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

Approximate Bayesian Computation (ABC) is a statistical learning technique tocalibrate and select models by comparing observed data to simulated data. Thistechnique bypasses the use of the likelihood and requires only the ability togenerate synthetic data from the models of interest. We apply ABC to fit andcompare insurance loss models using aggregated data. A state-of-the-art ABCimplementation in Python is proposed. It uses sequential Monte Carlo to samplefrom the posterior distribution and the Wasserstein distance to compare theobserved and synthetic data.

Approximate Bayesian Computations to fit and compare insurance loss models