There are 2 repositories under approximate-bayesian-computation topic.
Lectures on Bayesian statistics and information theory
Pure julia implementation of Multiple Affine Invariant Sampling for efficient Approximate Bayesian Computation
pyABC: distributed, likelihood-free inference
Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
A toolbox for C++ devs wanting to build geospatial population genetics simulators !
Simulator Expansion for Likelihood-Free Inference (SELFI): a python implementation
Approximate Bayesian Computation (ABC) with differential evolution (de) moves and model evidence (Z) estimates.
Adding Noise Noise Canceling Image resizing Resolution Study Filtering processes -Midic filter -Mean filter -Laplasian filter Photo Sharpening
Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
Likelihood-Free Inference for Julia.
Correlation functions versus field-level inference in cosmology: example with log-normal fields
Trabajo de Fin de Grado de Física 2022
Figuring out how Approximate Bayesian Computation works and how it can be applied to geological modeling.
Simulator of the Lotka-Volterra prey-predator system with demographic and observational noise and biases
Julia implementation of some ABC algorithms.
GPU and TPU implementation of parallelized ABC inference for a stochastic epidemiology model for COVID-19
Simple model class and non-vectorized samplers for Approximate Bayesian Statistics (ABC)
Publication Materials for "Extending Approximate Bayesian Computation with Supervised Machine Learning to Infer Demographic History from Genetic Polymorphisms Using DIYABC Random Forest" in *Molecular Ecology Resources* special issue
Bayesian inference tools. Including state-of-the-art inference methods: HMC family, ABC family, Data assimilation, and so on. Part of Mathepia.jl
Efficient Estimation of Generative Models using Tukey Depth
Repository on Approximate Bayesian Computation and the different distance metrics which can be implemented.
R-package protoABC: Flexible approach to Approximate Bayesian Computation (ABC)
Repo for projects in the Chalmers course "TIF345 / FYM345 Advanced simulation and machine learning" 2020. Authors: Sebastian Holmin and Erik Andersson
4th Year Project - Optimal Control of Directed Evolution
An R package to go along with my PhD research
Approximate Bayesian Computation algorithm based on simulated annealing
Comparison of summary statistic selection methods with a unifying perspective.
This repo contains code that implements vPET-ABC. Currently, we have included Python code attempting GPU acceleration on FDG compartment models.