报告人: Vesna Rajic, 教授，贝尔格莱德大学，塞尔维亚

报告人简介：贝尔格莱德大学经济与商业学院的教授，研究领域包括理论和应用统计学、非线性分析、精算数学。现担任塞尔维亚本国期刊Ekonomika preduzeca以及国际著名期刊J. of Statistics: Advances in Theory and Applications的编委会委员，著有Risk measurement and control in insurance以及 Quantitative Models in Economics两本专著。是塞尔维亚统计学会会员；塞尔维亚数学学会会员；经济学院理事会理事；经济学家科学学会会员；经济学院教授委员会委员，也是Neural Computing and Applications; FPTA; Journal of Applied Mathematics; Journal of Uncertainty Analysis and Applications; Journal of Statistical Computation and Simulation; Journal of Applied Statistics; Yujor; Economic Annals;Ekonomika preduzeća这些期刊的审稿人。Vesna Rajić在科学期刊上发表了40多篇文章，会议论文约30篇，专著章节11篇。曾参与4个国内项目和2个国际项目。

邀请人：李伟

报告地点：腾讯会议 #428-488-261

报告时间：28号从13点开始，每人两小时

报告题目1：Statistical analysis of fitting Pareto and Weibull distributions with Benford’s Law: theoretical approach and empirical evidence

摘要：We study the fundamental properties of Benford’s Law which investigates the distribution of the first digits’ appearance within datasets. The purpose and the usefulness of the research developed are to identify additional distributions, beyond those already investigated, that conform to the Benford distribution. As a main contribution, we state and prove with the new approach that the Pareto distribution and appropriate constant times Weibull density function, under some parameter constraint, obey Benford’s Law. Further, with the statistical tests and simulation method, we quantify how the fit varies as the parameters of the Pareto distribution change.

报告题目2：Using AI to verify and analyse Benford’s law in real data

摘要：Benford's law is a key tool for detecting irregularities and potential manipulations in numerical data sets. This law describes the probability of the appearance of the first digits in large sets of numerical values, which allows for the identification of anomalies and verification of authenticity in them. The subject of this paper is the application of artificial intelligence in the analysis and verification of Benford's law on real data. Given the increasingly widespread application of artificial intelligence in the automation of data analysis, fraud detection and statistical verification of economic and financial reports, the aim is to explore the possibilities of using machine learning algorithms, such as deep neural networks and classification methods, to recognize and analyze deviations from the expected distribution of the first digits.