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

Weighted model integration (WMI) extends weighted model counting (WMC) inproviding a computational abstraction for probabilistic inference in mixeddiscrete-continuous domains. WMC has emerged as an assembly language forstate-of-the-art reasoning in Bayesian networks, factor graphs, probabilisticprograms and probabilistic databases. In this regard, WMI shows immense promiseto be much more widely applicable, especially as many real-world applicationsinvolve attribute and feature spaces that are continuous and mixed.Nonetheless, state-of-the-art tools for WMI are limited and less mature thantheir propositional counterparts. In this work, we propose a new implementationregime that leverages propositional knowledge compilation for scaling upinference. In particular, we use sentential decision diagrams, a tractablerepresentation of Boolean functions, as the underlying model counting and modelenumeration scheme. Our regime performs competitively to state-of-the-art WMIsystems but is also shown to handle a specific class of non-linear constraintsover non-linear potentials.

Scaling up Probabilistic Inference in Linear and Non-Linear Hybrid Domains by Leveraging Knowledge Compilation