Fitting Latent Non-Gaussian Models Using Variational Bayes and Laplace Approximations
利用变分贝叶斯与拉普拉斯近似拟合潜在非高斯模型
Latent Gaussian models (LGMs) are perhaps the most commonly used class of models in statistical applications. Nevertheless, in areas ranging from longitudinal studies in biostatistics to geostatistics, it is easy to find datasets that contain inherently non-Gaussian features, such as sudden jumps or spikes, that adversely affect the inferences and predictions made using an LGM. These datasets require more general latent non-Gaussian models (LnGMs) that can handle…