Statistics & Algorithms

Contents

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 — statistics, linear algebra, probability distributions, analytical methods, and Bayesian vs. frequentist statistics

  • Model Fitting — fitting models to data and optimization

  • Monte Carlo Methods — MCMC, nested sampling, Hamiltonian Monte Carlo, convergence, and reporting results

  • Visualization — plotting posteriors and choosing colors well

  • Advanced Methods — Gaussian process regression, differentiable programming, and hierarchical modeling

  • Machine Learning — neural networks and deep learning

  • Miscellaneous — calculus, imaginary numbers, and other detours

  • Resources — courses, lecture series, and further reading

Contents