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用于贝叶斯预测的奇异值收缩先验AI 解读

We develop singular value shrinkage priors for the mean matrix parameters in the matrix-variate normal model with known covariance matrices. Our priors are superharmonic and put more weight on matrices with smaller singular values. They are a natural generalization of the Stein prior. Bayes estimators and Bayesian predictive densities based on our priors are minimax and dominate those based on the uniform prior in finite samples. In particular, our priors work well when the…

贝叶斯预测中的收缩先验AI 解读

We investigate shrinkage priors for constructing Bayesian predictive distributions. It is shown that there exist shrinkage predictive distributions asymptotically dominating Bayesian predictive distributions based on the Jeffreys prior or other vague priors if the model manifold satisfies some differential geometric conditions. Kullback–Leibler divergence from the true distribution to a predictive distribution is adopted as a loss function. Conformal transformations of model…

具有脉冲的连续过程采样时间序列的状态空间建模AI 解读

A non-Gaussian time series model useful for describing the dynamics of endocrinological data is introduced by using a state-space representation. This model is applicable even when the data are sparsely sampled in comparison with time lengths of pulses. Likelihood-based methods of inference for the model are developed. By fitting the present model to partially observed hormonal time series data, we can estimate the locations and heights of the peaks of the pulses as well as…

含脉冲连续过程采样时间序列的状态空间建模AI 解读

A non-Gaussian time series model useful for describing the dynamics of endocrinological data is introduced by using a state-space representation. This model is applicable even when the data are sparsely sampled in comparison with time lengths of pulses. Likelihood-based methods of inference for the model are developed. By fitting the present model to partially observed hormonal time series data, we can estimate the locations and heights of the peaks of the pulses as well as…