期刊
ENTROPY
卷 19, 期 5, 页码 -出版社
MDPI AG
DOI: 10.3390/e19050232
关键词
entropy; minimum cross entropy; joint probability distribution
资金
- National Science Foundation [CMMI 15-65168, CMMI 16-29752, CMMI 16-44991]
Entropy methods enable a convenient general approach to providing a probability distribution with partial information. The minimum cross-entropy principle selects the distribution that minimizes the Kullback-Leibler divergence subject to the given constraints. This general principle encompasses a wide variety of distributions, and generalizes other methods that have been proposed independently. There remains, however, some confusion about the breadth of entropy methods in the literature. In particular, the asymmetry of the Kullback-Leibler divergence provides two important special cases when the target distribution is uniform: the maximum entropy method and the maximum log-probability method. This paper compares the performance of both methods under a variety of conditions. We also examine a generalized maximum log-probability method as a further demonstration of the generality of the entropy approach.
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