Name
Abstract | ||
|---|---|---|
This paper describes a new algorithm for exact Bayesian inference that is based on a recently proposed compositional semantics of Bayesian networks in terms of channels. The paper concentrates on the ideas behind this algorithm, involving a linearisation (`stretchingu0027) of the Bayesian network, followed by a combination of forward state transformation and backward predicate transformation, while evidence is accumulated along the way. The performance of a prototype implementation of the algorithm in Python is briefly compared to a standard implementation (pgmpy): first results show competitive performance. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Artificial Intelligence | Principle of compositionality,Bayesian inference,Inference,Computer science,Communication channel,Algorithm,Bayesian network,Predicate (grammar),Python (programming language) |
DocType | Volume | Citations |
Journal | abs/1804.08032 | 1 |
PageRank | References | Authors |
0.36 | 3 | 1 |