KO
|
EN
gitlite — search
Search
#python
#javascript
#jquery
#css
#swift
#react
#php
#wordpress
#ios
#golang
#html5
#java
bayesquad
★ 32
Open GitHub ↗
Library for Bayesian Quadrature
Download README (.md)
Explore Similar Repositories
SOBER
:
Fast Bayesian optimization, quadrature, inference over arbitrary domain with GPU parallel acceleration
conformal-as-bayes-quad
:
Code to accompany "Conformal Prediction as Bayesian Quadrature" by Jake Snell & Tom Griffiths (ICML 2025 Outstanding Paper)
BASQ
:
(NeurIPS 2022) Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
herding-paper
:
Optimally-weighted herding is Bayesian Quadrature
Deep-Bayesian-Quadrature-Policy-Optimization
:
Official implementation of the AAAI 2021 paper Deep Bayesian Quadrature Policy Optimization.
// repository documentation
Was this content helpful?
★ 0
(0 ratings)
Select Rating:
★
★
★
★
★
Submit Feedback
Recent Feedback
×
Download README
Do you want to download the
README.md
file for
bayesquad
?
Download (.md)
# Bayesian Quadrature This library provides code for evaluating the integral of non-negative functions using Bayesian Quadrature, both serially and in a batched mode. For some background on Bayesian quadrature, see: - [Introductory slides by David Duvenaud](https://www.cs.toronto.edu/~duvenaud/talks/intro_bq.pdf), or [these slides by Roman Garnett](http://probabilistic-numerics.org/assets/pdf/nips2015_probint/roman_talk.pdf) And for gory detail: - [Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature, Gunter et al. 2014](https://papers.nips.cc/paper/5483-sampling-for-inference-in-probabilistic-models-with-fast-bayesian-quadrature.pdf) for details on the warped Gaussian Process model implemented here ("WSABI") - [Batch Selection for Parallelisation of Bayesian Quadrature](https://arxiv.org/abs/1812.01553) for details on our batch selection process ## Installation Check out this repository and run `pip install .` in the root directory of the repository (i.e. in the directory containing setup.py). You should then be able to run the example scripts. ## Documentation Documentation is still a work in progress, but some docs are available at https://OxfordML.github.io/bayesquad