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SUMMARY:Modelling Astrophysics Data for Discovery\, Classification and Pre
 cise Measurement - David Hogg (Centre for Cosmology and Particle Physics\,
  New York University)
DTSTART:20111011T153000Z
DTEND:20111011T163000Z
UID:TALK33441@talks.cam.ac.uk
CONTACT:David Titterington
DESCRIPTION:In applications as varied as the measurement of stellar proper
  motions\, the determination of the Milky Way mass with maser kinematics\,
  and the selection of quasar targets for SDSS-III BOSS\, precise - and mor
 e important\, accurate - data analysis requires a model that generates the
  data.  A generative model produces a probability distribution function in
  the space of the noisy data\, after convolution by observational uncertai
 nty distribution functions.  I show that proper modelling of the data-gene
 rating process performs better than other data analysis and classification
  methods\, in scientific applications in which measurements come with rela
 tively reliable uncertainty estimates.  I make also some comments on the t
 heoretical basis for\, and ideal outputs from\, any principled program of 
 data analysis.  These results have implications for almost all ongoing and
  future astrophysics projects.
LOCATION:Ryle Seminar Room\, Cavendish Laboratory
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