Bayesian spatiotemporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields

@inproceedings{Western2019BayesianSI,
  title={Bayesian spatiotemporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields},
  author={Luke M. Western and Z. Sha and Matthew L. Rigby and Anita L. Ganesan and Alistair J. Manning and M. Kieran and Stanley and Simon J. O'Doherty and Dickon Young and Jonathan Rougier},
  year={2019}
}
Abstract. We present a method to infer spatially and spatiotemporally correlated emissions of greenhouse gases from atmospheric measurements and a chemical transport model. The method allows fast computation of spatial emissions using a hierarchical Bayesian framework as an alternative to Markov chain Monte Carlo algorithms. The spatial emissions follow a Gaussian process with a Matérn correlation structure which can be represented by a Gaussian Markov random field through a stochastic partial… 

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