Abstract
Inference algorithms for probabilistic programming are complex imperative programs with many moving parts. Efficient inference often requires customising an algorithm to a particular probabilistic model or problem, sometimes called inference programming. Most inference frameworks are implemented in languages that lack a disciplined approach to side effects, which can result in monolithic implementations where the structure of the algorithms is obscured and inference programming is hard. Functional programming with typed effects offers a more structured and modular foundation for programmable inference, with monad transformers being the primary structuring mechanism explored to date.
This paper presents an alternative approach to inference programming based on algebraic effects. Using effect signatures to specify the key operations of the algorithms, and effect handlers to modularly interpret those operations for specific variants, we develop two abstract algorithms, or inference patterns, representing two important classes of inference: Metropolis-Hastings and particle filtering. We show how our approach reveals the algorithms’ high-level structure, and makes it easy to tailor and recombine their parts into new variants. We implement the two inference patterns as a Haskell library, and discuss the pros and cons of algebraic effects vis-à-vis monad transformers as a structuring mechanism for modular imperative algorithm design.
This paper presents an alternative approach to inference programming based on algebraic effects. Using effect signatures to specify the key operations of the algorithms, and effect handlers to modularly interpret those operations for specific variants, we develop two abstract algorithms, or inference patterns, representing two important classes of inference: Metropolis-Hastings and particle filtering. We show how our approach reveals the algorithms’ high-level structure, and makes it easy to tailor and recombine their parts into new variants. We implement the two inference patterns as a Haskell library, and discuss the pros and cons of algebraic effects vis-à-vis monad transformers as a structuring mechanism for modular imperative algorithm design.
Original language | English |
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Title of host publication | Haskell 2023 |
Subtitle of host publication | Proceedings of the 16th ACM SIGPLAN International Haskell Symposium |
Editors | Trevor L. McDonell, Niki Vazou |
Publisher | Association for Computing Machinery (ACM) |
Pages | 44–58 |
Number of pages | 15 |
ISBN (Electronic) | 9798400702983 |
DOIs | |
Publication status | Published - 31 Aug 2023 |
Event | ACM SIGPLAN International Conference on Functional Programming - Italy, Milan, Italy Duration: 8 Sept 2023 → 9 Sept 2023 https://dl.acm.org/conference/icfp |
Publication series
Name | Proceedings of the ACM SIGPLAN ... Haskell Symposium |
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ISSN (Print) | 2152-9213 |
Conference
Conference | ACM SIGPLAN International Conference on Functional Programming |
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Country/Territory | Italy |
City | Milan |
Period | 8/09/23 → 9/09/23 |
Internet address |
Research Groups and Themes
- Programming Languages
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Dive into the research topics of 'Effect Handlers for Programmable Inference'. Together they form a unique fingerprint.Student theses
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Effects and effect handlers for probabilistic programming
Nguyen, M. H. (Author), Wang, M. (Supervisor) & Perera, R. (Supervisor), 5 Dec 2023Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)
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