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

In the context of Gaussian process regression with functional inputs, it iscommon to treat the input as a vector. The parameter space becomesprohibitively complex as the number of functional points increases, effectivelybecoming a hindrance for automatic relevance determination in high-dimensionalproblems. Generalizing a framework for time-varying inputs, we introduce theasymmetric Laplace functional weight (ALF): a flexible, parametric functionthat drives predictive relevance over the index space. Automatic dynamicrelevance determination (ADRD) is achieved with three unknowns per inputvariable and enforces smoothness over the index space. Additionally, we discussa screening technique to assess under complete absence of prior and modelinformation whether ADRD is reasonably consistent with the data. Such tool mayserve for exploratory analyses and model diagnostics. ADRD is applied to remotesensing data and predictions are generated in response to atmosphericfunctional inputs. Fully Bayesian estimation is carried out to identifyrelevant regions of the functional input space. Validation is performed tobenchmark against traditional vector-input model specifications. We find thatADRD outperforms models with input dimension reduction via functional principalcomponent analysis. Furthermore, the predictive power is comparable tohigh-dimensional models, in terms of both mean prediction and uncertainty, with10 times fewer tuning parameters. Enforcing smoothness on the predictiverelevance profile rules out erratic patterns associated with vector-inputmodels.

Automatic Dynamic Relevance Determination for Gaussian process regression with high-dimensional functional inputs