# Understanding Bayes

I have an ongoing series called Understanding Bayes, in which I explain essential Bayesian concepts in an easy to understand format. The only reason more researchers aren’t using Bayesian methods is because they don’t know what they are or how to use them. The math can look complicated, and the theorems can be intimidating, but each tutorial includes intuitive graphics that will help you understand the core ideas underlying each concept. R-code is also provided so that you can play around with, reproduce, and extend the demonstrations as you see fit.

Understanding these concepts is crucial for understanding what Bayesian inference is all about. There are many posts in the queue so stay tuned! If there is a concept not in the queue that you’d like for me to cover feel free to leave a comment below. 🙂 I have also added introductory papers that I have been a part of that attempt to introduce the topic. Plus, I’ve added a list of other Bayes bloggers who are writing excellent introductions. If you’d like to start using Bayesian methods see the tutorial videos I’ve made with the JASP development team.

Relevant Papers

1. Introduction to the concept of likelihood and its applications (link) [This paper draws from a number of Understanding Bayes posts]
2. Introduction to Bayesian inference for psychology (link)
3. How to become a Bayesian in eight easy steps: An annotated reading list (link)
4. Bayesian inference for psychology. Part II: Example applications with JASP (link)

Completed posts

1. A look at the likelihood (link)
2. Updating priors via the likelihood (link)
3. Visualization of the Bayes factor (link)
5. How to become a Bayesian in eight easy steps () (PDF of the paper)
6. What is the maximum Bayes factor for a given p value? (link)

Posts in the works

1. A recipe for Bayesian replication
2. Posterior probabilities vs. posterior odds
3. Visualization of Markov chain Monte Carlo
4. Predicting the future
5. Replication Bayes factors (related)
6. Objective vs. subjective Bayes
7. Prior probabilities for models vs. parameters
8. Strength of evidence vs. probability of obtaining that evidence (related)
10. When do Bayesians and frequentists agree and why?
11. Bayesian Model Averaging
12. Bayesian bias mitigation (related1, related2)
13. Bayesian updating over multiple studies
14. Does Bayes have error control? (related)