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/

Zendy Plus

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

Zendy Tools

Zendy Open

We introduce a Bayesian framework for indirect local clustering of functionaldata, leveraging B-spline basis expansions and a novel dependent randompartition model. By exploiting the local support properties of B-splines, ourapproach allows partially coincident functional behaviors, achieved when sharedbasis coefficients span sufficiently contiguous regions. This is accomplishedthrough a cutting-edge dependent random partition model that enforcessemi-Markovian dependence across a sequence of partitions. By matching theorder of the B-spline basis with the semi-Markovian dependence structure, theproposed model serves as a highly flexible prior, enabling efficient modelingof localized features in functional data. Furthermore, we extend the utility ofthe dependent random partition model beyond functional data, demonstrating itsapplicability to a broad class of problems where sequences of dependentpartitions are central, and standard Markovian assumptions prove overlyrestrictive. Empirical illustrations, including analyses of simulated data andtide level measurements from the Venice Lagoon, showcase the effectiveness andversatility of the proposed methodology.

Bayesian local clustering of functional data via semi-Markovian random partitions