TY - JOUR
T1 - Automated quantized inference for probabilistic programs with AQUA
AU - Huang, Zixin
AU - Dutta, Saikat
AU - Misailovic, Sasa
N1 - Funding Information:
This research was supported in part by NSF Grants No. CCF-1846354, CCF-1956374, CCF-2008883, and Facebook PhD Fellowship. The previous version of this work appeared in ATVA 2021 [].
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
PY - 2022/9
Y1 - 2022/9
N2 - We present AQUA, a new probabilistic inference algorithm that operates on probabilistic programs with continuous posterior distributions. AQUA approximates programs via an efficient quantization of the continuous distributions. It represents the distributions of random variables using quantized value intervals (Interval Cube) and corresponding probability densities (Density Cube). AQUA’s analysis transforms Interval and Density Cubes to compute the posterior distribution with bounded error. We also present an adaptive algorithm for selecting the size and the granularity of the Interval and Density Cubes. We evaluate AQUA on 24 programs from the literature. AQUA solved all of 24 benchmarks in less than 43s (median 1.35s) with a high level of accuracy. We show that AQUA is more accurate than state-of-the-art approximate algorithms (Stan’s NUTS and ADVI) and supports programs that are out of reach of exact inference tools, such as PSI and SPPL.
AB - We present AQUA, a new probabilistic inference algorithm that operates on probabilistic programs with continuous posterior distributions. AQUA approximates programs via an efficient quantization of the continuous distributions. It represents the distributions of random variables using quantized value intervals (Interval Cube) and corresponding probability densities (Density Cube). AQUA’s analysis transforms Interval and Density Cubes to compute the posterior distribution with bounded error. We also present an adaptive algorithm for selecting the size and the granularity of the Interval and Density Cubes. We evaluate AQUA on 24 programs from the literature. AQUA solved all of 24 benchmarks in less than 43s (median 1.35s) with a high level of accuracy. We show that AQUA is more accurate than state-of-the-art approximate algorithms (Stan’s NUTS and ADVI) and supports programs that are out of reach of exact inference tools, such as PSI and SPPL.
KW - Probabilistic inference
KW - Probabilistic programming
KW - Quantization
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U2 - 10.1007/s11334-021-00433-3
DO - 10.1007/s11334-021-00433-3
M3 - Article
AN - SCOPUS:85123235686
SN - 1614-5046
VL - 18
SP - 369
EP - 384
JO - Innovations in Systems and Software Engineering
JF - Innovations in Systems and Software Engineering
IS - 3
ER -