https://github.com/s-baumann/BayesianIntegral.jl

Bayesian Integration of functions
https://github.com/s-baumann/BayesianIntegral.jl

bayesian-statistics machine-learning-algorithms numerical-integration

Last synced: 3 months ago
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Bayesian Integration of functions

• Host: GitHub
• URL: https://github.com/s-baumann/BayesianIntegral.jl
• Owner: s-baumann
• License: mit
• Created: 2019-01-05T02:00:20.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2021-08-19T22:48:43.000Z (about 3 years ago)
• Last Synced: 2024-07-27T22:03:51.107Z (3 months ago)
• Topics: bayesian-statistics, machine-learning-algorithms, numerical-integration
• Language: Julia
• Homepage:
• Size: 157 KB
• Stars: 3
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: Readme.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-sciml - s-baumann/BayesianIntegral.jl: Bayesian Integration of functions

README

        
# BayesianIntegral

| Build | Coverage | Documentation |

|-------|----------|---------------|

| [![Build status](https://github.com/s-baumann/BayesianIntegral.jl/workflows/CI/badge.svg)](https://github.com/s-baumann/BayesianIntegral.jl/actions) | [![codecov](https://codecov.io/gh/s-baumann/BayesianIntegral.jl/branch/main/graph/badge.svg?token=sElLVJgRel)](https://codecov.io/gh/s-baumann/BayesianIntegral.jl) | [![docs-latest-img](https://img.shields.io/badge/docs-latest-blue.svg)](https://s-baumann.github.io/BayesianIntegral.jl/dev/index.html) |

This package uses the term Bayesian Integration to mean approximating a function with a kriging metamodel (aka a gaussian process model) and then integrating under it. A kriging metamodel has the nice feature that uncertainty about the nature of the function is explicitly modelled (unlike for instance a approximation with Chebyshev polynomials) and the Bayesian Integral uses this feature to give a Gaussian distribution representing the probabilities of various integral values. The output of the bayesian_integral_gaussian_exponential function is the expectation and variance of this distribution.