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.

Online text books