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Hilbert space on probability density functions with Aitchison geometry

机译:具有Aitchison几何的概率密度函数上的希尔伯特空间

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摘要

Compositional data analysis motivated the introduction of a complete Euclidean structure in the simplex of D parts. This was based on the early work of J. Aitchison (1986) and completed recently when Aitchinson distance in the simplex was associated with an inner product and orthonormal bases were identified (Aitchison and others, 2002; Egozcue and others, 2003). A partition of the support of a random variable generates a composition by assigning the probability of each interval to a part of the composition. One can imagine that the partition can be refined and the probability density would represent a kind of continuous composition of probabilities in a simplex of infinitely many parts. This intuitive idea would lead to a Hilbert-space of probability densitiesby generalizing the Aitchison geometry for compositions in the simplex into the set probability densities
机译:成分数据分析促使D部分单纯形引入完整的欧几里得结构。这是基于J.Aitchison(1986)的早期工作,最近完成的,当单纯形中的Aitchinson距离与内部乘积相关联并且确定了正交基数时(Aitchison等,2002; Egozcue等,2003)。通过将每个间隔的概率分配给组成的一部分,随机变量支持的分区会生成组成。可以想象,可以对分区进行细化,并且概率密度将表示无限多个部分的单纯形中概率的一种连续组成。通过将单纯形中的成分的Aitchison几何推广到设定的概率密度中,这种直观的想法将导致概率密度的希尔伯特空间

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