12月22日学术交流报告通知：Tree-based mixture distributions

报告题目：

Tree-based mixture distributions: Applications in ecology and epidemiology

报告时间：12月22日（周二）15:00-17:00

报告地址：ZOOM  会议号：872 5682 0078

密 码：123456

二维码：

报告人简介：

Stephane Robin,法国国家农业科学研究院（INRA）高级研究员。现担任期刊《Biometrics》《J-SFdS》编委。主要研究方向为：统计学、分子生物统计学和生态统计学等。近年来，Robin研究员在概率图模型中潜变量的统计推断领域取得了丰硕的研究成果。著有3部著作，参编6部专著，在《Journal Royal of Statistical Society: Series B》、《Bernoulli》、《Annual of Applied Statistics》、《PLoS Computational Biology》、《Statistics and Computing》、《Statistical Modelling》、《Journal of Computational and Graphical Statistics》、《Biometrics》、《Computational Statistics and Data Analysis》等期刊上发表多篇研究成果。.

报告内容简介：

Graphical models provide a powerful framework to analyze the dependency structure relating a set of random variables. Recently, the inference of the structure of a graphical model has received a lot of attention, and one of the main issue is the exploration of the space of possible graphs, which can not be carried in a naive manner because of combinatorial complexity. Spanning trees constitute a subset of graph, which fulfill the popular sparsity assumption. Still, the tree structure is a too restrictive assumption for most applications. However, the Matrix-Tree theorem enables to integrate over the set of all spanning tree at the cost of the calculation of a determinant, which allows considering a mixture of tree-shaped graphical models. In this talk, we will show how mixtures of tree-shapes graphical models can be used to infer the graphical model of a set of variables and the applications in ecology and epidemiology.