A Library for Uncertainty Quantification.
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Updated
Aug 9, 2024 - Python
A Library for Uncertainty Quantification.
RAVEN is a flexible and multi-purpose probabilistic risk analysis, validation and uncertainty quantification, parameter optimization, model reduction and data knowledge-discovering framework.
[ICCV 2021 Oral] Deep Evidential Action Recognition
a modeling environment tailored to parameter estimation in dynamical systems
Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.
A phenology modelling framework in R
[CVPR 2023] Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection
Official code for "On Calibrating Diffusion Probabilistic Models"
pycalibrate is a Python library to visually analyze model calibration in Jupyter Notebooks
Delta hedging under SABR model
Codebase for "A Consistent and Differentiable Lp Canonical Calibration Error Estimator", published at NeurIPS 2022.
[MICCAI2022] Estimating Model Performance under Domain Shifts with Class-Specific Confidence Scores.
This is the official PyTorch codebase for the ACL 2023 paper: "What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization".
Parameter estimation and model calibration using Genetic Algorithm optimization in Python.
Simulating and Optimising Dynamical Models in Python 3
System Dynamics Review (2021)
ARBO is a Matlab/C++ package for simulation and analysis of arbovirus nonlinear dynamics.
An efficient Java™ solver implementation for SBML
A collection of time-efficient state estimation algorithms for the medium-fidelity WindFarmSimulator (WFSim) control model
An overview about PROFOUND code, data, protocols and algorithms for interfacing, calibrating and comparing forest models
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