https://github.com/dmetivie/abcmethods.jl
A Julia package implementing some Bayesian ABC methods
Science Score: 36.0%
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Repository
A Julia package implementing some Bayesian ABC methods
Basic Info
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Metadata Files
README.md
ABCMethods
A Julia package featuring various ABC methods.
Methods
This package implements
Classic ABC: See the historical paper Pritchard et al. - 1999
ABC-SMC: A sequential Monte Carlo version of the classic ABC method Moral et al. - 2012. Note that this is the only method not using a reference table for training.
ABC-CNN: ABC method as described by Åkesson et al. - 2021.
ABC-Conformal: ABC method completely free of summary statistics and threshold selection as described in Baragatti et al. - 2024. It use Approximate Bayesian Computation (ABC) with deep learning and conformal prediction.
Example of usage
The documentation is not (yet) available however, detailed Quarto notebook on several examples using all implemented ABC methods are available.
- MA(2) example (2 parameters) -> Well known toy Bayesian example.
- Discrete Lotka-Volterra example (3 parameters) -> Very challenging for some extreme parameters.
- Phytoplankton dynamics in Lake with a toy model example (9 parameters) -> High dimensional example.
Installation
The package is currently NOT in the Julia general registry (will be soon), but on a local registry, hence to download it just add:
julia
using Pkg
pkg"registry add https://github.com/dmetivie/LocalRegistry"
and then add it as a normal package
```julia Pkg.add("ABCMethods")
or
pkg> add ABCMethods ```
Owner
- Name: David Métivier
- Login: dmetivie
- Kind: user
- Location: Montpellier, France
- Company: INRAe, MISTEA
- Website: http://www.cmap.polytechnique.fr/~david.metivier/
- Repositories: 5
- Profile: https://github.com/dmetivie
I am a research scientist with a physics background. Now, I do statistics to tackle environmental, and climate change problems. Julia enthusiast!
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