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

Bayesian methods are a popular choice for statistical inference in small-dataregimes due to the regularization effect induced by the prior. In the contextof density estimation, the standard nonparametric Bayesian approach is totarget the posterior predictive of the Dirichlet process mixture model. Ingeneral, direct estimation of the posterior predictive is intractable and somethods typically resort to approximating the posterior distribution as anintermediate step. The recent development of quasi-Bayesian predictive copulaupdates, however, has made it possible to perform tractable predictive densityestimation without the need for posterior approximation. Although theseestimators are computationally appealing, they tend to struggle on non-smoothdata distributions. This is due to the comparatively restrictive form of thelikelihood models from which the proposed copula updates were derived. Toaddress this shortcoming, we consider a Bayesian nonparametric model with anautoregressive likelihood decomposition and a Gaussian process prior. While thepredictive update of such a model is typically intractable, we derive aquasi-Bayesian predictive update that achieves state-of-the-art results insmall-data regimes.

Quasi-Bayesian Nonparametric Density Estimation via Autoregressive Predictive Updates