Bayesian Inference, Step 2: Running Bayesian Inference with Your Data

If you've had the opportunity to install R Studio and JAGS, and to download the associated programs needed to run Bayesian inference, it is a small step to actually getting your parameter estimates. Two of the programs from the BEST folder - BESTExample.R, and BEST1G.R - allow you to run independent-samples and one-sample t-tests, respectively. All that's required is changing the input in the "y1" and "y2" strings with your data, separate each observation with a comma, and then run the program. Other options can be changed, such as which value you are comparing against in the one-sample t-test, but everything else can essentially remain the same.

I realize it's been a while between posts, but right now I'm currently in the process of applying for jobs; this should start to pick up again in mid-October once the deadlines pass, but in the meantime, wish me luck!

Bayesian Inference, Step 1: Installing JAGS On Your Machine

Common complaint: "Bayesian analysis is too hard! Also, I have kidney stones."
Solution: Make Bayesian analysis accessible and efficient through freeware that anyone can use!

These days, advances in technology, computers, and lithotripsy have made Bayesian analysis easy to implement on any personal computer. All it requires is a couple of programs and a library of scripts to run the actual process of Bayesian inference; all that needs to be supplied by you, the user, is the data you have collected. Conceptually, this is no more difficult then entering in data into SAS or SPSS, and, I would argue, is easier in practice.

This can be done in R, statistical software that can interface with a variety of user-created packages. You can download one such package, JAGS, to do the MCMC sampling for building up distributions of parameter estimates, and then use those parameter estimates to brag to your friends about how you've "Gone Bayes."

All of the software and steps you need to install R, JAGS, and rjags (a program allowing JAGS to talk to R) can be found on John Kruschke's website here. Once you have that, it's simply a matter of entering in your own data, and letting the program do the nitty-gritty for you.