Book recommendations

Some good introductory books freely available on line

These books can help you revise some basic concepts in advance, to make the most out of our time together, or later on, as reference material.

• Bayes Rules!. An Introduction to Applied Bayesian Modeling. Alicia A. Johnson, Miles Q. Ott, Mine Dogucu. 2021.

• Modern Statistics for Modern Biology. Susan Holmes, Wolfgang Huber. 2019.

• Doing Bayesian data analysis. A Tutorial with R and BUGS. John K. Kruschke. 2018.

• An Introduction to Bayesian Thinking. A Companion to the Statistics with R Course. Merlise Clyde, Mine Çetinkaya-Rundel, Colin Rundel, David Banks, Christine Chai, Lizzy Huang. 2022.

• Probability and Bayesian Modeling. Jim Albert and Jingchen Hu. 2020.

If you need to refresh your R skills, here are a couple of good options:

• Modern Data Science with R. Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton. 2024

• Statistical Inference via Data Science. Chester Ismay and Albert Y. Kim. 2024.