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

Functional data registration is a necessary processing step for manyapplications. The observed data can be inherently noisy, often due tomeasurement error or natural process uncertainty, which most functionalalignment methods cannot handle. A pair of functions can also have multipleoptimal alignment solutions, which is not addressed in current literature. Inthis paper, a flexible Bayesian approach to functional alignment is presented,which appropriately accounts for noise in the data without any pre-smoothingrequired. Additionally, by running parallel MCMC chains, the method can accountfor multiple optimal alignments via the multi-modal posterior distribution ofthe warping functions. To most efficiently sample the warping functions, theapproach relies on a modification of the standard Hamiltonian Monte Carlo to bewell-defined on the infinite-dimensional Hilbert space. This flexible Bayesianalignment method is applied to both simulated data and real data sets to showits efficiency in handling noisy functions and successfully accounting formultiple optimal alignments in the posterior; characterizing the uncertaintysurrounding the warping functions.

Multimodal Bayesian Registration of Noisy Functions using Hamiltonian Monte Carlo