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Nonstandard interpretations of probabilistic programs for efficient inference (2011)
| Content Provider | CiteSeerX |
|---|---|
| Author | Wingate, David Goodman, Noah D. Stuhlmüller, Andreas Siskind, Jeffrey M. |
| Description | In Advances In Neural Information Processing Systems 23 Probabilistic programming languages allow modelers to specify a stochastic process using syntax that resembles modern programming languages. Because the program is in machine-readable format, a variety of techniques from compiler design and program analysis can be used to examine the structureofthedistribution represented by the probabilistic program. We show how nonstandard interpretations of probabilistic programs can be used to craft efficient inference algorithms: information about the structure of a distribution (such as gradients or dependencies) is generated as a monad-like side computation while executing the program. These interpretations can be easily coded using special-purpose objects and operator overloading. We implement two examples of nonstandard interpretations in two different languages, and use them as building blocks to construct inference algorithms: automatic differentiation, which enables gradient based methods, and provenance tracking, which enables efficient construction of global proposals. 1 |
| File Format | |
| Language | English |
| Publisher Date | 2011-01-01 |
| Access Restriction | Open |
| Subject Keyword | Compiler Design Automatic Differentiation Probabilistic Programming Language Probabilistic Program Nonstandard Interpretation Efficient Construction Inference Algorithm Modern Programming Language Provenance Tracking Efficient Inference Algorithm Monad-like Side Computation Operator Overloading Different Language Global Proposal Special-purpose Object Program Analysis Stochastic Process Efficient Inference Machine-readable Format Building Block |
| Content Type | Text |
| Resource Type | Article |
