The theories about crime and correction have their inception in the eighteenth century, highly influenced by the anthropological thoughts emerging during the age of Enlightenment. Throughout the decades, the criminological studies observed their sociological essence encompassing practices from other scientific fields to explain the more contemporary questions, becoming Criminology an inherently interdisciplinary science as a result. The adoption of concepts from Exact Sciences is a recent moving, originating it a novel research area, called Computational Criminology, which employs procedures from Applied Mathematics, Statistics and Computer Science to provide original or enhanced solutions to such questions. One of the most prominent tasks brought by this rising field is crime prediction, which attempts to uncover potential targets for future police intervention and also help solving already committed offenses. The present comparative analysis thus investigates the employment of statistical inference by means of Bayesian network for predictive policing, using the openly accessible registers from Chicago Police Department. Numerous algorithms are available to learn the structure for a Bayesian network purely from data and a comparative examination about them is hence described, with the purpose to establish the most precise and efficient one, according to the attributes of the said criminal dataset, for the implementation of the intended inference.

中文翻译：

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贝叶斯网络结构学习算法应用于犯罪数据的比较分析

有关犯罪和矫正的理论于18世纪问世，受到启蒙时代出现的人类学思想的强烈影响。在过去的几十年中，犯罪学研究观察到了其社会学实质，涵盖了其他科学领域的实践，以解释更为当代的问题，从而使犯罪学成为一门固有的跨学科科学。来自Exact Sciences的概念的采用是最近的发展趋势，它是一个崭新的研究领域，称为计算犯罪学，该领域采用了应用数学，统计学和计算机科学的程序来提供此类问题的原始或增强的解决方案。这个新兴领域带来的最突出的任务之一是犯罪预测，它试图发现潜在的目标，以备将来警方干预，也有助于解决已经犯下的罪行。因此，本比较分析使用贝叶斯网络通过芝加哥警察局公开可访问的登记册，调查了采用统计推理进行贝叶斯预测性警务的情况。有许多算法可用于纯粹从数据中学习贝叶斯网络的结构，因此描述了对它们的比较检查，目的是根据所述犯罪数据集的属性建立最精确，最有效的算法，以进行实施预期的推断。因此，本比较分析使用贝叶斯网络通过芝加哥警察局可公开获取的登记册，调查了采用贝叶斯网络进行预测性警务的统计推断方法。有许多算法可用于纯粹从数据中学习贝叶斯网络的结构，因此描述了对它们的比较检查，目的是根据所述犯罪数据集的属性建立最精确，最有效的算法，以进行实施预期的推断。因此，本比较分析使用贝叶斯网络通过芝加哥警察局可公开获取的登记册，调查了采用贝叶斯网络进行预测性警务的统计推断方法。有许多算法可用于纯粹从数据中学习贝叶斯网络的结构，因此描述了对它们的比较检查，目的是根据所述犯罪数据集的属性建立最精确，最有效的算法，以进行实施预期的推断。