Institute of Statistical Science Academia Sinica [Seminar Feed] http://www.stat.sinica.edu.tw Statistics, Stat, Edu en-us Mon, 22 Oct 2018 13:11:07 +0800 http://www.stat.sinica.edu.tw/statnewsite/seminar/rss/ PHP admin@stat.sinica.edu.tw admin@stat.sinica.edu.tw Statistics with a human face http://www.stat.sinica.edu.tw/statnewsite/seminar/show/2301/

Abstract

    Three-dimensional surface imaging, through laser-scanning or stereo-photogrammetry, provides high-resolution data defining the surface shape of objects.  In an anatomical setting this can provide invaluable quantitative information, for example on the success of surgery.  Two particular applications are in the success of facial surgery and in developmental issues with associated facial shapes.  An initial challenge is to extract suitable information from these images, to characterise the surface shape in an informative manner.  Landmarks are traditionally used to good effect but these clearly do not adequately represent the very much richer information present in each digitised images. Curves with clear anatomical meaning provide a good compromise between informative representations of shape and simplicity of structure, as well as providing guiding information for full surface representations.  Some of the issues involved in analysing data of this type will be discussed and illustrated.  Modelling issues include the measurement of asymmetry and longitudinal patterns of growth.


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Wed, 15 Aug 2018 09:13:20 +0800 http://www.stat.sinica.edu.tw/statnewsite/seminar/show/2301/
Bayesian approach to deal with incomplete information for queueing models http://www.stat.sinica.edu.tw/statnewsite/seminar/show/2307/

Abstract

 

    In this talk, we describe challenges in modeling and analyzing queueing systems with incomplete information, which is often the case arising from large-scaled systems with dependence such as various Internet applications involving big data.  We try to use Bayesian approach for queueing models with miss information. We first start with a simple example to demonstrate basic ideas and concepts and then show how Bayesian approach works for the join-the-shortest-queue (JSQ for short) model with two parallel queues, where the servers may be heterogeneous.  Since in practice, the complete data are not always (or seldom) available, we are dealing with the uncertainty of the JSQ parameters in case of missing information.  The uncertainty is, indeed, caused by the lack of information (to the observer or customers) about the desired queueing model, resulted in situations where the shortest queue is not always assigned (by the system) to the upcoming jobs. We show how a Bayesian approach can be applied to estimate unknown system parameters and to how the prediction can help the system to assign the shortest queue to the upcoming jobs more accurately.

 

This talk is based on the joint work with Ehssan Ghashim.

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Sat, 13 Oct 2018 18:24:34 +0800 http://www.stat.sinica.edu.tw/statnewsite/seminar/show/2307/
Statistical and Computational Approaches for the Identification of Novel Viruses and Virus-host Interactions http://www.stat.sinica.edu.tw/statnewsite/seminar/show/2308/

Abstract


    Viruses play important roles in controlling bacterial population size, altering host metabolism, and have broader impacts on the functions of microbial communities, such as human gut, soil, and ocean microbiomes. However, the investigations of viruses and their functions were vastly underdeveloped. Metagenomic studies provide enormous resources for the identifications of novel viruses and their hosts. We recently developed a k-mer based method, VirFinder, for the identification of novel virus contigs in metagenomic samples [1]. Applications to a liver cirrhosis metagenomic data suggest that viruses play important roles in the development of the disease. We also developed an alignment-free statistic, VirHost-Matcher, for the identification of bacterial hosts of viruses [2] and machine learning based approaches to identify new viruses infecting particular hosts with a relative large number of infecting viruses [3]. Recently we also developed an integrative approach for predicting virus-host interactions [4].

 

1.    J Ren, NA Ahlgren, et al. (2017) Microbiome 5(1):69

2.    NA Ahlgren,  J Ren, et al. (2017) Nucleic Acids Research 45(1):39-53

3.    MG Zhang, et al. (2017) BMC Bioinformatics (APBC2017) 18 (3), 60

4.    WL Wang, J Ren et al. (2018) Under Review.

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Fri, 19 Oct 2018 09:27:19 +0800 http://www.stat.sinica.edu.tw/statnewsite/seminar/show/2308/