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April 14th-16th, 2008
The primary aim of the workshop is to highlight recent methodological and applied advances in the Bayesian analysis of complex data.
The last decade witnessed an unprecedented growth in the generation of large amounts of heterogeneous datasets. Typical examples are satellite images and environmental measurements, fingerprints, gene and protein expression, astronomical data, high frequency financial time series, brain imaging and multiple spike trains recordings. The substantial experimental error affecting these data requires developing appropriate statistical methodologies for separating signal and noise. The fast-paced accumulation of such complex data underscores the key role of Bayesian methods for synthesizing the information accrued and for out-of-sample predictions.
The workshop will be organised into five keynote presentations and several related invited presentations. Confirmed speakers are:
(Carnegie Mellon University, USA)
The meeting organisers are: