This is the main reason that people use Stan---squeezing as much info out of your data as possible. That and the ability to write custom models for these situations.
There are hundreds of different applications of Stan across the physical, biologial, and social sciences, as well as in finance, education, sports analytics, actuarial sciences, transportation planning, all sorts of material and chemical and civil engineering, clinical trials and pharmacometrics, etc. etc. It's most popular in fields like ecology and epidemiology where Bayesian methods are already popular. For instance, many of the Covid models (like the one for NY state) are being built with Stan. All four baseball teams in the semifinals (LCS) use Stan for analytics, for example. Google and Facebook use Stan for ad attribution and resource allocation. It's been used for models of neutrino mass and models of galactic mass, models of supernovas, and it's even used in the LIGO gravitational wave experiments.
There are hundreds of different applications of Stan across the physical, biologial, and social sciences, as well as in finance, education, sports analytics, actuarial sciences, transportation planning, all sorts of material and chemical and civil engineering, clinical trials and pharmacometrics, etc. etc. It's most popular in fields like ecology and epidemiology where Bayesian methods are already popular. For instance, many of the Covid models (like the one for NY state) are being built with Stan. All four baseball teams in the semifinals (LCS) use Stan for analytics, for example. Google and Facebook use Stan for ad attribution and resource allocation. It's been used for models of neutrino mass and models of galactic mass, models of supernovas, and it's even used in the LIGO gravitational wave experiments.