Building inference algorithms from monad transformers
We show how to decompose popular inference algorithms into a set of simple, reusable building blocks corresponding to monad transformers. We define a collection of such building blocks and implement them in Haskell producing a library for constructing inference algorithms in a modular fashion. We are also working towards formalizing those concepts as monadic denotational semantics for inference algorithms.
Adam Scibior, Yufei Cai, Klaus Ostermann, Zoubin Ghahramani
Welcome to PPS, workshop on probabilistic programming semantics, on Tuesday, 17 January 2017, colocated right before POPL. This informal workshop aims to bring programming-language and machine-learning researchers together to advance the semantic foundations of probabilistic programming.
We are delighted that Gordon Plotkin has accepted our invitation to give a talk “Towards a metric semantics for probabilistic programming“. In addition, as listed on the posted schedule, we have accepted 21 extended abstracts submitted by a wide range of contributors. We accepted 10 submissions as posters and 11 as talks, not on the basis of reviewer scores but based on which medium we thought would be most effective in conveying the material. So, some highly ranked submissions that are more technical in nature are accepted as posters.
To foster collaboration and establish common ground, we ask all accepted contributors to post their revised extended abstracts on this site, along with any other materials such as preprints they want to share.
Everyone is welcome to post comments, questions, and other discussion on the posts. Because probabilistic programming is a research area that bridges multiple communities with different vocabularies, comments of the flavor “I don’t understand what you mean by X” are particularly valuable!