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arXiv physics/0406023physics.data-ancs.ITmath.ITmath.STstat.TH
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最大熵多元密度估计:一种精确的拟合优度方法

Maximum Entropy Multivariate Density Estimation: An exact goodness-of-fit approach

Sabbir Rahman, Mahbub Majumdar

摘要:

我们考虑的问题,估计人口的概率分布给定一组有限的多元样本,使用最大熵的方法。严格按照Jaynes的原始定义,我们对问题的精确表述仅考虑估计分布的平滑度(由其熵测量)和与样本数据的拟合优度相关的损失泛函的贡献,特别是不使用任何不能单独从样本数据证明的额外约束。通过将一般的多变量问题映射到一个易于处理的单变量问题,我们能够使用传统的基于似然的方法和严格的信息论方法写出任意多变量分布对任何给定样本集的拟合优度的精确表达式,从而解决了一个长期存在的问题。作为一个推论,我们也给出了一个确切的解决方案,以确定从人口与已知的概率分布的样本的预期分布的“前向问题”。

英文摘要:

We consider the problem of estimating the population probability distribution given a finite set of multivariate samples, using the maximum entropy approach. In strict keeping with Jaynes' original definition, our precise formulation of the problem considers contributions only from the smoothness of the estimated distribution (as measured by its entropy) and the loss functional associated with its goodness-of-fit to the sample data, and in particular does not make use of any additional constraints that cannot be justified from the sample data alone. By mapping the general multivariate problem to a tractable univariate one, we are able to write down exact expressions for the goodness-of-fit of an arbitrary multivariate distribution to any given set of samples using both the traditional likelihood-based approach and a rigorous information-theoretic approach, thus solving a long-standing problem. As a corollary we also give an exact solution to the `forward problem' of determining the expected distributions of samples taken from a population with known probability distribution.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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