Simulate N-player competitions via Approximate Bayesian Computation
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Updated
Nov 24, 2017 - R
Simulate N-player competitions via Approximate Bayesian Computation
Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
pyABC: distributed, likelihood-free inference
Julia implementation of some ABC algorithms.
Figuring out how Approximate Bayesian Computation works and how it can be applied to geological modeling.
Joint modelling of abundance and genetic diversity. An integrated model of population genetics and community ecology.
Simple model class and non-vectorized samplers for Approximate Bayesian Statistics (ABC)
Adding Noise Noise Canceling Image resizing Resolution Study Filtering processes -Midic filter -Mean filter -Laplasian filter Photo Sharpening
A Python package for likelihood-free inference (LFI) methods such as Approximate Bayesian Computation (ABC)
GPU and TPU implementation of parallelized ABC inference for a stochastic epidemiology model for COVID-19
Code for ABC-APTMC paper
Lectures on Bayesian statistics and information theory
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
Repo for projects in the Chalmers course "TIF345 / FYM345 Advanced simulation and machine learning" 2020. Authors: Sebastian Holmin and Erik Andersson
User interface to DIYABC/AbcRanger
Likelihood-Free Inference for Julia.
R-package protoABC: Flexible approach to Approximate Bayesian Computation (ABC)
Bayesian inference tools. Including state-of-the-art inference methods: HMC family, ABC family, Data assimilation, and so on. Part of Mathepia.jl
Correlation functions versus field-level inference in cosmology: example with log-normal fields
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