RAVEN is a flexible and multi-purpose probabilistic risk analysis, validation and uncertainty quantification, parameter optimization, model reduction and data knowledge-discovering framework.
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Updated
May 23, 2024 - C++
RAVEN is a flexible and multi-purpose probabilistic risk analysis, validation and uncertainty quantification, parameter optimization, model reduction and data knowledge-discovering framework.
Optimal delta hedging with SABR model
a modeling environment tailored to parameter estimation in dynamical systems
Simulating and Optimising Dynamical Models in Python 3
This R package allows calibration parameter estimation for inexact computer models with heteroscedastic errors proposed by Sung, Barber, and Walker (2022) in SIAM/ASA Journal on Uncertainty Quantification.
A Library for Uncertainty Quantification.
Calibration of the significant Social Force Parameters in Vissim
Codebase for "A Consistent and Differentiable Lp Canonical Calibration Error Estimator", published at NeurIPS 2022.
A phenology modelling framework in R
Parameter estimation and model calibration using Genetic Algorithm optimization in Python.
[MICCAI2022] Estimating Model Performance under Domain Shifts with Class-Specific Confidence Scores.
Parameter space reduction algorithm for search-based model calibration algorithms
[ICCV 2021 Oral] Deep Evidential Action Recognition
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".
Data for the Quantitative Single-Neuron Modeling Competition (2009).
[CVPR 2023] Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection
Data for the Quantitative Single-Neuron Modeling Competition (2007).
Official code for "On Calibrating Diffusion Probabilistic Models"
ARBO is a package for simulation and analysis of arbovirus nonlinear dynamics.
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