Firstly, I appreciate all the thoughtful posts and replies on this forum. Though I have 5 years of industry experience as a Data Scientist for a market research firm, my exposure to Bayesian modeling is minimal in practice and even more minimal through undergrad and grad school, in short - I’m very much new to this…
The company I work for does a lot of Discrete Choice Modeling (as is typical in market research), and uses Sawtooth to fit these models. We run several Conjoint and MaxDiff (best-worst scaling) studies each year. It’s typical for us to report out the respondent-level utilities, or create simulators to estimate preference shares… again, very typical for MR.
I’m generally not involved in these studies but that is likely to change in the near-term. I have several reasons for wanting to ditch Sawtooth, but the main ones are:
- Sawtooth is expensive.
- I can easily run a model in Sawtooth w/out knowing what’s actually going on. I would like to force myself to learn more about these models by using something that takes more thought and effort.
- I am an R programmer and would love to not have to break my workflow by stepping outside of R.
So what am I asking?
- Are there any resources for people with no prior (pun intended) knowledge of Bayesian modeling, who don’t have PhD level mathematical chops, to gain a sufficient understanding of the subject in order to thoughtfully apply it in practice?
- Practically speaking, where should I start in terms of learning to use Stan/
brmsin place of Sawtooth for what they’d call “Hierarchical Bayes” for estimating utilities for Conjoint and MaxDiff studies? A lot of my confusion on this may stem from the wide variety of terminology that exists.
Thanks in advanced!