Output details
11 - Computer Science and Informatics
University of Edinburgh
Output title
A model-learner pattern for bayesian reasoning
Type
E - Conference contribution
Name of conference/published proceedings
Proceedings of the 40th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Volume number
-
Issue number
-
First page of article
403
ISSN of proceedings
-
Year of publication
2013
Number of additional authors
7
Additional information
<08> Originality: Identifies a probabilistic programming abstraction for a wide variety of Bayesian models in machine learning. It is standard that Bayesian reasoning is based on a prior and likelihood; the paper captures the abstraction in programming language terms for the first time.
Significance: The model-learner pattern allows the generic expression of tasks such as model testing, and generic compositions including mixture models, evidence-based model averaging, and mixtures of experts. This pattern from POPL'13 is the basis of a new programming language Tabular accepted for publication at POPL'14.
Rigour: Formal semantics in measure theory; three separate practical implementations.
Interdisciplinary
-
Cross-referral requested
-
Research group
F - Laboratory for Foundations of Computer Science
Citation count
1
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
