Adaptive Gaussian Copula ABC

Yanzhi Chen, Michael Gutmann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Approximate Bayesian computation (ABC) is a set of techniques for Bayesian inference when the likelihood is intractable but sampling from the model is possible. This work presents a simple yet effective ABC algorithm based on the combination of two classical ABC approaches — regression ABC and sequential ABC. The key idea is that rather than learning the posterior directly, we first target another auxiliary distribution that can be learned accurately by existing methods, through which we then subsequently learn the desired posterior with the help of a Gaussian copula. During this process, the complexity of the model changes adaptively according to the data at hand. Experiments on a synthetic dataset as well as three real-world inference tasks demonstrates that the proposed method is fast, accurate, and easy to use.
Original languageEnglish
Title of host publicationProceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019)
Place of PublicationNaha, Okinawa, Japan
Number of pages14
Publication statusPublished - 25 Apr 2019
Event22nd International Conference on Artificial Intelligence and Statistics - Naha, Japan
Duration: 16 Apr 201918 Apr 2019

Publication series

NameProceedings of Machine Learning Research
ISSN (Electronic)2640-3498


Conference22nd International Conference on Artificial Intelligence and Statistics
Abbreviated titleAISTATS 2019
Internet address


Dive into the research topics of 'Adaptive Gaussian Copula ABC'. Together they form a unique fingerprint.

Cite this