample efficiency, yet are difficultto tune. In this paper, we present kernel embedding likelihood-free inference(KELFI), a holistic framework that automatically learns model hyperparametersto improve inference accuracy given limited simulation budget. By leveraginglikelihood smoothness with conditional mean embeddings, we nonparametricallyapproximate likelihoods and posteriors as surrogate densities and sample fromclosed-form posterior mean embeddings, whose hyperparameters are learned underits approximate marginal likelihood. Our modular framework demonstratesimproved accuracy and efficiency on challenging inference problems in ecology.

Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic Likelihood-Free Inference