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关于5月24日下午三场学术报告&香港中文大学学生分享会的报名通知

编辑:wfy 时间:2019年05月20日 访问次数:1202

时间:201952414001815

地点:玉泉校区数学中心五楼报告厅

具体安排:

a)14:00—14:45 Prof. Yam学术报告

TitleShape-constrained Inference: Monotonicity of Densities

Abstract: An a priori understanding of the (even just topological) structure of the data from a specific 
discipline can often strengthen much of the effectiveness of the inference, both for estimation and hypothesis  
testing, behind the mechanism of interest. Although the celebrated work of Grenander (1956) on the estimation  
of decreasing densities has been around for long, it is until recently that Shape-constrained inference has 
 become one of the most popular research areas in Statistics. One major reason behind is due to the 
challenges and mathematical difficulties from the theory; indeed, just limit to the scope of monotonicity of 
densities, there still remain plenty of lasting problems. In this talk, I shall introduce a couple of them, and then 
discuss on what the final resolution we provided. 
b)14:45—15:30 Prof. Kong Dexing学术报告

Title&abstract: to be confirmed

c)15:30—16:15 Prof. Zhang Peng学术报告

Title: Cluster analysis and visualization of sparse categorical data

Abstract: Sparsity in features presents a big technical challenge to existing cluster analysis and visualization of categorical data. Hierarchical Bayesian Bernoulli mixture model (HBBMM) incorporates constrained empirical Bayes priors for model parameters, so the resulting Expectation Maximization (EM) algorithm of estimator searching is confined in a proper region. The EM algorithm enables to obtain the maximum a posterior (MAP) estimation, in which cluster labels are simultaneously assigned. Another mixture model-based approach to clustering categorical data, as well as visualization method using latent variables, is also discussed. Several real-world sparse categorical datasets are analyzed with the proposed methods. 

d)16:45—18:15港中大学生向浙大师生介绍分享学习、实习情况等

报名联系人:张驰昊同学

注:需参加完1400-18:15的全部活动后,记三场学术报告(要交三份报告)、一次形策。