Name
Abstract | ||
|---|---|---|
We introduce inference metaprogramming for probabilistic programming languages, including new language constructs, a formalism, and the rst demonstration of e ectiveness in practice. Instead of relying on rigid black-box inference algorithms hard-coded into the language implementation as in previous probabilistic programming languages, infer- ence metaprogramming enables developers to 1) dynamically decompose inference problems into subproblems, 2) apply in- ference tactics to subproblems, 3) alternate between incorpo- rating new data and performing inference over existing data, and 4) explore multiple execution traces of the probabilis- tic program at once. Implemented tactics include gradient- based optimization, Markov chain Monte Carlo, variational inference, and sequental Monte Carlo techniques. Inference metaprogramming enables the concise expression of proba- bilistic models and inference algorithms across diverse elds, such as computer vision, data science, and robotics, within a single probabilistic programming language.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3192366.3192409 | PLDI |
Keywords | Field | DocType |
Inference, Probabilistic Programming, Semantics | Metaprogramming,Monte Carlo method,Programming language,Markov chain Monte Carlo,Inference,Computer science,Theoretical computer science,Probabilistic programming language,Constructed language,Probabilistic logic,Semantics | Conference |
Volume | Issue | ISSN |
53 | 4 | 0362-1340 |
ISBN | Citations | PageRank |
978-1-4503-5698-5 | 2 | 0.38 |
References | Authors | |
17 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vikash K. Mansinghka | 1 | 452 | 34.78 |
| ulrich schaechtle | 2 | 4 | 1.11 |
| Shivam Handa | 3 | 2 | 1.06 |
| Alexey Radul | 4 | 35 | 8.90 |
| Yutian Chen | 5 | 680 | 36.28 |
| Martin C. Rinard | 6 | 4739 | 277.55 |