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SUMMARY:New Thinking about Statistical Inference and its Application to Cl
 imate Prediction - Arthur  Dempster (Harvard)
DTSTART:20101117T140000Z
DTEND:20101117T150000Z
UID:TALK27805@talks.cam.ac.uk
CONTACT:Richard Nickl
DESCRIPTION:Bayesian models and methods have arguably been the gold standa
 rd for probabilistic assessment of  scientific uncertainty for 250 years. 
 The theory of belief functions\, also known as Dempster-Shafer (DS) theory
  referring to its founders in the 1960s and 1970s\, has the potential to b
 reak through limitations and confusions that are the legacy of the major c
 onceptual and technical advances of the 20th century. I start with a brief
  sketch of sources\, motivations\, and key elements of DS\, including why 
 this may be a propitious time for a new look at a not-so-new formalism. Fo
 r statisticians\, key ideas are that probabilities are personal and extend
  beyond the Bayesian paradigm to include probabilities of "don't know"\, p
 roviding new tools for addressing problems of robustification\, multiparam
 eter estimation\, and nonidentifiability. For climatology\, DS opens up ne
 w ways to think about what is predictable and what is not predictable.
LOCATION:MR15\, CMS\, Wilberforce Road\, Cambridge\, CB3 0WB
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