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A0742
Title: Non-linear and joint models with the inlabru package Authors:  Finn Lindgren - University of Edinburgh (United Kingdom) [presenting]
Abstract: The Integrated Nested Laplace Approximation (INLA) method was developed to handle latent Gaussian additive regression models. Combined with the stochastic partial differential equation method for constructing computationally efficient representations of Gaussian random fields, this has enabled fast Bayesian analysis of a wide range of models. The inlabru package extends this to a more general model class that allows more non-linearity, and a more user-friendly interface for specifying complex models, such as point process models and joint models for multiple response variables and spatial covariates. By using an iterated INLA approach, the computational power of the R-INLA implementation is extended to a wider range of models.