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复杂系统的贝叶斯线性校准预测AI 解读

A calibration-based approach is developed for predicting the behavior of a physical system that is modeled by a computer simulator. The approach is based on Bayes linear adjustment using both system observations and evaluations of the simulator at parameterizations that appear to give good matches to those observations. This approach can be applied to complex high-dimensional systems with expensive simulators, where a fully Bayesian approach would be impractical. It is…

线性贝叶斯估计量的一般方差修正AI 解读

We consider the problem of modifying the linear Bayes estimator for the mean of a distribution of unknown form by using an estimate of sample variance. The general conditions under which there is no advantageous variance modification to the linear Bayes rule are identified. These results are applied to the problem of sampling from a normal distribution with unknown mean and variance, and the implications are discussed.

线性贝叶斯估计量的一般方差修正AI 解读

We consider the problem of modifying the linear Bayes estimator for the mean of a distribution of unknown form by using an estimate of sample variance. The general conditions under which there is no advantageous variance modification to the linear Bayes rule are identified. These results are applied to the problem of sampling from a normal distribution with unknown mean and variance, and the implications are discussed.

模型选择中的样本量选择与可区分性评估AI 解读

In this article we present a general Bayesian approach to discrimination. We describe how the difference between two separate families of distributions can be assessed in terms of differences between two binomial models. Two examples that illustrate the methodology are included. Specifically, we investigate the extent to which it is possible to discriminate between the von Mises and the wrapped normal distributions and also between the normal and a family of t distributions.

模型选择中的样本量选择与可区分性评估AI 解读

In this article we present a general Bayesian approach to discrimination. We describe how the difference between two separate families of distributions can be assessed in terms of differences between two binomial models. Two examples that illustrate the methodology are included. Specifically, we investigate the extent to which it is possible to discriminate between the von Mises and the wrapped normal distributions and also between the normal and a family of t distributions.

弱先验假设下的线性贝叶斯回归估计量AI 解读

A simple set of prior assumptions is given with respect to which the linear Bayes regression estimator, in the case of univariate regression, is determined explicitly. The risk, also determined explicitly, is shown to decrease,at rate 0 ( n-1 ), to the minimum risk attainable by any linear fitting procedure.

弱先验假设下的线性贝叶斯回归估计量AI 解读

A simple set of prior assumptions is given with respect to which the linear Bayes regression estimator, in the case of univariate regression, is determined explicitly. The risk, also determined explicitly, is shown to decrease,at rate 0 ( n-1 ), to the minimum risk attainable by any linear fitting procedure.

方差修正的线性贝叶斯估计量AI 解读

Summary A modification of the linear Bayes estimator of the mean of a distribution of unknown form is proposed, involving a linear estimate for the variance of the distribution. The risk properties of the estimator are discussed and an example is given.

指数族分布贝叶斯估计量的收缩变换AI 解读

Sufficient conditions are provided to ensure that the Bayes estimator with squared error loss for the natural parameter of an exponential family distribution is a linear function of the sample value, given that various contractions of the Bayes estimator are also Bayes estimators, generalizing a result of Meeden (1976).