7. Statistical Data Analysis
Full list of references in Chapter 7 of the IPython Cookbook, the definitive guide to high-performance scientific computing and data science in Python, by Dr. Cyrille Rossant, Packt Publishing, 400 pages, 2014.
- Python for Data Analysis by Wes McKinney
Frequentism and Bayesanism
- Statistical hypothesis testing.
- Credible interval.
- MAP estimation.
- Conjugate prior.
- Prior probability.
- Jeffreys prior.
Kernel Density Estimation
- PyMC model fitting.
- A great PyMC tutorial which we largely took inspiration from.
- A must-read free ebook on the subject, by Cameron Davidson-Pilon, entirely written in the IPython notebook!.
- The Monte-Carlo Markov Chain method.
- The Metropolis-Hastings algorithm.