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University of Cambridge > Talks.cam > Statistical Laboratory Graduate Seminars > Bayesians turn to Experts for Advice!

## Bayesians turn to Experts for Advice!Add to your list(s) Download to your calendar using vCal - Steven de Rooij (Cambridge)
- Wednesday 19 November 2008, 17:00-18:00
- CMS, MR4.
If you have a question about this talk, please contact HoD Secretary, DPMMS. In Bayesian model selection and model averaging, inference is normally based on a posterior distribution on the models, usually
interpreted as a measure of how likely we consider each of the models to
be “true”, or at least in some sense close to true, given on the
observations.
Rather than with truth, I will be concerned with the more practical
goal of finding a “useful” model, in the sense that it predicts future
outcomes of the underlying process well. As it turns out, the most
useful model may well vary depending on the number of available
observations! For instance, given ten samples from some continuous
density, a seven-bin histogram model is more useful than a 1,000-bin
model, even though the latter is arguably closer to being “true”.
As it turns out, methods for tracking transient performance of
prediction strategies have already been developed in the learning theory
literature under the heading “prediction with expert advice”. I will
illustrate how these methods can improve model selection performance
using results from computer simulations on density estimation problems.
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