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Talk at Oxford on Friday March 11

March 5, 2016

I’ve been invited to present my work on sequential Monte Carlo methods for Raman spectroscopy at the Oxford Computational Statistics and Machine Learning Reading Group (OCSMLRG), 11am on Friday 11 March. I’ve made some good progress since my seminar at QUT last year, so I’m looking forward to presenting these methods for a new audience. The abstract of my talk is below.

Title: Bayesian modelling & computation for Raman spectroscopy
Speaker: Dr. Matthew T. Moores, Department of Statistics, University of Warwick
Date: Friday March 11, 2016
Time: 4pm 11am
Location: Common room, 2 south parks road, University of Oxford

Raman spectroscopy can be used to identify molecules by the characteristic scattering of light from a laser. Each Raman-active dye label has a unique spectral signature, comprised by the locations and amplitudes of the peaks. The Raman spectrum is discretised into a multivariate observation that is highly collinear, hence it lends itself to a reduced-rank representation. We introduce a sequential Monte Carlo (SMC) algorithm to separate this signal into a series of peaks plus a smoothly-varying baseline, corrupted by additive white noise. By incorporating this representation into a Bayesian functional regression, we can quantify the relationship between dye concentration and peak intensity. We also estimate the model evidence using SMC to investigate long-range dependence between peaks. These methods have been implemented as an R package, using RcppEigen and OpenMP.

This is joint work with Mark Girolami (Warwick & ATI), Jake Carson (Warwick), Kirsten Gracie, Karen Faulds & Duncan Graham (Strathclyde).


From → Functional Data

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