Smartang3's starred repositories
Transitional_Ensemble_MCMC
Repository to tutorials on the implementation of the Transitional Ensemble Markov Chain Monte Carlo (TEMCMC) sampler for Bayesian Model Updating.
hamiltorch
PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks
Matlab-HMC
Matlab implementation of Hamiltonian Monte Carlo and Riemann Manifold Hamiltonian Monte Carlo
Bayesian-Modelling-in-Python
A python tutorial on bayesian modeling techniques (PyMC3)
hyperparameter-optimization
Implementation of Bayesian Hyperparameter Optimization of Machine Learning Algorithms
langevin-monte-carlo
A simple pytorch implementation of Langevin Monte Carlo algorithms.
Hybrid-GWOPSO-optimization
This script implements the hybrid of PSO and GWO optimization algorithm.
Hybrid-Algorithm-of-optimization
The hybrid algorithm of optimization
DEN-ARMOEA
# Introduction of DNN-AR-MOEA This repository contains code necessary to reproduce the experiments presented in Evolutionary Optimization of High-DimensionalMulti- and Many-Objective Expensive ProblemsAssisted by a Dropout Neural Network. Gaussian processes are widely used in surrogate-assisted evolutionary optimization of expensive problems. We propose a computationally efficient dropout neural network (EDN) to replace the Gaussian process and a new model management strategy to achieve a good balance between convergence and diversity for assisting evolutionary algorithms to solve high-dimensional multi- and many-objective expensive optimization problems. mainlydue to the ability to provide a confidence level of their outputs,making it possible to adopt principled surrogate managementmethods such as the acquisition function used in Bayesian opti-mization. Unfortunately, Gaussian processes become less practi-cal for high-dimensional multi- and many-objective optimizationas their computational complexity is cubic in the number oftraining samples. # References If you found DNN-AR-MOEA useful, we would be grateful if you cite the following reference: Evolutionary Optimization of High-DimensionalMulti- and Many-Objective Expensive ProblemsAssisted by a Dropout Neural Network (IEEE Transactions on Systems, Man and Cybernetics: Systems).
Langevin-dynamics
Sampling with gradient-based Markov Chain Monte Carlo approaches
OptAlgorithms
differential evolution (DE), particle swarm optimization (PSO), delayed rejection Markov Chain (MCMC), Metropolis adjusted differential evolution (MADE), Metropolis adjusted Langevin defined on the Riemann manifold (PRML)