Multiresolution Gaussian processes

Published

Journal Article

We propose a multiresolution Gaussian process to capture long-range, non-Markovian dependencies while allowing for abrupt changes and non-stationarity. The multiresolution GP hierarchically couples a collection of smooth GPs, each defined over an element of a random nested partition. Long-range dependencies are captured by the top-level GP while the partition points define the abrupt changes. Due to the inherent conjugacy of the GPs, one can analytically marginalize the GPs and compute the marginal likelihood of the observations given the partition tree. This property allows for efficient inference of the partition itself, for which we employ graph-theoretic techniques. We apply the multiresolution GP to the analysis of magnetoencephalography (MEG) recordings of brain activity.

Duke Authors

Cited Authors

  • Fox, EB; Dunson, DB

Published Date

  • December 1, 2012

Published In

Volume / Issue

  • 1 /

Start / End Page

  • 737 - 745

International Standard Serial Number (ISSN)

  • 1049-5258

Citation Source

  • Scopus