Below you can find the notes for this course.
The material is constantly under development and constructive feedback is always welcome.
The course material draws from a variety of sources, beyond the documents that will be shared during the course. Here are some books references that will help you deepen your understanding of the topics we'll cover:
Many of the concepts and examples in this course are derived from Practical Bayesian Inference by Coryn Bailer-Jones, which is available online at UB Heidelberg.
If you’re interested in exploring the theory in greater detail, I recommend Data Analysis: A Bayesian Tutorial by Devinderjit Sivia and John Skilling. You can also find this in the library.
Another useful book is A Practical Guide to Data Analysis for Physical Science Students by Louis Lyons. This short book covers basic theory and has simple examples. It is not intended as a general reference book on statistics, but it is a good starting point for undergraduates who are carrying out lab experiments for the first time.
For those interested in statistics with R, I suggest Statistics and Data with R by Yosef Cohen and Jeremiah Cohen.
Numerical Recipes: The Art of Scientific Computing is an excellent source for optimal algorithms and a highly recommended resource.
For those that enjoy comics, The Cartoon Guide to Statistics by Larry Gonick and Woollcott Smith is good fun and explains some key concepts in an entertaining way.
On the more popular-science level, I can recommend these books: