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

Identification of taxa can significantly be assisted by statisticalclassification based on trait measurements in two major ways; eitherindividually or by phylogenetic (clustering) methods. In this paper we presenta general Bayesian approach for classifying species individually based onmeasurements of a mixture of continuous and ordinal traits as well as any typeof covariates. It is assumed that the trait vector is derived from a latentvariable with a multivariate Gaussian distribution. Decision rules based onsupervised learning are presented that estimate model parameters throughblockwise Gibbs sampling. These decision regions allow for uncertainty (partialrejection), so that not necessarily one specific category (taxon) is outputwhen new subjects are classified, but rather a set of categories including themost probable taxa. This type of discriminant analysis employs reward functionswith a set-valued input argument, so that an optimal Bayes classifier can bedefined. We also present a way of safeguarding against outlying newobservations, using an analogue of a $p$-value within our Bayesian setting. Ourmethod is illustrated on an original ornithological data set of birds. We alsoincorporate model selection through cross-validation, examplified on anotheroriginal data set of birds.

Identification of taxon through classification with partial reject options