University of Birmingham > Talks@bham > Data Science and Computational Statistics Seminar > Graphical approximations of Matern Gaussian fields: theory and applications

Graphical approximations of Matern Gaussian fields: theory and applications

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In this talk I will introduce graphical representations of stochastic partial differential equations as a way to approximate Matern Gaussian fields. Approximation error guarantees will be established building on and generalizing the theory of spectral convergence of graph Laplacians. Graphical representations allow inference and sampling with linear algebra methods for sparse matrices, thus reducing the computational cost of Gaussian field approaches. In addition, they bridge and unify several models in Bayesian inverse problems, spatial statistics and graph-based machine learning. We demonstrate through examples in these three disciplines that the unity revealed by graphical representations facilitates the exchange of ideas across them. This is joint work with Ruiyi Yang.

This talk is part of the Data Science and Computational Statistics Seminar series.

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