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 present the GPry algorithm for fast Bayesian inference of general(non-Gaussian) posteriors with a moderate number of parameters. GPry does notneed any pre-training, special hardware such as GPUs, and is intended as adrop-in replacement for traditional Monte Carlo methods for Bayesian inference.Our algorithm is based on generating a Gaussian Process surrogate model of thelog-posterior, aided by a Support Vector Machine classifier that excludesextreme or non-finite values. An active learning scheme allows us to reduce thenumber of required posterior evaluations by two orders of magnitude compared totraditional Monte Carlo inference. Our algorithm allows for parallelevaluations of the posterior at optimal locations, further reducing wall-clocktimes. We significantly improve performance using properties of the posteriorin our active learning scheme and for the definition of the GP prior. Inparticular we account for the expected dynamical range of the posterior indifferent dimensionalities. We test our model against a number of synthetic andcosmological examples. GPry outperforms traditional Monte Carlo methods whenthe evaluation time of the likelihood (or the calculation of theoreticalobservables) is of the order of seconds; for evaluation times of over a minuteit can perform inference in days that would take months using traditionalmethods. GPry is distributed as an open source Python package (pip installgpry) and can also be found at https://github.com/jonaselgammal/GPry.

Fast and robust Bayesian Inference using Gaussian Processes with GPry