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:20210129T043000Z DTEND:20210129T061500Z DTSTAMP:20260421T174009Z UID:BPS/18 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 29th\, 2021\nTime: 11:00-11: 45am\n\nLecture 3: Bayesian Modelling and Analysis of Challenging Data Des cribing\nSystems of Data\n\nAbstract: A common challenge is to analyse dat a as part of a\nsystem. In this presentation\, I describe a number of Baye sian\napproaches to modelling such data\, using as motivation case studies in\nneurology\, ecology and industry. These case studies focus on\nunders tanding changes in the brain associated with a degenerative\ndisease\, and suggesting optimal dredgingstrategies for conservation of\nseagrass. The techniques employed include Bayesian wombling and\ndynamic Bayesian networ ks.\n\nDate: January 29th\, 2021\nTime: 12:00-12:45pm\n\nLecture 4: Bayesi an Modelling and Analysis of Challenging Data Making New\nSources of Data Trustworthy\n\nAbstract: Evidence-based decisions depend critically on tru stworthy\ndata. Two forms of data that have been brought into question in recent\ntimes are sensor data and citizen science data. Sensors are a key\ ncomponent of IoT and have created a step-change in our ability to\nmonito r systems. However\, they are often subject to technical\nanomalies that r aise concerns about the validity of their data and\nsignals. Citizen scien ce is also growing in utility and interest in\nmany areas\, but often suff ers from concerns about the credibility the\ninformation provided by commu nity members. In this presentation\, I\nwill describe some new approaches to resolving some of these\nconcerns. These include new methods for anomal y detection in\nhigh-dimensional streaming time series\, and Bayesian mode ls for\nestimating the latent ability of citizens taking into account the\ ndifficulty of the tasks. This work has been developed in collaboration\nw ith a number of teams working on challenges in ecology and industry\;\nthe se teams will be acknowledged and the associated challenges\ndiscussed dur ing the presentation.\n\n\nThe lectures were originally due to held in Mar ch 2020 but are now being\nheld online via Zoom platform.\n\nhttps://us02w eb.zoom.us/j/88118975864?pwd=cFRmWXMreWdiLzR5VGlCYXBCbE1WZz09\n LOCATION:https://researchseminars.org/talk/BPS/18/ END:VEVENT END:VCALENDAR