# Batch Selection for Parallelisation of Bayesian Quadrature

@article{Wagstaff2018BatchSF, title={Batch Selection for Parallelisation of Bayesian Quadrature}, author={Edward Wagstaff and Saad Hamid and Michael A. Osborne}, journal={ArXiv}, year={2018}, volume={abs/1812.01553} }

Integration over non-negative integrands is a central problem in machine learning (e.g. for model averaging, (hyper-)parameter marginalisation, and computing posterior predictive distributions). Bayesian Quadrature is a probabilistic numerical integration technique that performs promisingly when compared to traditional Markov Chain Monte Carlo methods. However, in contrast to easily-parallelised MCMC methods, Bayesian Quadrature methods have, thus far, been essentially serial in nature…

## 3 Citations

### Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination

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This work proposes a parallelised (batch) BQ method, employing techniques from kernel quadrature, that possesses an empirically exponential convergence rate and permits simultaneous inference of both posteriors and model evidence.

### Bayesian Quadrature on Riemannian Data Manifolds

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- 2021

This work focuses on Bayesian quadrature ( bq) to numerically compute integrals over normal laws on Riemannian manifolds learned from data and shows that by leveraging both prior knowledge and an active exploration scheme, bq outperforms Monte Carlo methods on a wide range of integration problems.

### SOBER: Scalable Batch Bayesian Optimization and Quadrature using Recombination Constraints

- Computer Science, Business
- 2023

Batch Bayesian optimisation (BO) has shown to be a sample-eﬃcient method of performing optimisation where expensive-to-evaluate objective functions can be queried in parallel. However, current…

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