Partial Evaluation in Junction Trees

Martin Roa-Villescas; Patrick W.A. Wijnings; Sander Stuijk; Henk Corporaal

doi:10.1109/DSD57027.2022.00064

Partial Evaluation in Junction Trees

Martin Roa-Villescas, Patrick W.A. Wijnings, Sander Stuijk, Henk Corporaal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

1 Downloads (Pure)

Abstract

One prominent method to perform inference on probabilistic graphical models is the probability propagation in trees of clusters (PPTC) algorithm. In this paper, we demonstrate the use of partial evaluation, an established technique from the compiler domain, to improve the performance of online Bayesian inference using the PPTC algorithm in the context of observed evidence. We present a metaprogramming-based method to transform a base program into an optimized version by precomputing the static input at compile time while guaranteeing behavioral equivalence. We achieve an inference time reduction of 21% on average for the Promedas benchmark.

Original language	English
Title of host publication	Proceedings : 2022 25th Euromicro Conference on Digital System Design, DSD 2022
Editors	Himar Fabelo, Samuel Ortega, Amund Skavhaug
Publisher	Institute of Electrical and Electronics Engineers
Pages	429-437
Number of pages	9
ISBN (Electronic)	978-1-6654-7404-7
DOIs	https://doi.org/10.1109/DSD57027.2022.00064
Publication status	Published - 4 Jan 2022
Event	25th Euromicro Conference on Digital System Design, DSD 2022 - Maspalomas, Spain Duration: 31 Aug 2022 → 2 Sept 2022

Conference

Conference	25th Euromicro Conference on Digital System Design, DSD 2022
Country/Territory	Spain
City	Maspalomas
Period	31/08/22 → 2/09/22

Bibliographical note

Funding Information:
This work is partially funded by the Netherlands Organization for Scientific Research.

Keywords

Bayesian inference
junction trees
message passing
partial evaluation
probabilistic graphical models

Access to Document

10.1109/DSD57027.2022.00064

Cite this

@inproceedings{dc1109c94c0d4b2ab76239665d346b12,

title = "Partial Evaluation in Junction Trees",

abstract = "One prominent method to perform inference on probabilistic graphical models is the probability propagation in trees of clusters (PPTC) algorithm. In this paper, we demonstrate the use of partial evaluation, an established technique from the compiler domain, to improve the performance of online Bayesian inference using the PPTC algorithm in the context of observed evidence. We present a metaprogramming-based method to transform a base program into an optimized version by precomputing the static input at compile time while guaranteeing behavioral equivalence. We achieve an inference time reduction of 21% on average for the Promedas benchmark.",

keywords = "Bayesian inference, junction trees, message passing, partial evaluation, probabilistic graphical models",

author = "Martin Roa-Villescas and Wijnings, {Patrick W.A.} and Sander Stuijk and Henk Corporaal",

note = "Funding Information: This work is partially funded by the Netherlands Organization for Scientific Research. ; 25th Euromicro Conference on Digital System Design, DSD 2022 ; Conference date: 31-08-2022 Through 02-09-2022",

year = "2022",

month = jan,

day = "4",

doi = "10.1109/DSD57027.2022.00064",

language = "English",

pages = "429--437",

editor = "Himar Fabelo and Samuel Ortega and Amund Skavhaug",

booktitle = "Proceedings : 2022 25th Euromicro Conference on Digital System Design, DSD 2022",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

}

Roa-Villescas, M , Wijnings, PWA , Stuijk, S & Corporaal, H 2022, Partial Evaluation in Junction Trees. in H Fabelo, S Ortega & A Skavhaug (eds), Proceedings : 2022 25th Euromicro Conference on Digital System Design, DSD 2022. Institute of Electrical and Electronics Engineers, pp. 429-437, 25th Euromicro Conference on Digital System Design, DSD 2022, Maspalomas, Spain, 31/08/22. https://doi.org/10.1109/DSD57027.2022.00064

Partial Evaluation in Junction Trees. / Roa-Villescas, Martin ; Wijnings, Patrick W.A.; Stuijk, Sander et al.
Proceedings : 2022 25th Euromicro Conference on Digital System Design, DSD 2022. ed. / Himar Fabelo; Samuel Ortega; Amund Skavhaug. Institute of Electrical and Electronics Engineers, 2022. p. 429-437.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Partial Evaluation in Junction Trees

AU - Roa-Villescas, Martin

AU - Wijnings, Patrick W.A.

AU - Stuijk, Sander

AU - Corporaal, Henk

N1 - Funding Information: This work is partially funded by the Netherlands Organization for Scientific Research.

PY - 2022/1/4

Y1 - 2022/1/4

N2 - One prominent method to perform inference on probabilistic graphical models is the probability propagation in trees of clusters (PPTC) algorithm. In this paper, we demonstrate the use of partial evaluation, an established technique from the compiler domain, to improve the performance of online Bayesian inference using the PPTC algorithm in the context of observed evidence. We present a metaprogramming-based method to transform a base program into an optimized version by precomputing the static input at compile time while guaranteeing behavioral equivalence. We achieve an inference time reduction of 21% on average for the Promedas benchmark.

AB - One prominent method to perform inference on probabilistic graphical models is the probability propagation in trees of clusters (PPTC) algorithm. In this paper, we demonstrate the use of partial evaluation, an established technique from the compiler domain, to improve the performance of online Bayesian inference using the PPTC algorithm in the context of observed evidence. We present a metaprogramming-based method to transform a base program into an optimized version by precomputing the static input at compile time while guaranteeing behavioral equivalence. We achieve an inference time reduction of 21% on average for the Promedas benchmark.

KW - Bayesian inference

KW - junction trees

KW - message passing

KW - partial evaluation

KW - probabilistic graphical models

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U2 - 10.1109/DSD57027.2022.00064

DO - 10.1109/DSD57027.2022.00064

M3 - Conference contribution

AN - SCOPUS:85146671671

SP - 429

EP - 437

BT - Proceedings : 2022 25th Euromicro Conference on Digital System Design, DSD 2022

A2 - Fabelo, Himar

A2 - Ortega, Samuel

A2 - Skavhaug, Amund

PB - Institute of Electrical and Electronics Engineers

T2 - 25th Euromicro Conference on Digital System Design, DSD 2022

Y2 - 31 August 2022 through 2 September 2022

ER -

Partial Evaluation in Junction Trees

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JunctionTrees: Bayesian inference in discrete graphical models

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Partial Evaluation in Junction Trees

Abstract

Conference

Bibliographical note

Keywords

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JunctionTrees: Bayesian inference in discrete graphical models

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