Titlebook: Statistical Modeling Using Bayesian Latent Gaussian Models; With Applications in Birgir Hrafnkelsson Book 2023 Springer Nature Switzerland

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Improving Numerical Weather Forecasts by Bayesian Hierarchical Modelling,nd correct systematic differences between forecasts and observation. We review the state of the art of statistical postprocessing of predictions produced by atmospheric simulation models and report encouraging results on the application of Bayesian hierarchical models to improve on the existing stat

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Bayesian Latent Gaussian Models for High-Dimensional Spatial Extremes,using extended latent Gaussian models (LGMs), and how to exploit the fitted model in practice for the computation of long-term return levels. The extended LGM framework assumes that the data follow a specific parametric distribution, whose unknown parameters are transformed using a multivariate link

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Book 2023els is described in a thorough introductory chapter, which explains how to construct prior densities for the model parameters, how to infer the parameters using Bayesian computation, and how to use the models to make predictions. The remaining six chapters focus on the application of Bayesian latent

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anizations that have become mythical in themselves. These narratives are presented as organizational sagas to reveal an archetypal dimension of organizing and organizations.978-1-349-35414-6978-0-230-58360-3