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

This paper introduces the probabilistic module interface, which allowsencapsulation of complex probabilistic models with latent variables alongsidecustom stochastic approximate inference machinery, and provides aplatform-agnostic abstraction barrier separating the model internals from thehost probabilistic inference system. The interface can be seen as a stochasticgeneralization of a standard simulation and density interface for probabilisticprimitives. We show that sound approximate inference algorithms can beconstructed for networks of probabilistic modules, and we demonstrate that theinterface can be implemented using learned stochastic inference networks andMCMC and SMC approximate inference programs.

Encapsulating models and approximate inference programs in probabilistic modules