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Bayesian methods are great if you want to squeeze all the information you get from each of your data points, and also injecting specific prior information to he
by j7ake 6y ago
Bayesian methods are great if you want to squeeze all the information you get from each of your data points, and also injecting specific prior information to help prevent over fitting.
These methods are ideal for small datasets with correlation structures that aren’t necessarily independent.
Also great if you want uncertainty with your estimates.
- standevbob 6y agoThis 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.