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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

To analyze complex datasets efficiently, ABC algorithms require well-chosen low dimensional summary statistics of the data. We present a method to construct appropriate summary statistics for ABC in a semi-automatic manner. Previous attempts at this have been based around constructing statistics that are sufficient (or approximately sufficient). However, in most real applications it is difficult to know if low-dimensional sufficient statistics exist, or how to approximate them if they do. We take an alternative approach of aiming for summary statistics which will enable inference about certain parameters of interest to be as accurate as possible. &#xa0;This talk describes our method, the underpinning theory, and promising empirical results on a range of applications

Constructing ABC summary statistics: semi-automatic ABC