>it's confusing to conflate probabilistic programming and libraries that support Bayesian inference
But it's a generic term so you could say the same about functional programming or logic programming, both of which can be done in Python even if there are more advanced or integrated systems elsewhere. I don't really think most people care, besides perhaps PL researchers, at which portion of the stack things are happening at or being optimised; if you are using the relevant mathematics and statistics that's what you are doing. I think people are playing semantics to say it only means one thing when it's obviously used in a general way and a sometimes in specific way.
The bottom line is the guy who wrote the book thinks it's probabilistic programming, ogrisel does, I do, and the people who run http://probabilistic-programming.org/wiki/Home seem to be referring to it as probabilistic programming as well. I don't buy Ian's argument that it's part of some latter type of category on the site, PyMC is directly linked in a section titled "Existing probabilistic programming systems". They use "as well as" to link the two groups so either the first is "systems" and the rest are still "probabilistic programming" just without "systems" or they are all "probabilistic programming systems" if "as well as" is operating in that way. The arguments against this seem to be splitting hairs and playing semantics far too much when n-grams regularly have more than one meaning. Indeed it's amusing to see probabilistic people arguing for one interpretation rather than saying that there could be more than one and it depends on context (an NLP program trying to disambiguate the meaning of a given n-gram would look at other words present, topic models for the document, et cetera).
But it's a generic term so you could say the same about functional programming or logic programming, both of which can be done in Python even if there are more advanced or integrated systems elsewhere. I don't really think most people care, besides perhaps PL researchers, at which portion of the stack things are happening at or being optimised; if you are using the relevant mathematics and statistics that's what you are doing. I think people are playing semantics to say it only means one thing when it's obviously used in a general way and a sometimes in specific way.
The bottom line is the guy who wrote the book thinks it's probabilistic programming, ogrisel does, I do, and the people who run http://probabilistic-programming.org/wiki/Home seem to be referring to it as probabilistic programming as well. I don't buy Ian's argument that it's part of some latter type of category on the site, PyMC is directly linked in a section titled "Existing probabilistic programming systems". They use "as well as" to link the two groups so either the first is "systems" and the rest are still "probabilistic programming" just without "systems" or they are all "probabilistic programming systems" if "as well as" is operating in that way. The arguments against this seem to be splitting hairs and playing semantics far too much when n-grams regularly have more than one meaning. Indeed it's amusing to see probabilistic people arguing for one interpretation rather than saying that there could be more than one and it depends on context (an NLP program trying to disambiguate the meaning of a given n-gram would look at other words present, topic models for the document, et cetera).