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uncertainty-toolbox / uncertainty-toolbox

A python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

visualization metrics scoring-rules toolbox uncertainty calibration visualizations uncertainty-quantification uncertainty-estimation bayesian-neural-networks bayesian-deep-learning sharpness predictive-uncertainty recalibration uncertainty-calibration uncertainty-metrics uncertainty-toolbox

Updated Jan 17, 2022
Python

SALib / SALib

Star

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

python numpy uncertainty uncertainty-quantification sensitivity-analysis morris sensitivity-analysis-library sobol global-sensitivity-analysis salib joss

Updated Apr 1, 2022
Python

ahmedmalaa / deep-learning-uncertainty

Star

Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertainty estimation in deep learning models.

deep-neural-networks deep-learning uncertainty-quantification uncertainty-estimation prediction-uncertainty

Updated Sep 3, 2021
Python

EmuKit / emukit

Star

A Python-based toolbox of various methods in uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.

python machine-learning emulation decision-making uncertainty-quantification sensitivity-analysis bayesian-optimization experimental-design bayesian-quadrature multi-fidelity

Updated Apr 8, 2022
Python

jonathf / chaospy

Star

Chaospy - Toolbox for performing uncertainty quantification.

uncertainty-quantification sensitivity-analysis sparse-grids variance-reduction gaussian-quadrature

Updated Apr 2, 2022
Python

valeman / awesome-conformal-prediction

Star

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD theses, articles and open-source libraries.

python machine-learning awesome r probability uncertainty datascience probability-distribution awesome-list machinelearning deeplearning probability-distributions uncertainty-quantification uncertainty-estimation conformal-prediction

Updated Apr 3, 2022

simetenn / uncertainpy

Star

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

python neuroscience uncertainty-quantification sensitivity-analysis computational-modeling

Updated Jun 18, 2021
Python

idaholab / raven

Star

Open

[DEFECT] Running Tests Creates Too Many Files

dylanjm commented Sep 1, 2021

Defect Description

Describe the defect

When a user runs:

./run_tests -j 12

It creates a minimum number of 110 files and directories that Git shows as untracked files.

What did you expect to see happen?

Running the tests should not change the state of the repository.

What did you see instead?

I see that a bunch of files an

GridEntity needs to use the InputParameters structure

BayesWatch / deep-kernel-transfer

Star

Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)

kernel deep-learning paper regression uncertainty bayesian-methods classification kernel-methods one-shot-learning uncertainty-quantification gaussian-processes kernels meta-learning shot-learning deep-kernel-learning few-shot-learning gpytorch

Updated Jan 19, 2022
Python

IBM / UQ360

Star

Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty in machine learning model predictions.

machine-learning metrics artificial-intelligence uncertainty calibration uncertainty-quantification trusted-ai bayesianneuralnetwork uncertainty-estimates

Updated Nov 8, 2021
Python

openturns / openturns

Star

Uncertainty treatment library

python optimization reliability metamodel probability-distribution data-analysis stochastic-process sensitivity uncertainty-quantification

Updated Apr 9, 2022
C++

SURGroup / UQpy

Star

UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.

monte-carlo probability stochastic monte-carlo-simulation stochastic-process uncertainty-quantification probabilistic uncertainty-propagation latin-hypercube uncertainty-sampling latin-hypercube-sampling

Updated Apr 9, 2022
Jupyter Notebook

IntelLabs / bayesian-torch

Star

A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch

deep-neural-networks deep-learning pytorch uncertainty-neural-networks bayesian-inference uncertainty-quantification uncertainty-estimation bayesian-neural-networks bayesian-deep-learning stochastic-variational-inference bayesian-layers

Updated Mar 3, 2022
Python

ykwon0407 / UQ_BNN

Star

Uncertainty quantification using Bayesian neural networks in classification (MIDL 2018, CSDA)

uncertainty-quantification bayesian-neural-networks

Updated Jul 30, 2019
Jupyter Notebook

madsjulia / Mads.jl

Star

MADS: Model Analysis & Decision Support

machine-learning decision-making inversion matrix-factorization calibration data-analytics high-performance-computing parameter-estimation uncertainty-quantification sensitivity-analysis decision-support model-reduction model-analysis blind-source-separation

Updated Apr 7, 2022
HTML

cics-nd / cnn-surrogate

Star

Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

uncertainty-quantification encoder-decoder bayesian-surrogate image-regression stein-variational-gradient-decent

Updated Aug 4, 2020
Python

usgs / pestpp

Star

tools for scalable and non-intrusive parameter estimation, uncertainty analysis and sensitivity analysis

optimization parallel-computing parameter-estimation optimization-tools ensembles uncertainty-quantification sensitivity-analysis global-sensitivity-analysis ensemble-methods non-intrusive

Updated Mar 30, 2022
C++

Lingkai-Kong / SDE-Net

Star

Code for paper: SDE-Net: Equipping Deep Neural network with Uncertainty Estimates

deep-learning uncertainty-quantification robustness open-world-classification

Updated Oct 22, 2020
Python

timueh / PolyChaos.jl

Star

Open

Include jldoctests in documentation

timueh commented Mar 15, 2019

jldoctests are tested when building the documentation with Documenter.jl. This should be favored to @example or its friends.

Plotting using Plot.jl and recipes

mbhenaff / EEN

Star

EEN: Error Encoding Network

deep-neural-networks deep-learning convolutional-neural-networks uncertainty-quantification video-prediction-models

Updated Dec 3, 2017
Python

UCL-CCS / EasyVVUQ

Star

Python 3 framework to facilitate verification, validation and uncertainty quantification (VVUQ) for a wide variety of simulations.

python simulation verification uncertainty-quantification hpc-applications vvuq

Updated Mar 23, 2022
Jupyter Notebook

nikitadurasov / masksembles

Star

Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR2021).

computer-vision deep-learning tensorflow paper torch uncertainty-neural-networks uncertainty-quantification uncertainty-estimation out-of-distribution-detection

Updated Jun 22, 2021
Python

cambridge-mlg / DUN

Star

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

machine-learning deep-learning reproducible-research pytorch uncertainty neural-networks expectation-maximization uncertainty-neural-networks bayesian-inference uncertainty-quantification variational-inference bayesian-neural-networks robustness neural-architecture-search bayesian-deep-learning probabilistic-inference reproducible-paper probabilistic-neural-network learnt-depth network-depths

Updated Mar 12, 2022
Jupyter Notebook

ORNL / TASMANIAN

Star

The Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN

uncertainty-quantification inverse-problems tasmanian-sparse-grids reduced-order-models high-order-approximation-models

Updated Apr 9, 2022
C++

Cogito2012 / DEAR

Star

[ICCV 2021 Oral] Deep Evidential Action Recognition

model-calibration uncertainty-quantification action-recognition video-understanding debiasing evidential-deep-learning ood-detection openset-recognition

Updated Jan 22, 2022
Python

SciML / DiffEqUncertainty.jl

Sponsor

Star

Uncertainty quantification for scientific machine learning (SciML) and differential equations

differential-equations differentialequations uncertainty-quantification uq scientific-machine-learning sciml

Updated Jan 21, 2022
Julia

mingzhang-yin / SIVI

Star

Using neural network to build expressive hierarchical distribution; A variational method to accurately estimate posterior uncertainty; A fast and general method for Bayesian inference. (ICML 2018)

mcmc uncertainty-quantification variational-inference variational-autoencoder implicit-modeling implicit-flow approximate-bayesian-inference

Updated Aug 26, 2020
MATLAB

pygpc-polynomial-chaos / pygpc

Star

A Sensitivity and uncertainty analysis toolbox for Python based on the generalized polynomial chaos method

dynamical-systems uncertainty-quantification uncertainty-estimation uncertainty-propagation gpc polynomial-chaos-expansion

Updated Mar 8, 2022
Jupyter Notebook

sandialabs / UQTk

Star

Sandia Uncertainty Quantification Toolkit

uncertainty bayesian-inference mcmc uncertainty-quantification uq uncertainty-estimation uncertainty-propagation mcmc-sampler mcmc-methods snl-science-libs snl-data-analysis

Updated Mar 22, 2022
Fortran

cselab / korali

Star

High-performance framework for uncertainty quantification, optimization and reinforcement learning.

python reinforcement-learning cplusplus optimization-methods uncertainty-quantification

Updated Apr 8, 2022
C++

uncertainty-quantification

Here are 196 public repositories matching this topic...

uncertainty-toolbox / uncertainty-toolbox

SALib / SALib

ahmedmalaa / deep-learning-uncertainty

EmuKit / emukit

jonathf / chaospy

valeman / awesome-conformal-prediction

simetenn / uncertainpy

idaholab / raven

[DEFECT] Running Tests Creates Too Many Files

Defect Description

What did you expect to see happen?

What did you see instead?

GridEntity needs to use the InputParameters structure

BayesWatch / deep-kernel-transfer

IBM / UQ360

openturns / openturns

SURGroup / UQpy

IntelLabs / bayesian-torch

ykwon0407 / UQ_BNN

madsjulia / Mads.jl

cics-nd / cnn-surrogate

usgs / pestpp

Lingkai-Kong / SDE-Net

timueh / PolyChaos.jl

Include jldoctests in documentation

Plotting using Plot.jl and recipes

mbhenaff / EEN

UCL-CCS / EasyVVUQ

nikitadurasov / masksembles

cambridge-mlg / DUN

ORNL / TASMANIAN

Cogito2012 / DEAR

SciML / DiffEqUncertainty.jl

mingzhang-yin / SIVI

pygpc-polynomial-chaos / pygpc

sandialabs / UQTk

cselab / korali

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