inlabru: Bayesian spatial and spatio-temporal modelling in R Seminar

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, in particular in environmental sciences requiring spatial and spatio-temporal random field 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, allowing both easier access to complex spatial model specification, and new applications in ecology, epidemiology, and geosciences.

Speaker Information

Speaker: Prof. Finn Lindgren

Bio:
Finn Lindgren is a Professor of Statistics in the School of Mathematics at the University of Edinburgh. He received a PhD in Engineering in Mathematical Statistics at Lund University (2003), and has since worked as lecturer and research fellow at Lund University and Norwegian Institute of Science and Technology in Trondheim, followed by four years as Reader at the University of Bath, before joining the growing Statistics group in Edinburgh in 2016. He has served as Associate Editor of Annals of Applied Statistics, as member of the Royal Statistical Society Research Committee, and is an Elected Member of ISI. His research covers spatial and spatio-temporal modelling and computational Bayesian methods. In particular, the development of stochastic partial differential equations to enable the use of computationally efficient methods for sparse matrices and Markov random fields lead to an RSS read paper in 2011. The subsequent software development, including the MCMC-free Bayesian statistics R packages INLA, inlabru, and excursions, have lead to involvement in a broad range of applications, including large scale modelling for climate science, point process models for animal abundance in ecology and earthquake forecasting in geoscience, as well as animal movement models, finance, genetics, and epidemiology.

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