martintrapp

3 ms·

1. 1.▲
   by martintrapp 6y ago

   AD does automatic differentiation, PLLs transform a generative model into some suitable form to perform automatic Bayesian inference, e.g. by using AD and black-box variational inference. Or said differently, in a PPL you specify the forwar
2. 2.▲
   by martintrapp 6y ago

   One of the arguments for python is the exceptional support of automation differentiation and GPU computing through deep learning libraries. Most python based PPLs focus on static model with differentiable log joints, allowing the applicatio
3. 3.▲
   by martintrapp 6y ago

   Thanks for mentioning Turing. In comparison to other PPLs in Julia, Turing is less a single probabilistic programming library and more a framework for probabilistic programming by providing a large collection of exchangeable libraries. Adva
4. 4.▲
   by martintrapp 6y ago

   I agree. Turing.jl, which is one of the major PPLs in Julia, is also based around effect handlers and does not relying on meta programming for the inference part. This allows the composition of inference algorithms and makes it more easy to
5. 5.▲
   by martintrapp 6y ago

   Thank you. In addition to the docs on turning.ml, you can find a few examples for the book “Statistical Rethinking” implemented using Turing in https://github.com/StatisticalRethinkingJulia/TuringModels.j... . We are cu