Elo-MMR VS stan

It is not. MMR is a system that is inherited from ELO in chess, you can read the full academic definition/breakdown on this paper https://github.com/EbTech/Elo-MMR/blob/master/paper/EloMMR.pdf if you are interested but in short is a series of Mathematical calculations and algorithms that create a number to represent players skill and use that number to match people of similar numbers. If there is a difference in the MMR between players then the underdog (the person with the lowest number) will increase their MMR by a large number since the system expected them to loose.

The author of said paper also published an implementation of it in Rust on his GitHub: https://github.com/EbTech/Elo-MMR

For multiplayer games the recent Elo-MMR is even better (faster, provable guarantees):

[1] https://github.com/EbTech/Elo-MMR

[2] https://arxiv.org/abs/2101.00400

Alternatives: stan and edward

remove.packages(c("StanHeaders", "rstan")) install.packages("rstan", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))

My approach to problems like this is to write down the proposed model mathematically first, in extreme detail. I find hierarchical form to be the easiest way to break it down piece by piece. Once I have the maths then I turn it into a Stan model. Last step is to use the Stan output to answer the research questions.

Here is a good breakdown of recommendations from Andrew Gelman.

For instance my first choice in these cases is always a Bayesian inference tool like Stan. In my experience as someone who’s more of a programmer than mathematician/statistician, Bayesian tools like this make it much easier to not accidentally fool yourself with assumptions, and they can be pretty good at catching statistical mistakes.

I tend to be most impressed by tools and libraries. The stuff that has most impressed me in my time in ML is stuff like pytorch and Stan, tools that allow expression of a wide variety of statistical (and ML, DL models, if you believe there's a distinction) models and inference from those models. These are the things that have had the largest effect in my own work, not in the sense of just using these tools, but learning from their design and emulating what makes them successful.

oh its certainly used in practice. you should look into frameworks like Stan[1] and pyro[2]. i think bayesian models are seen as more explainable so they will be used in industries that value that sort of thing

[1] https://mc-stan.org/