Bayesian Analysis in SAS


9/7/18

I do Bayesian analyses for fun/knowledge, if required to, or if I need very strong, subjective modeling assumptions, especially for problems that have small sample sizes. As an aside, I don't really consider these solutions to be addressing "probability", but rather something like "chance", "uncertainty", or "belief". There are some exceptions, for example if they use a lot of data, use few and/or weak assumptions, and have good frequentist properties.

Anyway, it has become apparent to me that if you do a general search for software to do Bayesian analyses, you are likely to come across names like Stan, R, OpenBUGS, WinBUGS, JAGS, JASP, and maybe a few others. However, there is one yuge, glaring omission from this list, and that is SAS. I believe SAS is typically left off this list because SAS may be perceived as being software for doing "frequentist" statistics.

SAS has a history of being that program you use for ANOVA, and not one that you would use for statistical programming, at least that is my perception based on graduate school and industry (~15 years) experience. On the contrary, SAS actually has fantastic capabilities to carry out just about any analysis, including thorough Bayesian analyses.

For starters, Bayesian can be done in the following five procedures (and possibly more I'm leaving out). Each of these procedures has a ton of options (way too many for me to list) to fully customize a Bayesian analysis.

There is also the multi-purpose

Another possible reason why those wanting Bayesian analysis gravitate towards the aforementioned Bayesian software, is that graphs are very useful for Bayesian analysis, and back in the day SAS was not very good at making graphs. In fact, they were downright ugly and took a lot of code to make. In graduate school, we essentially used SAS for ANOVA and R for graphs. Times have changed however, and now graphs in SAS are beautiful and much easier to implement.

Be sure to check out the non-SAS software for Bayesian analysis, but personally, I am not too infatuated with many of them from what I've seen. Being free or open source certainly has pros, but also has cons, and the cons are rarely mentioned in articles discussing them.

Some obvious cons are

SAS has a huge user base, tons of support, documentation, books, and experts, and has been around for at least twice as many decades as its nearest competitor. Changes might be slower to get implemented, but I have at least an order of magnitude more confidence in it. I strongly recommend SAS for your Bayesian, and frequentist, analyses.

Here are a few good resources on Bayesian analysis using SAS

Thank you for reading.


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