GPstuff - Gaussian process models for Bayesian analysis
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Updated
Dec 30, 2022 - MATLAB
GPstuff - Gaussian process models for Bayesian analysis
Bayesian Adaptive Direct Search (BADS) optimization algorithm for model fitting in MATLAB
Bayesian Optimization of Combinatorial Structures
This repository contains the source code for “Thompson sampling efficient multiobjective optimization” (TSEMO).
standard, parallel, constrained, and multiobjective EGO algorithms
Particle filter-based Gaussian process optimisation for parameter inference
Spearmint integrated Bayesian Optimization for hyper parameter tuning of Auto sparse encoder embedded with softmax Classifier for MNIST digit Classification.
Bayesian Statistics Guide
Implementation of a Bayesian approach to cryo-EM structure determination
Using Bayesian optimization to optimaze the network of CNN,which is used in fault diagnosis
A Matlab toolbox for Bayesian optimization.
Gaussian-Process Surrogate Optimisation
Python implementation of Bayesian optimization over permutation spaces.
Official implementation of TEVC'2024 paper "Hypervolume-Guided Decomposition for Parallel Expensive Multiobjective Optimization"
Surrogate-Assisted Tuning
Tutorial covering group DCM analyses of fMRI and M/EEG
Repository for my course projects in I.I.T Kanpur
Data for the Quantitative Single-Neuron Modeling Competition (2007).
Reservoir computing for short-and long-term prediction of chaotic systems, with tasks Lorenz and Mackey-Glass systems. Bayesian optimization (hyperparameter optimization algorithm) is used to tune the hyperparameters and improve the performance.
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