Probability Trees
Diego A. Mej\'ia, Andr\'es F. Uribe-Zapata

TL;DR
This paper formally defines probability trees, explores their structural properties, and demonstrates their applications across probability, measure, and set theory, establishing foundational connections and invariance results.
Contribution
It introduces a formal definition of probability trees, proves their correspondence with inductive probability measures, and explores their applications in various mathematical domains.
Findings
Probability trees are characterized by inductive probability measures.
Dependent Bernoulli tests' distributions are bounded by binomial distributions.
Null ideals of probability trees are Tukey equivalent to those on [0,1].
Abstract
In this article, we introduce a formal definition of the concept of probability tree and conduct a detailed and comprehensive study of its fundamental structural properties. In particular, we define what we term an inductive probability measure and prove that such trees can be identified with these measures. Furthermore, we prove that probability trees are completely determined by probability measures on the Borel -algebra of the tree's body. We then explore applications of probability trees in several areas of mathematics, including probability theory, measure theory, and set theory. In the first, we show that the cumulative distribution of finitely many dependent and non-identically distributed Bernouli tests is bounded by the cumulative distribution of some binomial distribution. In the second, we establish a close relationship between probability trees and the real line,…
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Taxonomy
TopicsData Mining Algorithms and Applications
