This is more of a phylosophical question that has puzzled me for a long time and I would like to ask for your opinion, and wheter Stan and bayesian inference could help.
In the molecular biology field is very common to find published (and highly cited) papers presenting models that relies on no data whatsoever. This is also due to the fact that a lot of biologial data is qualitative, and authos are reluctant to share their data.
Models parameters are obtained in one (or more) of the following ways:
resusing the estimation from previous papers and plugging them into the current model, even if formulas and causal connections are different (I suppose it accounts for some sort of “prior” in the bayesian framework, but never updated with real data)
making up some parameters values that somehow make things work as expected (again some sort of prior knowkledge)
adjusting parameters by trying several values along a range and check if the model is “robust” to these changes, meaning that outcomes do not vary too much (this is indeed very common but I can’t find a rationale for that).
Then they usually go on making prediction from these half-imaginary models and drawing conclusions from them, and that’s all.
Do you think that this makes any sense in the bayesian framework, or that I could harness the power of Stan or similar tools to improve the current (pathetic) situations?
As an example you can check this review (about auxin transport in the roots) http://dev.biologists.org/content/develop/140/11/2253.full.pdf?with-ds=yes. You will find many models like the one described, some of them are even Nature or Science publications.
thanks a lot