Editing Bayesian inference (section)

==Probabilistic programming==
{{main|Probabilistic programming}}

While conceptually simple, Bayesian methods can be mathematically and numerically challenging. Probabilistic programming languages (PPLs) implement functions to easily build Bayesian models together with efficient automatic inference methods. This helps separate the model building from the inference, allowing practitioners to focus on their specific problems and leaving PPLs to handle the computational details for them.<ref>Bessiere, P., Mazer, E., Ahuactzin, J. M., & Mekhnacha, K. (2013). Bayesian Programming (1 edition) Chapman and Hall/CRC.</ref><ref>{{cite journal|author=Daniel Roy|date=2015|title=Probabilistic Programming|website=probabilistic-programming.org|url=http://probabilistic-programming.org/wiki/Home|access-date=2020-01-02| archive-date=2016-01-10|archive-url=https://web.archive.org/web/20160110035042/http://probabilistic-programming.org/wiki/Home| url-status=dead}}</ref><ref>{{cite journal | last1 = Ghahramani | first1 = Z | year = 2015 | title = Probabilistic machine learning and artificial intelligence | url = https://www.repository.cam.ac.uk/handle/1810/248538| journal = Nature | volume = 521 | issue = 7553| pages = 452–459 | doi = 10.1038/nature14541 | pmid = 26017444 | bibcode = 2015Natur.521..452G | s2cid = 216356 }}</ref>