Re:Stan is a state-of-the-art platform for statistical modeling
by nightski on Wednesday December 23, 2020 @09:12AM
(#25517773)
While it is true that Bayesian inference is very powerful in that it allows one to introspect and view effects of the model's parameters on the outcome, it is equally as good at predicting the outcome variable as well. It just depends on what you want to get out of it. In fact you get more information about your outcome variable from Bayesian Inference as it is a distribution.
I'm not saying it is better than DL by any means, as DL can scale much better. Just that I don't think it's necessary to pigeonhole Bayesian inference to just predicting the parameters. In my opinion the "fundamental difference of focus" is just a personal decision, not something inherent to the method.
Re:Stan is a state-of-the-art platform for statistical modeling
by borroka on Wednesday December 23, 2020 @02:58PM
(#25521503)
The focus of statistical models (including Bayesian models) is on inference and uncertainty (both for parameter values and for predictions), the focus of ML models (including DL models) is on prediction and it is rarely possible to obtain any quantification of uncertainty.
Re:Stan is a state-of-the-art platform for statistical modeling
by jgalt212 on Wednesday December 23, 2020 @03:46PM
(#25522010)
> rarely possible to obtain any quantification of uncertainty.
Can't this be estimated via bootstrapping?
Deep Learning: Predict an outcome variable
For example, if I want to know what effect household income has on a student's chance of getting into college, Stan would allow you to estimate that given a proposed model.
If instead I wanted to predict a given student's chance of getting into college, I might use Machine Learning.
Of course, those two problems are linked, but it's a fundamental difference of focus.