Paper on Mixtures of Gaussian Process Priors

[Joerg_Lemm]

Dear colleagues,

The following paper (10 pages) is now available at

        http://xxx.lanl.gov/ps/physics/9911077 

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        "Mixtures of Gaussian process priors" 

        by Joerg Lemm, Muenster University


Abstract: 

Nonparametric Bayesian approaches based on Gaussian processes have
recently become popular in the empirical learning community. They
encompass many classical methods of statistics, like Radial Basis
Functions or various splines, and are technically convenient because
Gaussian integrals can be calculated analytically. Restricting to Gaussian
processes, however, forbids for example the implemention of genuine
nonconcave priors. Mixtures of Gaussian process priors, on the other hand,
allow the flexible implementation of complex and situation specific, also
nonconcave "a priori" information. This is essential for tasks with,
compared to their complexity, a small number of available training data.
The paper concentrates on the formalism for Gaussian regression problems
where prior mixture models provide a generalisation of classical
quadratic, typically smoothness related, regularisation approaches being
more flexible without having a much larger computational complexity.

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The paper and related material can also be found at my home page:

        http://pauli.uni-muenster.de/~lemm/

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Joerg Lemm                      
Universitaet Muenster                        Email: lemm at uni-muenster.de     
Institut fuer Theoretische Physik I          Phone:     +49(251)83-34922
Wilhelm-Klemm-Str.9                            Fax:     +49(251)83-36328
D-48149 Muenster, Germany             http://pauli.uni-muenster.de/~lemm      
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