stan VS pytensor

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/

I wouldn't overlook also how many goodies these existing PPLs are coming with. Starting from scratch means that one would have code up all the utilities to assess convergence, chain properties, etc. etc. on their own. And more advanced tricks like auto-differentiations that by themselves are huge perks are also unavailable (goodbye easy ADVI). For example PyMC, now uses Numba and JAX (via Aesara/PyTensor depending on your version), so theoretically (and practically) you can include arbitary PyTensor/JAX code in your model. I use PyMC as an example here, but this extends to most other PPLs too depending on their backends.