# Posterior inference for sparse hierarchical non-stationary models

@article{MonterrubioGomez2020PosteriorIF, title={Posterior inference for sparse hierarchical non-stationary models}, author={Karla Monterrubio-G'omez and Lassi Roininen and Sara Wade and Theodoros Damoulas and Mark A. Girolami}, journal={Comput. Stat. Data Anal.}, year={2020}, volume={148}, pages={106954} }

Gaussian processes are valuable tools for non-parametric modelling, where typically an assumption of stationarity is employed. While removing this assumption can improve prediction, fitting such models is challenging. In this work, hierarchical models are constructed based on Gaussian Markov random fields with stochastic spatially varying parameters. Importantly, this allows for non-stationarity while also addressing the computational burden through a sparse banded representation of the…

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