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 provide the first stochastic convergence rates for a family of adaptivequadrature rules used to normalize the posterior distribution in Bayesianmodels. Our results apply to the uniform relative error in the approximateposterior density, the coverage probabilities of approximate credible sets, andapproximate moments and quantiles, therefore guaranteeing fast asymptoticconvergence of approximate summary statistics used in practice. The family ofquadrature rules includes adaptive Gauss-Hermite quadrature, and we apply thisrule in two challenging low-dimensional examples. Further, we demonstrate howadaptive quadrature can be used as a crucial component of a modern approximateBayesian inference procedure for high-dimensional additive models. The methodis implemented and made publicly available in the aghq package for the Rlanguage, available on CRAN.

Stochastic Convergence Rates and Applications of Adaptive Quadrature in Bayesian Inference