Given a prior estimate of a probability, q, and a constraint /spl Sigma/p/sub i/x/sub i/=a, one well-known way of estimating p is to minimize the cross-entropy I(p; q) subject to the constraint. A modification to this method is proposed for use when the value a is only approximately known. The modification is based on the penalty function method in constrained optimization. It has an interpretation in differential geometry methods in statistics and it sometimes gives a maximum-likelihood estimate.
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机译：给定先验的概率q和约束条件/ spl Sigma / p / sub i / x / sub i / = a，一种众所周知的估计p的方法是最小化交叉熵I（p; q ）受到约束。当值α仅是近似已知时，建议对该方法进行修改。该修改基于约束优化中的罚函数法。它对统计中的微分几何方法有一种解释，有时会给出最大似然估计。
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