Integrated Nested Laplace Approximation (INLA)

We introduce a novel technique to model IFS datasets, which treats the observed galaxy properties as manifestations of an unobserved Gaussian Markov random field. The method is computationally efficient, resilient to the presence of low-signal-to-noise regions, and uses an alternative to Markov Chain Monte Carlo for fast Bayesian inference – the Integrated Nested Laplace Approximation. The proposed Bayesian approach enables the creation of synthetic images, recovery of areas with bad pixels, and an increased power to detect structures in datasets subject to substantial noise and/or sparsity of sampling.