scoring-rules

Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

Scoring rules like the Brier Score (Mean Squared Error, Quadratic Score) and Log Loss (Cross-Entropy, Negative Log-Likelihood, Logarithmic Score) can favor incorrect predictions. To address this limitation, the Probabilistic Brier Score (PBS) and Probabilistic Logarithmic Loss (PLL) have been introduced for probabilistic classifiers.

DiffeRential Evolution Adaptive Metropolis algorithm: MATLAB and Python Toolbox

Verification Tools for the Statistical Postprocessing of Ensemble Forecasts

Simulations of "Proper scoring rules for multivariate probabilistic forecasts based on aggregation and transformation", Pic et al. (2025)

Code for the paper: "Simulation-Based Inference with Generative Neural Networks via Scoring Rule Minimization"

Official code repository for "Distributional Autoencoders Know the Score", NeurIPS 2025

When Are Scoring Rules Proper? Bridging Theory and Practice in Survival Model Evaluation

Scoring Rules of Hydrograph Functionals

Calibration and cost-aware backtesting of prediction-market prices (Polymarket)

Investigation of inflation of Integrated Brier Score for validation of survival models

Calibrated Serie A match forecasting and match-leverage season simulation — a reproducible rebuild of a 2018 IJCA paper

Rust primitives for probabilistic forecasting and AI agents: Bayesian updates, calibration, scoring, decision theory, and auditable journals.

Reproduction of "Proper Scoring Rules for Survival Analysis" (Yanagisawa, ICML 2023): censored log, Brier, RPS and Portnoy losses for neural survival models on three clinical datasets (flchain, prostateSurvival, SUPPORT), with calibration analysis in PyTorch.

Frequency-report scoring rules for belief elicitation, with finite-sample belief and mean bounds from discrete reports.