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VERSION:2.0
PRODID:-//University of Liverpool Computer Science Seminar System//v2//EN
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DTSTAMP:20260413T001047Z
UID:Seminar-DMML-1107@lxserverM.csc.liv.ac.uk
ORGANIZER:CN=Danushka Bollegala:MAILTO:Danushka.Bollegala@liverpool.ac.uk
DTSTART:20201118T110000
DTEND:20201118T120000
SUMMARY:Data Mining and Machine Learning Series
DESCRIPTION:David Hughes: Fast approximate inference for multivariate longitudinal data\n\nCollecting information on multiple longitudinal outcomes is increasingly common in many clinical settings. In many cases it is desirable to model these outcomes jointly. However, in large datasets, with many outcomes, computational burden often prevents the simultaneous modelling of multiple outcomes within a single model. We develop a mean field variational Bayes algorithm, to jointly model multiple Gaussian, Poisson or binary longitudinal markers within a multivariate generalised linear mixed model. Through simulation studies and clinical applications (in the fields of sight threatening diabetic retinopathy and primary biliary cirrhosis) we demonstrate substantial computational savings of our approximate approach when compared to a standard Markov Chain Monte Carlo, while maintaining good levels of accuracy of model parameters.\n\nhttps://www.csc.liv.ac.uk/research/seminars/abstract.php?id=1107
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