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 nearly any challenging scientific problem evaluation of the likelihood isproblematic if not impossible. Approximate Bayesian computation (ABC) allows usto employ the whole Bayesian formalism to problems where we can use simulationsfrom a model, but cannot evaluate the likelihood directly. When summarystatistics of real and simulated data are compared --- rather than the datadirectly --- information is lost, unless the summary statistics are sufficient.Here we employ an information-theoretical framework that can be used toconstruct (approximately) sufficient statistics by combining differentstatistics until the loss of information is minimized. Such sufficient sets ofstatistics are constructed for both parameter estimation and model selectionproblems. We apply our approach to a range of illustrative and real-world modelselection problems.

Considerate Approaches to Achieving Sufficiency for ABC model selection