Information value in nonparametric Dirichlet-process Gaussian-process (DPGP) mixture models

This paper presents tractable information value functions for Dirichlet-process Gaussian-process (DPGP) mixture models obtained via collocation methods and Monte Carlo integration. Quantifying information value in tractable closed form is key to solving control and estimation problems for autonomous...

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Bibliographic Details
Published inAutomatica (Oxford) Vol. 74; pp. 360 - 368
Main Authors Wei, Hongchuan, Lu, Wenjie, Zhu, Pingping, Ferrari, Silvia, Liu, Miao, Klein, Robert H., Omidshafiei, Shayegan, How, Jonathan P.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.12.2016
Subjects
Bayesian nonparametric models
Dirichlet process
Gaussian process
Information gain
Information theory
Bayesian nonparametric models
Dirichlet process
Gaussian process
Information gain
Information theory
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Summary:This paper presents tractable information value functions for Dirichlet-process Gaussian-process (DPGP) mixture models obtained via collocation methods and Monte Carlo integration. Quantifying information value in tractable closed form is key to solving control and estimation problems for autonomous information-gathering systems. The properties of the proposed value functions are analyzed and then demonstrated by planning sensor measurements so as to minimize the uncertainty in DPGP target models that are learned incrementally over time. Simulation results show that sensor planning based on expected KL divergence outperforms algorithms based on mutual information, particle filters, and randomized methods.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2016.07.018