Contents Menu Expand Light mode Dark mode Auto light/dark, in light mode Auto light/dark, in dark mode Skip to content
probly 0.3.1 documentation
Light Logo Dark Logo

Content

  • Introduction
  • The probly Python Package
  • Core Concepts
  • Main Components
  • Advanced Topics
  • Examples and Tutorials
  • Implemented methods
  • Contributing Guide
  • References and Further Reading
  • FAQ and Troubleshooting

Notebooks

  • Notebook Examples
    • Utilities and Layers
      • Custom Loss Functions
      • Evaluation Metrics
      • Key Probabilistic Layers in probly
      • Utility Functions
    • Evaluation and Quantification
      • Credal Sets Visualization Plotting
      • A tutorial for clustervisualiser.py
      • Dirichlet distribution tutorial
      • Calibration Metrics
      • Interpretation techniques
      • Visualisation Tools
    • Batch Ensemble Networks
    • Bayesian Transformation
    • Dropconnect Transformation
    • Dropout Transformation
    • Ensemble vs SubEnsemble Notebook
    • Ensemble Transformation
    • Potential Advantages of Ensembling Random Forests
    • 2. Theoretical Background
    • 3. Data Generation
    • 4. Random Forest Ensemble Prototype
    • 5. Uncertainty Analysis
    • 6. Performance Metrics
    • 7. Conclusion
    • Evidential Classification Transformation
    • Evidential Regression Transformation
    • Out-of-Distribution Detection with an Ensemble
    • Calibration with Label Relaxation
    • Lazy Dispatch Test
    • Multilib Demo
    • A Brief Introduction to PyTraverse
    • Uncertainty Quantification using scikit-learn-Ensembles
    • Using probly with scikit-learn
    • Sub-Ensembles for Fast Uncertainty Estimation
    • Uncertainty for a Synthetic Regression Task using probly
    • Calibration using Temperature Scaling
    • Bayesian Neural Networks
    • Evidential Model for Classification
    • Evidential Regression Model
    • Transformation Comparison: Dropout vs DropConnect vs Ensemble vs Bayesian vs Evidential (PyTorch)
GitHub Logo
Back to top

Note

Go to the end to download the full example code.

Sphinx-Gallery smoke test.ΒΆ

This page exists mainly to verify that Sphinx-Gallery is correctly configured for the project.

These examples are executed during the documentation build. Keep them lightweight and avoid optional heavy ML dependencies unless you explicitly want the docs build to require them.

Sphinx-Gallery is running (probly 0.3.1)
from __future__ import annotations

import matplotlib.pyplot as plt

import probly

plt.figure(figsize=(4, 2))
plt.plot([0, 1, 2], [0, 1, 0])
plt.title(f"Sphinx-Gallery is running (probly {probly.__version__})")
plt.tight_layout()

Total running time of the script: (0 minutes 0.092 seconds)

Download Python source code: plot_gallery_smoke_test.py

Download zipped: plot_gallery_smoke_test.zip

Gallery generated by Sphinx-Gallery

Copyright © 2025, probly team
Made with Sphinx and @pradyunsg's Furo