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What is probabilistic programming actually useful for? Interpretable inference or something like that? It seems like if raw prediction of a data distribution i
by whimsicalism 2y ago
What is probabilistic programming actually useful for? Interpretable inference or something like that?
It seems like if raw prediction of a data distribution is what you are interested in, explicitly specified statistical models are probably less useful? At least if you have lots of data and can tolerate a model with lots of 'variance'
- Karrot_Kream 2y agoYeah interpretability is the main reason that I know. If you need to make decisions based on statistics of the data, I think it's a lot easier with explicit statistical models.
- ivansavz 2y ago> What is probabilistic programming actually useful for? You can think of a probabilistic programming language as a set of building blocks for building statistical models. In the olden days, people used very simple frequentist models based on standard reference distributions like the normal, Student's t, chi2, etc. The models were simple because the computational capabilities were limited. In modern days, thanks to widespread compute and the inference algorithms, you can "fit" a much wider class of models, so researchers now tend to build bespoke models adapted for each particular application they are interested in. Probabilistic programming language are used to build those "custom" models.