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复合似然推断与模型选择札记AI 解读

A composite likelihood consists of a combination of valid likelihood objects, usually related to small subsets of data. The merit of composite likelihood is to reduce the computational complexity so that it is possible to deal with large datasets and very complex models, even when the use of standard likelihood or Bayesian methods is not feasible. In this paper, we aim to suggest an integrated, general approach to inference and model selection using composite likelihood…

基于共轭潜在过程的指数族状态空间模型AI 解读

Summary This paper introduces non-linear and non-Gaussian state space models with analytic updating recursions for filtering and prediction. This new class of models involves some well-known results in the theory of exponential models and of exponential dispersion models and the latent process is defined in such a way that both the filtering and the prediction distributions turn out to be conjugate to the observation distribution at each step. The corresponding analytic and…

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基于 p* 公式的简化预测密度AI 解读

The predictive density proposed by Harris (1989) is based on integrating the density for a new observation with respect to the estimated sampling distribution of the maximum likelihood estimator of the unknown parameter. This has good properties, but is rather complicated to compute even for simple models. An approximation to the Harris proposal is considered which consists of approximating the sampling distribution of the maximum likelihood estimator of the unknown…