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

Estimating information-theoretic quantities such as entropy and mutualinformation is central to many problems in statistics and machine learning, butchallenging in high dimensions. This paper presents estimators of entropy viainference (EEVI), which deliver upper and lower bounds on many informationquantities for arbitrary variables in a probabilistic generative model. Theseestimators use importance sampling with proposal distribution families thatinclude amortized variational inference and sequential Monte Carlo, which canbe tailored to the target model and used to squeeze true information valueswith high accuracy. We present several theoretical properties of EEVI anddemonstrate scalability and efficacy on two problems from the medical domain:(i) in an expert system for diagnosing liver disorders, we rank medical testsaccording to how informative they are about latent diseases, given a pattern ofobserved symptoms and patient attributes; and (ii) in a differential equationmodel of carbohydrate metabolism, we find optimal times to take blood glucosemeasurements that maximize information about a diabetic patient's insulinsensitivity, given their meal and medication schedule.

Estimators of Entropy and Information via Inference in Probabilistic Models