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

We introduce a Bayesian framework for inference with a supervised version ofthe Gaussian process latent variable model. The framework overcomes the highcorrelations between latent variables and hyperparameters by using an unbiasedpseudo estimate for the marginal likelihood that approximately integrates overthe latent variables. This is used to construct a Markov Chain to explore theposterior of the hyperparameters. We demonstrate the procedure on simulated andreal examples, showing its ability to capture uncertainty and multimodality ofthe hyperparameters and improved uncertainty quantification in predictions whencompared with variational inference.

Pseudo-marginal Bayesian inference for supervised Gaussian process latent variable models