Probability Distribution on Full Rooted Trees

Yuta Nakahara*, Shota Saito, Akira Kamatsuka, Toshiyasu Matsushima


研究成果: Article査読

3 被引用数 (Scopus)


The recursive and hierarchical structure of full rooted trees is applicable to statistical models in various fields, such as data compression, image processing, and machine learning. In most of these cases, the full rooted tree is not a random variable; as such, model selection to avoid overfitting is problematic. One method to solve this problem is to assume a prior distribution on the full rooted trees. This enables the optimal model selection based on Bayes decision theory. For example, by assigning a low prior probability to a complex model, the maximum a posteriori estimator prevents the selection of the complex one. Furthermore, we can average all the models weighted by their posteriors. In this paper, we propose a probability distribution on a set of full rooted trees. Its parametric representation is suitable for calculating the properties of our distribution using recursive functions, such as the mode, expectation, and posterior distribution. Although such distributions have been proposed in previous studies, they are only applicable to specific applications. Therefore, we extract their mathematically essential components and derive new generalized methods to calculate the expectation, posterior distribution, etc.

出版ステータスPublished - 2022 3月

ASJC Scopus subject areas

  • 情報システム
  • 数理物理学
  • 物理学および天文学(その他)
  • 電子工学および電気工学


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