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Integrated Nested Laplace Approximation (INLA)

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Integrated nested Laplace approximation (INLA) is an algorithm for approximate Bayesian inference in a class of latent Gaussian models. This class of models is characterized by linking the possibly non-Gaussian outputs to the inputs through a latent Gaussian field controlled by few hyperparameters and includes, among others, generalized linear models, additive models, smoothing splines, state space models, spatial and spatiotemporal models, and log-Gaussian Cox processes. The main advantage of INLA over other Bayesian inference methods, such as MCMC , is computation time. In this talk, we will describe the algorithm in detail and provide a demo of the R-INLA package.

This talk is part of the Machine Learning Reading Group @ CUED series.

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