# Statistics & Algorithms

A curated path through the statistical and computational machinery behind modern data
analysis — from linear algebra and probability through Bayesian inference, Monte Carlo
methods, Gaussian processes, and machine learning.

## Chapters

* **[Foundations](foundations.md)** — statistics, linear algebra, probability
  distributions, analytical methods, and Bayesian vs. frequentist statistics
* **[Model Fitting](model_fitting.md)** — fitting models to data and optimization
* **[Monte Carlo Methods](monte_carlo.md)** — MCMC, nested sampling, Hamiltonian Monte
  Carlo, convergence, and reporting results
* **[Visualization](visualization.md)** — plotting posteriors and choosing colors well
* **[Advanced Methods](advanced_methods.md)** — Gaussian process regression,
  differentiable programming, and hierarchical modeling
* **[Machine Learning](machine_learning.md)** — neural networks and deep learning
* **[Miscellaneous](miscellaneous.md)** — calculus, imaginary numbers, and other detours
* **[Resources](resources.md)** — courses, lecture series, and further reading