Gaussian process regression using Laplace approximations under localization uncertainty

Mahdi Jadaliha, Yunfei Xu, Jongeun Choi

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

6 Citations (Scopus)


In this paper, we formulate Gaussian process regression with observations under the localization uncertainty. In our formulation, effects of observations, measurement noise, localization uncertainty and prior distributions are all correctly incorporated in the posterior predictive statistics. The analytically intractable posterior predictive statistics are proposed to be approximated by Laplace approximations in different degrees of complexity. Such approximations have been carefully tailored to our problems and their approximation errors and complexity are analyzed. Simulation results demonstrate that the proposed approaches perform much better than approaches without considering the localization uncertainty correctly.

Original languageEnglish
Title of host publication2012 American Control Conference, ACC 2012
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781457710957
Publication statusPublished - 2012
Event2012 American Control Conference, ACC 2012 - Montreal, QC, Canada
Duration: 2012 Jun 272012 Jun 29

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2012 American Control Conference, ACC 2012
CityMontreal, QC

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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