This paper focuses on building models of stochastic systems with aleatoric uncertainty. The nature of the considered systems is such that the identical inputs can result in different outputs, i.e. the output is a random variable. This paper suggests a novel algorithm of boosted ensemble training of multiple models for obtaining a probability distribution of an individual output as a function of a system input. The deterministic component in the ensemble can be an arbitrarily-chosen regression model. The algorithm does not require any special properties of the model, other than having descriptive capabilities with some expected accuracy for the chosen dataset type. The efficiency of the suggested algorithm is demonstrated by comparing it with the Monte-Carlo simulations and by modelling of a complex social system - sports betting choices adjusted for obtaining a monetary gain.
翻译:本文侧重于建立具有偏移不确定性的随机系统模型。 被考虑的系统的性质是, 相同的输入可以导致不同的输出, 即输出是一个随机变量。 本文建议了一种新型的算法, 即增强多种模型的混合培训, 以获得单个输出的概率分布作为系统输入的函数。 组合中的确定性成分可以是任意选择的回归模型。 算法并不要求模型的任何特殊特性, 除了具备描述能力, 且对所选数据集类型具有某种预期的准确性。 所建议的算法的效率可以通过将其与蒙特卡罗模拟和复杂的社会系统建模( 为获得金钱收益而调整的体育赌注选择)加以比较来证明。