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贝叶斯分析中有界影响的一个注记AI 解读

Let χ be a single observation from a distribution having unknown location parameter θ. Dawid (1973) provided sufficient conditions for the posterior distribution of θ to approach the prior distribution as χ tends to infinity, so that an outlier has bounded and vanishing influence on the posterior distribution. We present a result closely related to Dawid's theorem. This enables us to consider priors and likelihoods for the location problem which have bounded but nonvanishing…

涉及指数族的贝叶斯推断中后验累积量的关系AI 解读

For Bayesian inference in one-parameter contexts where either the likelihood or the prior has an exponential family form, relationships are derived for posterior moments and cumulants of (functions of) both the canonical and the expectation parameters. The identities exhibited generalize the simple relationships well known in the conjugate analysis case. Applications of these results are indicated in the areas of Bayesian robustness and approximation. In particular, results…

指数族贝叶斯推断中的后验累积量关系AI 解读

For Bayesian inference in one-parameter contexts where either the likelihood or the prior has an exponential family form, relationships are derived for posterior moments and cumulants of (functions of) both the canonical and the expectation parameters. The identities exhibited generalize the simple relationships well known in the conjugate analysis case. Applications of these results are indicated in the areas of Bayesian robustness and approximation. In particular, results…

正态位置参数后验矩的精确与近似表达AI 解读

SUMMARY The forms of first and second posterior moments for a normal location parameter are identified for a rather general class of prior distributions. Exact and approximate illustrations are given where the prior distribution is double exponential or Student t respectively.

区分正态线性模型的标准贝叶斯程序之替代方法AI 解读

We consider the standard Bayesian procedure for discrimination, focusing on its tendency to give low posterior probabilities to some correct models, even in conditions of moderate to large sampling sizes. Furthermore, in some situations that could well occur in practice, the standard procedure is even asymptotically inconsistent. We claim that the explanation is that the standard procedure inflates the posterior probability of the model that has the smallest expected…

正态线性模型判别中标准贝叶斯程序的替代方法AI 解读

We consider the standard Bayesian procedure for discrimination, focusing on its tendency to give low posterior probabilities to some correct models, even in conditions of moderate to large sampling sizes. Furthermore, in some situations that could well occur in practice, the standard procedure is even asymptotically inconsistent. We claim that the explanation is that the standard procedure inflates the posterior probability of the model that has the smallest expected…

正态化变换的贝叶斯方法AI 解读

The analysis of transformation of observations in the linear model with normal errors proposed by Box & Cox (1964) is considered. A different choice of noninformative unnormed prior is advocated, which is not outcome dependent. This new selection of prior leads to a formal identity between likelihood and Bayesian inference, both for the estimation of the best transformation to normality and for the presence of homoscedasticity and additivity under this transformation.…