Smartang3

mfgsa

multifidelity global sensitivity analysis

SALib

Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.

uncertainpy

Uncertainpy: a Python toolbox for uncertainty quantification and sensitivity analysis, tailored towards computational neuroscience.

Affine-Invariant-Ensemble-Sampler-Tutorials

A repository of tutorials demonstrating the implementation of the Affine Invariant Ensemble Sampler.

EMCMC

Affine-invariance-TMCMC

Two stage Bayesian model updating

hamiltonian-monte-carlo

Implementation and description of the Hamiltonian Monte Carlo algorithm

HamiltonianMarkovChainMonteCarlo

Basic implementation of Hamiltonian Markov Chain Monte Carlo with numerical derivatives

Hamiltonian-Monte-Carlo

Implements Metropolis Hastings, Langevin Monte Carlo, and Hamiltonian Monte Carlo

deep-ensembles

PyTorch implementation of Lakshminarayanan et. al (2016) https://arxiv.org/abs/1612.01474

DeepEnsembles

Implementation of Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

PtychoPINN

fast, high-resolution lensless imaging in Tensorflow

pytorch-deep-ensembles

This repo contains a PyTorch implementation of the paper: "Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles"

Predictive-Uncertainty-Estimation-using-Deep-Ensemble

This repository is the code for Predictive Uncertainty Estimation using Deep Ensemble

uncertainty-deep-learning

Dropout_Tutorial_in_PyTorch

Dropout as Regularization and Bayesian Approximation

incertae

Code for MC Dropout and Model Ensembling Uncertainty Estimate experiments

Physics-Informed-Neural-Networks

Investigating PINNs

PhysNet_Thermal_Models

Physics informed neural networks for control-oriented building thermal models

Bayes-by-Backprop

TensorFlow implementation of Bayes-by-Backprop algorithm from "Weight Uncertainty in Neural Networks" paper

transitional-mcmc

This repo contains the code of Transitional Markov chain Monte Carlo algorithm

bayes-by-backprop

PyTorch implementation of "Weight Uncertainty in Neural Networks"

pytorch-bayes-by-backprop

PyTorch implementation of "Weight Uncertainties in Neural Networks" (Bayes-by-Backprop)

weight_uncertainty

Implementing Bayes by Backprop

NUTS-matlab

ERA-software

Software developed for ERA Group (TUM)

ML-SGHMC

Experiment code for Stochastic Gradient Hamiltonian Monte Carlo

Bayesian-Neural-Networks

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

bayesian-neural-network-pytorch

PyTorch implementation of bayesian neural network [torchbnn]

bnn

Bayesian neural network using Variational Inference, Monte Carlo Dropout, and Hamiltonian Monte Carlo.