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

With larger data at their disposal, scientists are emboldened to tacklecomplex questions that require sophisticated statistical models. It is notunusual for the latter to have likelihood functions that elude analyticalformulations. Even under such adversity, when one can simulate from thesampling distribution, Bayesian analysis can be conducted using approximatemethods such as Approximate Bayesian Computation (ABC) or Bayesian SyntheticLikelihood (BSL). A significant drawback of these methods is that the number ofrequired simulations can be prohibitively large, thus severely limiting theirscope. In this paper we design perturbed MCMC samplers that can be used withinthe ABC and BSL paradigms to significantly accelerate computation whilemaintaining control on computational efficiency. The proposed strategy relieson recycling samples from the chain's past. The algorithmic design is supportedby a theoretical analysis while practical performance is examined via a seriesof simulation examples and data analyses.

Finding our Way in the Dark: Approximate MCMC for Approximate Bayesian Methods