时间：2019年5月24日14：00―18：15

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

Title：Shape-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.

报名联系人：张驰昊同学

注：需参加完14：00-18:15的全部活动后，记三场学术报告(要交三份报告)、一次形策。