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X-WR-CALDESC:Events for Australian Data Science Network
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DTSTART:20200101T000000
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DTSTART;VALUE=DATE:20210921
DTEND;VALUE=DATE:20210924
DTSTAMP:20210903T045102Z
CREATED:20210903T044703Z
LAST-MODIFIED:20210903T045102Z
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SUMMARY:Data-driven queueing challenges
DESCRIPTION:Data-driven queueing challenges\nThe increasing availability of data in the operation of large computer networks and the management of human service systems creates new problems in queueing theory. To respond\, ACEMS and NETWORKS are co-sponsoring an on-line workshop on `Data-driven queueing challenges’\, in association with The Alan Turing Institute. \nThe workshop brings together researchers from statistics\, stochastic modelling and data science to consider research directions in modelling and controlling queues\, and in dealing with parameter uncertainty\, when there is access to operational data. \nThe program contains 16 speakers and 2 panel discussions. \nRegister now
URL:https://australiandatascience.net/event/data-driven-queueing-challenges/
CATEGORIES:Online workshop
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DTSTART;TZID=Australia/Brisbane:20210921T110000
DTEND;TZID=Australia/Brisbane:20210921T120000
DTSTAMP:20210916T020334Z
CREATED:20210916T020333Z
LAST-MODIFIED:20210916T020334Z
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SUMMARY:Distinguished Visitor Seminar - Assistant Professor Linda Tan
DESCRIPTION:Distinguished Visitor Seminar – Assistant Professor Linda Tan\nThe Centre for Data Science Distinguished Visitor Seminar Series has been created to provide a platform for our esteemed colleagues from across the nation and the globe to share their research\, knowledge and expertise in the field of data science. \nRegister now \nThe next seminar will be presented by Linda S. L. Tan. Linda is an Assistant Professor in the Department of Statistics and Data Science at the National University of Singapore. Her research interests are in variational approximation methods and improving the accuracy and rate of convergence of Bayesian computational algorithms. In this seminar\, Linda will present: \nEfficient data augmentation techniques for state space models\nWe propose a data augmentation scheme for improving the rate of convergence of the EM algorithm in estimating Gaussian state space models. The scheme considers a linear transformation of the latent states in which two working parameters are introduced for rescaling and recentering. We derive optimal values of the working parameters by minimizing the fraction of missing information and study their large sample properties and dependence on the persistence and signal-to-noise ratio. An alternating expectation-conditional maximization (AECM) algorithm is designed to take advantage of the proposed scheme and shown to be a more attractive alternative to the centered parametrization (CP) or noncentered parametrization (NCP). We extend earlier results to Bayesian Markov chain Monte Carlo (MCMC) algorithms for non-Gaussian state space models\, focusing on the stochastic volatility and stochastic conditional duration models. A block-specific reparametrization (BSR) strategy for multi-block MCMC samplers is proposed which enables the EM data augmentation scheme to be applied to non-Gaussian models via a mixture of normals approximation. Applications on simulated data and benchmark real data sets indicate that the BSR strategy can yield improvements in simulation efficiency compared with the CP or NCP\, and sometimes even over ASIS (which interweaves the CP and NCP). \n*** This will be an online event only. A Zoom link will be emailed to registrants on the day of the event. ***
URL:https://australiandatascience.net/event/distinguished-visitor-seminar-assistant-professor-linda-tan/
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