# Introductory books on statistics

## Paper text books

- Statistical Modeling and Machine Learning for Molecular Biology
- By Alan Moses

- Statistical Inference (2nd Edition)
- By Casella and Berger
- An introductory book to mathematical statistics: probability, distributions, MLE and significance testing

- Statistical Rethinking (2nd Edition)
- Written by a scientist this is a good Bayesian inference book for scientists (and perhaps not statisticians) with a little bit statistics and computing background
- Designed for sequential (study from the beginning to the end) not random (read about certain topics) access
- Youtube lectures available

- Bayesian Data analysis (3rd Edition)
- Classic Bayesian text book by Gelman et al.
- Chapters 1-3, 5. Bayesian statistics background
- Chapters 10-11. MCMC
- Chapter 14.15: Bayesian regression

- Machine Learning: a probabilistic perspective
- Chapter 4. Multivariate normal distributions
- Chapter 5. Bayesian model selection
- Chapters 7, 8, 9, 13: regression models
- Chapter 11. mixture model and EM
- Chapter 12. Latent linear models
- Chapter 17. HMM.
- Chapter 21. Variational inference
- Chapter 24: MCMC.
- Chapter 25. Clustering

- Data Analysis for the Life Sciences with R
- By Rafa Irizzary

- Likelihood, Bayesian and MCMC methods in quantitative genetics
- By Sorensen and Gianola
- This book has many examples illustrated in quantitative genetics

- Mathematics for Machine Learning
- Has some nice details on various topics, e.g., Bayesian linear regression.

- Graphical models by Michael Jordan
- Chapter 10 on mixture models.
- Chapter 11 on EM.

- High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications

## Online text books

- Foundations of Applied Statistics by John Storey.
- Covers many useful basic material with great examples, though some are incomplete as they currently stand

- Intro to machine learning
- By Rafa Irizzary
- Lots of basic R technical intro in it as well

- An Introduction to Bayesian Thinking
- This book has lots of good R examples
- Chapters 4 to 7 are helpful topics

- Interpretable Machine Learning
- Shapley Values also in Chapter 9 of the book above