13 August 2020 | 22:00 UTC/GMT
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Speaker Abstracts
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Motivation:
Bayesian methods have been extensively developed during the past decades and been very successful in different applications such as genetics, imaging and epidemiology. However, recent advanced biomedical technology has generated a large number of data sets which may involve huge sample size, high dimensional variables, and more complex structures according to the specific data type. The standard Bayesian tools are often inadequate to analyze those data sets in term of flexibility and robustness in prior modeling as well as computational feasibility for posterior inferences. According to the needs of analyzing different type of complex biomedical data, novel prior specifications, new statistical modeling strategy and more efficient posterior computation algorithms are actively being developed. Hence, there is a timely need to discuss and disseminate these advanced Bayesian methods and techniques. An organized invited session at one of the IBC 2020 would be an excellent platform to do so. We feel the topic is timely, original and impactful, and should greatly cater to the interest of IBC attendees.
Organizer:
Jian Kang, University of Michigan
Speakers:
Matt Wand, University of Technology Sydney, Australia
Streamlined variational inference for random effects
Bhramar Mukherjee, University of Michigan, Ann Arbor, USA
Bayesian Methods for Hight Dimensional Mediation Analysis
Tingting Zhang, University of Virginia, USA
The evolution of the directional brain network throughout seizure development
Yi Shen, University of Waterloo, Canada
Random topology in the soft-thresholded Gaussian model