Predictive approximate Bayesian computation via saddle points

Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He

Research output: Contribution to journalConference article

Abstract

Approximate Bayesian computation (ABC) is an important methodology for Bayesian inference when the likelihood function is intractable. Sampling-based ABC algorithms such as rejection- and K2-ABC are inefficient when the parameters have high dimensions, while the regression-based algorithms such as K- and DR-ABC are hard to scale. In this paper, we introduce an optimization-based ABC framework that addresses these deficiencies. Leveraging a generative model for posterior and joint distribution matching, we show that ABC can be framed as saddle point problems, whose objectives can be accessed directly with samples. We present the predictive ABC algorithm (P-ABC), and provide a probabilistically approximately correct (PAC) bound for its learning consistency. Numerical experiment shows that P-ABC outperforms both K2- and DR-ABC significantly.

Original languageEnglish (US)
Pages (from-to)10260-10270
Number of pages11
JournalAdvances in Neural Information Processing Systems
Volume2018-December
StatePublished - Jan 1 2018
Event32nd Conference on Neural Information Processing Systems, NeurIPS 2018 - Montreal, Canada
Duration: Dec 2 2018Dec 8 2018

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Fingerprint Dive into the research topics of 'Predictive approximate Bayesian computation via saddle points'. Together they form a unique fingerprint.

  • Cite this