I'm working on probabilistic programming right now, and a lot of the papers I'm reading are from Microsoft Research. They have a cool Infer.NET project, and an ecosystem based on it is beginning to form. For example take a look at Tabular, which is an excel addon to do "Bayesian inference for the masses" based on Infer.NET: https://www.microsoft.com/en-us/research/project/tabular/
Overall though, by freeing developers from writing custom inference algorithms, all the work gets pushed to the language designer/implementer. It is not at all clear to me that one (or even a few) generic inference algorithms will be able to satisfy the needs for different problem domains. So there could be not one general purpose PPL but multiple ones with different problem domains.
Overall though, by freeing developers from writing custom inference algorithms, all the work gets pushed to the language designer/implementer. It is not at all clear to me that one (or even a few) generic inference algorithms will be able to satisfy the needs for different problem domains. So there could be not one general purpose PPL but multiple ones with different problem domains.