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

The context tree source is a source model in which the occurrence probabilityof symbols is determined from a finite past sequence, and is a broader class ofsources that includes i.i.d. and Markov sources. The proposed source model inthis paper represents that a subsequence in each interval is generated from adifferent context tree model. The Bayes code for such sources requiresweighting of the posterior probability distributions for the change patterns ofthe context tree source and for all possible context tree models. Therefore,the challenge is how to reduce this exponential order computational complexity.In this paper, we assume a special class of prior probability distribution ofchange patterns and context tree models, and propose an efficient Bayes codingalgorithm whose computational complexity is the polynomial order.

An Efficient Bayes Coding Algorithm for the Non-Stationary Source in Which Context Tree Model Varies from Interval to Interval