報(bào)告人: Vesna Rajic, 教授，貝爾格萊德大學(xué)，塞爾維亞
報(bào)告人簡(jiǎn)介：貝爾格萊德大學(xué)經(jīng)濟(jì)與商業(yè)學(xué)院的教授，研究領(lǐng)域包括理論和應(yīng)用統(tǒng)計(jì)學(xué)、非線性分析、精算數(shù)學(xué)。現(xiàn)擔(dān)任塞爾維亞本國(guó)期刊Ekonomika preduzeca以及國(guó)際著名期刊J. of Statistics: Advances in Theory and Applications的編委會(huì)委員，著有Risk measurement and control in insurance以及 Quantitative Models in Economics兩本專著。是塞爾維亞統(tǒng)計(jì)學(xué)會(huì)會(huì)員；塞爾維亞數(shù)學(xué)學(xué)會(huì)會(huì)員；經(jīng)濟(jì)學(xué)院理事會(huì)理事；經(jīng)濟(jì)學(xué)家科學(xué)學(xué)會(huì)會(huì)員；經(jīng)濟(jì)學(xué)院教授委員會(huì)委員，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 preduze?a這些期刊的審稿人。Vesna Raji?在科學(xué)期刊上發(fā)表了40多篇文章，會(huì)議論文約30篇，專著章節(jié)11篇。曾參與4個(gè)國(guó)內(nèi)項(xiàng)目和2個(gè)國(guó)際項(xiàng)目。
邀請(qǐng)人：李偉
報(bào)告地點(diǎn)：騰訊會(huì)議 #428-488-261
報(bào)告時(shí)間：28號(hào)從13點(diǎn)開(kāi)始，每人兩小時(shí)
報(bào)告題目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.
報(bào)告題目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.
