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在当前大数据背景下,信息主导警务已经逐渐成为新常态,其中一个重要方面就是犯罪信息研判。在犯罪信息研判中,侦查人员必须对获取的信息进行分析以对进行必要的预测,其中的一种重要方式就是概率推理。犯罪信息研判中的概率推理是以事件发生的概率作为前提或者结论的推理,用以计算侦查工作中大量存在的随机事件出现的概率。在进行概率推理之前,侦查人员必须首先确定简单事件的概率,而对简单事件的概率有三种不同的解释。在确定了简单事件的概率之后,侦查人员可以利用概率演算的规则计算出复合事件的概率。在此基础上,侦查人员可以在个体、样本和总体之间进行不同类型的概率推理。当然,侦查人员同时必须遵守相应的推理规则以避免相关的谬误,得到具有最大可靠性的结论,提高犯罪信息研判的准确度和精确度。
In the current context of big data, information-led policing has gradually become the new normal, one of the most important aspects is the criminal information judgments. One of the important ways in which criminal investigators must analyze the acquired information to make the necessary predictions at the crime trial is probabilistic reasoning. The probabilistic reasoning in the criminal information judgment is based on the probability of the occurrence of the incident as the premise or the reasoning of the conclusion, which is used to calculate the probability of occurrence of random events that exist in large quantities during the investigation. Before probabilistic reasoning, the investigator must first determine the probability of a simple event, and there are three different interpretations of the probability of a simple event. After determining the probability of a simple event, investigators can use the rules of probability calculus to calculate the probability of a compound event. On this basis, investigators can perform different types of probabilistic reasoning between individuals, samples and totals. Of course, investigators must also comply with the corresponding rules of reasoning to avoid the related fallacies, get the conclusion with the maximum reliability, and improve the accuracy and accuracy of criminal information judgments.