New Frontiers in Bayesian Modeling Using the <b>INLA</b> Package in <i>R</i>
Janet van Niekerk, Haakon Bakka, Håvard Rue, Olaf Schenk
Abstract
The INLA package provides a tool for computationally efficient Bayesian modeling and inference for various widely used models, more formally the class of latent Gaussian models. It is a non-sampling based framework which provides approximate results for Bayesian inference, using sparse matrices. The swift uptake of this framework for Bayesian modeling is rooted in the computational efficiency of the approach and catalyzed by the demand presented by the big data era. In this paper, we present new developments within the INLA package with the aim to provide a computationally efficient mechanism for the Bayesian inference of relevant challenging situations.
Topics & Concepts
Computer scienceInferenceBayesian probabilityBayesian inferenceR packageGaussian processMachine learningArtificial intelligenceData miningGaussianProgramming languagePhysicsQuantum mechanicsStatistical Methods and Bayesian InferenceGaussian Processes and Bayesian InferenceBayesian Methods and Mixture Models