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杂论：非线性问题中的序贯设计
In this paper we study the consistency of parameter estimators in sequential nonlinear design. Our main tool is a recursion which holds for the sequence of posterior variances. Under mild conditions, we establish the strong consistency of Bayes estimators in stochastic regression models which cover a broad range of nonlinear problems. Four examples from nonlinear sequential design are discussed.
利用后验协方差矩阵研究随机回归的强一致性
In this paper we use posterior covariance matrices to study the strong consistency of Bayes estimators in stochastic regression models under various assumptions on the stochastic regressors. The random errors are assumed to form a martingale difference sequence. Several results are obtained using a recursion satisfied by the sequence of posterior covariance matrices. These results suggest that the posterior covariance matrix is a useful tool in studying strong consistency…