Mercurial 发表于 2025-3-27 00:00:31

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APO 发表于 2025-3-27 02:49:11

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

disrupt 发表于 2025-3-27 05:51:26

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

新星 发表于 2025-3-27 11:04:46

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Engaged 发表于 2025-3-27 15:34:09

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A简洁的 发表于 2025-3-27 21:25:12

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

杀子女者 发表于 2025-3-28 01:17:50

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Diluge 发表于 2025-3-28 04:06:56

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