| 1. |
- Gordon, Andrew D., et al.
(författare)
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A Model-Learner Pattern for Bayesian Reasoning
- 2013
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Ingår i: Proceedings of the 40th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages. - New York, NY : Association for Computing Machinery (ACM). - 9781450318327 ; , s. 403-416
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Konferensbidrag (refereegranskat)abstract
- A Bayesian model is based on a pair of probability distributions, known as the prior and sampling distributions. A wide range of fundamental machine learning tasks, including regression, classification, clustering, and many others, can all be seen as Bayesian models. We propose a new probabilistic programming abstraction, a typed Bayesian model, which is based on a pair of probabilistic expressions for the prior and sampling distributions. A sampler for a model is an algorithm to compute synthetic data from its sampling distribution, while a learner for a model is an algorithm for probabilistic inference on the model. Models, samplers, and learners form a generic programming pattern for model-based inference. They support the uniform expression of common tasks including model testing, and generic compositions such as mixture models, evidence-based model averaging, and mixtures of experts. A formal semantics supports reasoning about model equivalence and implementation correctness. By developing a series of examples and three learner implementations based on exact inference, factor graphs, and Markov chain Monte Carlo, we demonstrate the broad applicability of this new programming pattern.
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| 3. |
- Gordon, Andrew D., et al.
(författare)
-
Tabular : a schema-driven probabilistic programming language
- 2014
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Ingår i: Proc. 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages. - New York : ACM Press. - 9781450325448 ; , s. 321-334
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Konferensbidrag (refereegranskat)abstract
- We propose a new kind of probabilistic programming language for machine learning. We write programs simply by annotating existing relational schemas with probabilistic model expressions. We describe a detailed design of our language, Tabular, complete with formal semantics and type system. A rich series of examples illustrates the expressiveness of Tabular. We report an implementation, and show evidence of the succinctness of our notation relative to current best practice. Finally, we describe and verify a transformation of Tabular schemas so as to predict missing values in a concrete database. The ability to query for missing values provides a uniform interface to a wide variety of tasks, including classification, clustering, recommendation, and ranking.
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