Here are some papers, books, and other resources I have found helpful, as a researcher and a student. This is by no means an exhaustive bibliography, rather an (incomplete) list of personal favorites.

Conceptual articles

These articles discuss the roles of a model and various approaches to analyzing data. While they are conceptual and even have a philosophical flavor, they still address very concrete issues.

Tutorials and case studies

These are great places to get your hands dirty with modeling and coding:

Algorithms and numerical methods

Your code (be it for a specific model or a general purpose tool) is at its best when you understand the math, and more broadly the technical details. Sometimes, you need to break the black box open. Here are some helpful places to start at:


A classic for Bayesian analysis with a rather pragmatic discussion of the topic.

Here are some classics with a mathematical and somewhat theoretical treatment of statistics. Beware that it is not always obvious how to bridge theory and application!