The official code repo for HyperAgent: A Simple, Scalable, Efficient and Provable Reinforcement Learning Framework for Complex Environments, ICML 2024.
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Updated
May 12, 2024 - Python
The official code repo for HyperAgent: A Simple, Scalable, Efficient and Provable Reinforcement Learning Framework for Complex Environments, ICML 2024.
Simulate N-player competitions via Approximate Bayesian Computation
This repo contains code that implements vPET-ABC. Currently, we have included Python code attempting GPU acceleration on FDG compartment models.
R-package protoABC: Flexible approach to Approximate Bayesian Computation (ABC)
Joint modelling of abundance and genetic diversity. An integrated model of population genetics and community ecology.
Approximate Bayesian Computation algorithm based on simulated annealing
Code for ABC-APTMC paper
Repo for projects in the Chalmers course "TIF345 / FYM345 Advanced simulation and machine learning" 2020. Authors: Sebastian Holmin and Erik Andersson
Repository on Approximate Bayesian Computation and the different distance metrics which can be implemented.
Comparison of summary statistic selection methods with a unifying perspective.
An R package to go along with my PhD research
4th Year Project - Optimal Control of Directed Evolution
Simple model class and non-vectorized samplers for Approximate Bayesian Statistics (ABC)
Simulation-based inference using SSNL
Bayesian inference tools. Including state-of-the-art inference methods: HMC family, ABC family, Data assimilation, and so on. Part of Mathepia.jl
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
Efficient Estimation of Generative Models using Tukey Depth
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
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