BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Kerrie Mengersen (Queensland University of Technology\, Australia) DTSTART:20210127T043000Z DTEND:20210127T061500Z DTSTAMP:20260421T173915Z UID:BPS/17 DESCRIPTION:Title: Bay esian Modelling and Analysis of Challenging Data\nby Kerrie Mengersen (Queensland University of Technology\, Australia) as part of Bangalore Pro bability Seminar\n\n\nAbstract\nPCM-2019-20\n\nhttps://www.isibang.ac.in/~ statmath/pcm2019/\n\nProfessor Kerrie Mengersen\nScience and Engineering F aculty\, School of Mathematical Sciences\nQueensland University of Technol ogy (QUT)\n(https://staff.qut.edu.au/staff/k.mengersen)\n\nwill deliver th e P.C. Mahalanobis Memorial Lectures 2019-20 on\n\nBayesian Modelling and Analysis of Challenging Data\n\nDate: January 27th\, 2021\nTime: 11:00-11: 45am\n\nLecture 1: Bayesian Modelling and Analysis of Challenging Data\nId entifying the Intrinsic Dimension of High-Dimensional Data\n\nAbstract: On e of the challenges of high-dimensional data is\nidentifying the true info rmation contained therein. In this\npresentation\, I will describe some ap proaches that we have developed\nto address this challenge. These approach es include Bayesian methods\nof matrix factorisation\, intrinsic dimension and structured variable\nselection. This discussion will be set in the co ntext of substantive\ncase studies in image analysis\, sport and genomics. \n\nDate: January 27th\, 2021\nTime: 12:00-12:45pm\n\nLecture 2: Bayesian Modelling and Analysis of Challenging Data Finding\nPatterns in Highly Str uctured Spatio-Temporal Data\n\nAbstract: Many forms of current data are i ndexed by space and/or\ntime. Often these space-time relationships are hig hly\nstructured. Examples include data collected across networks of\nstrea ms\, multi-state data collected over time\, and multivariate\nresponses co llected at small area level across a geographic space. In\nthis presentati on\, I will describe some of our approaches to Bayesian\nmodelling and ana lysis of these types of data. Two particular issues\nwill be discussed. Th e first is the role of priors in spatial models\nand hidden Markov models. The second is the detection of anomalies for\nevent identification and to increase the trustworthiness of the data.\n\n\nThe lectures were original ly due to held in March 2020 but are now being\nheld online via Zoom platf orm.\n\nhttps://us02web.zoom.us/j/88118975864?pwd=cFRmWXMreWdiLzR5VGlCYXBC bE1WZz09\n LOCATION:https://researchseminars.org/talk/BPS/17/ END:VEVENT END:VCALENDAR