Stats4Cosmology

A curated collection of Jupyter notebooks introducing and applying statistical methods to problems in cosmology.

The aim of this repository is to provide practical, hands-on examples for students and researchers who want to learn how to use statistics in the context of cosmological data analysis, model comparison, and forecasting.

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✨ Contents

• Probability & Statistics Refresher
  Basics of probability distributions, likelihoods, and Bayes' theorem.

• Bayesian Inference in Cosmology
  MCMC, nested sampling, and posterior exploration applied to toy cosmological models.

• Fisher Forecasting
  Deriving Fisher matrices and using them to forecast cosmological parameter constraints.

• Likelihoods for Cosmology
  Building Gaussian and non-Gaussian likelihoods, connecting with cosmological data vectors.

• Applications

  • Toy CMB & LSS parameter estimation
  • Combining multiple probes
  • Impact of priors and parameter degeneracies

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🚀 Getting Started

Fork this repository:

git clone https://github.com/your-username/Stats4Cosmology.git
cd Stats4Cosmology

Launch Jupyter Lab or Notebook:

jupyter lab

Open any notebook under the tutorials/ folder and start exploring.

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📚 Typical Dependencies

The notebooks rely on standard scientific Python libraries widely used in astronomy and cosmology:

• numpy – numerical computing and arrays
• scipy – scientific routines (optimization, integration, stats)
• astropy – astronomy & cosmology utilities (units, constants, cosmology tools)
• seaborn – statistical visualization
• matplotlib – plotting
• jupyter – interactive notebooks
• emcee or ultranest – Bayesian inference (optional, for MCMC examples)

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🧑‍🏫 Who Is This For?

• Students in astrophysics, physics, or statistics who want to learn about cosmological inference.
• Researchers looking for ready-to-use templates for teaching, lectures, or quick experimentation.
• Educators who want examples for cosmology and data science courses.

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👩‍🚀 Author

Developed and maintained by Guadalupe Cañas-Herrera, with inspiration from real-world cosmological pipelines in the era of Euclid, LSST, and CMB-S4.

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📄 License

This project is licensed under the MIT License.
See the LICENSE file for details.

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⭐ Citation

If you find these notebooks useful in your research or teaching, please cite:

@misc{stats4cosmology,
  author       = {Cañas Herrera, Guadalupe},
  title        = {Stats4Cosmology: Jupyter notebooks for statistical methods in cosmology},
  year         = {2025},
  howpublished = {\url{https://github.com/gcanasherrera/Stats4Cosmology}}
}