Efficient methods for Gaussian Markov random fields under sparse linear constraints

David Bolin, Jonas Wallin

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceedingPeer review

Sammanfattning

Methods 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 beunfeasible. 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.
Originalspråkengelska
Titel på värdpublikationAdvances in Neural Information Processing Systems
Volym34
StatusPublished - 2021
Evenemang35th Conference on Neural Information Processing Systems (NeurIPS 2021) -
Varaktighet: 2021 dec. 62021 dec. 14

Konferens

Konferens35th Conference on Neural Information Processing Systems (NeurIPS 2021)
Period2021/12/062021/12/14

Ämnesklassifikation (UKÄ)

  • Sannolikhetsteori och statistik

Fingeravtryck

Utforska forskningsämnen för ”Efficient methods for Gaussian Markov random fields under sparse linear constraints”. Tillsammans bildar de ett unikt fingeravtryck.

Citera det här