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dc.contributor.authorBolin, David
dc.contributor.authorWallin, Jonas
dc.date.accessioned2022-06-19T11:56:53Z
dc.date.available2021-06-07T06:30:44Z
dc.date.available2022-06-19T11:56:53Z
dc.date.issued2021-01-01
dc.identifier.isbn9781713845393
dc.identifier.issn1049-5258
dc.identifier.urihttp://hdl.handle.net/10754/669418
dc.description.abstractMethods for inference and simulation of linearly constrained Gaussian Markov Random Fields (GMRF) are computationally prohibitive when the number of constraints is large. In some cases, such as for intrinsic GMRFs, they may even be unfeasible. We propose a new class of methods to overcome these challenges in the common case of sparse constraints, where one has a large number of constraints and each only involves a few elements. Our methods rely on a basis transformation into blocks of constrained versus non-constrained subspaces, and we show that the methods greatly outperform existing alternatives in terms of computational cost. By combining the proposed methods with the stochastic partial differential equation approach for Gaussian random fields, we also show how to formulate Gaussian process regression with linear constraints in a GMRF setting to reduce computational cost. This is illustrated in two applications with simulated data.
dc.publisherNeural information processing systems foundation
dc.relation.urlhttps://arxiv.org/pdf/2106.01712.pdf
dc.rightsArchived with thanks to Neural information processing systems foundation
dc.titleEfficient methods for Gaussian Markov random fields under sparse linear constraints
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.conference.date2021-12-06 to 2021-12-14
dc.conference.name35th Conference on Neural Information Processing Systems, NeurIPS 2021
dc.conference.locationVirtual, Online
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Statistics, Lund University, Sweden
dc.identifier.volume12
dc.identifier.pages9882-9894
dc.identifier.arxivid2106.01712
kaust.personBolin, David
dc.identifier.eid2-s2.0-85131800800
refterms.dateFOA2021-06-07T06:31:08Z


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