Typesafety for Explicitly-Coded Probabilistic Inference Procedures
Name
MIT-CSAIL-TR-2017-014.pdf
Size
967.8 KB
Format
Adobe PDF
Checksum (MD5)
91d9b4494a891ef05dd720158fb6519a
Download all files submitted through automated deposit
Author(s)
Atkinson, Eric
Carbin, Michael
Advisor(s)
Michael Carbin
Date Issued
November 9, 2017
Series/Report no.
MIT-CSAIL-TR-2017-014
Abstract
Researchers have recently proposed several systems that ease the process of developing Bayesian probabilistic inference algorithms. These include systems for automatic inference algorithm synthesis as well as stronger abstractions for manual algorithm development. However, existing systems whose performance relies on the developer manually constructing a part of the inference algorithm have limited support for reasoning about the correctness of the resulting algorithm. In this paper, we present Shuffle, a programming language for developing manual inference algorithms that enforces 1) the basic rules of probability theory and 2) statistical dependencies of the algorithm's corresponding probabilistic model. We have used Shuffle to develop inference algorithms for several standard probabilistic models. Our results demonstrate that Shuffle enables a developer to deliver performant implementations of these algorithms with the added benefit of Shuffle's correctness guarantees.
Subjects
type safety
Persistent DSpace Link
