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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Probabilistic independence can dramatically simplify the task of eliciting,representing, and computing with probabilities in large domains. A keytechnique in achieving these benefits is the idea of graphical modeling. Wesurvey existing notions of independence for utility functions in amulti-attribute space, and suggest that these can be used to achieve similaradvantages. Our new results concern conditional additive independence, which weshow always has a perfect representation as separation in an undirected graph(a Markov network). Conditional additive independencies entail a particularfunctional for the utility function that is analogous to a productdecomposition of a probability function, and confers analogous benefits. Thisfunctional form has been utilized in the Bayesian network and influence diagramliterature, but generally without an explanation in terms of independence. Thefunctional form yields a decomposition of the utility function that can greatlyspeed up expected utility calculations, particularly when the utility graph hasa similar topology to the probabilistic network being used.

Graphical Models for Preference and Utility