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SUMMARY:Virtual BSU Seminar: &quot\;Variable Selection and Prioritization 
 in Bayesian Machine Learning Methods&quot\; - Lorin Crawford\, Associate P
 rofessor of Biostatistics\, Brown University 
DTSTART:20221018T130000Z
DTEND:20221018T140000Z
UID:TALK184088@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:A consistent theme of the work done in my lab group is to take
  modern computational approaches and develop theory that enable their inte
 rpretations to be related back to classical genomic principles. The centra
 l aim of this talk is to address variable selection questions in nonlinear
  and nonparametric regression. Motivated by statistical genetics\, where n
 onlinear interactions and non-additive variation are of particular interes
 t\, we introduce a novel\, interpretable\, and computationally efficient w
 ay to summarize the relative importance of predictor variables. Methodolog
 ically\, we present flexible and scalable classes of Bayesian feedforward 
 models which provide interpretable probabilistic summaries such as posteri
 or inclusion probabilities and credible sets for association mapping tasks
  in high-dimensional studies. We illustrate the benefits of our methods ov
 er state-of-the-art linear approaches using extensive simulations. We also
  demonstrate the ability of these methods to recover both novel and previo
 usly discovered genomic associations using real human complex traits from 
 the Wellcome Trust Case Control Consortium (WTCCC)\, the Framingham Heart 
 Study\, and the UK Biobank.
LOCATION:Virtual Seminar 
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