A model-learner pattern for bayesian reasoning
POPL '13 Proceedings of the 40th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
| Hi-index | 0.00 |
Probabilistic programming promises to make probabilistic modeling easier by making it possible to create models using the power of programming languages, and by applying general-purpose algorithms to reason about models. We present a new probabilistic programming language named Figaro that was designed with practicality and usability in mind. Figaro can represent models naturally that have been difficult to represent in other languages, such as probabilistic relational models and models with undirected relationships with arbitrary constraints. An important feature is that the Figaro language and reasoning algorithms are embedded as a library in Scala. We illustrate the use of Figaro through a case study.