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MCMC methods for functions modifying old algorithms to make them faster

Abstract:

Many problems arising in applications result in the need to probe a probability distribution for functions. Examples include Bayesian nonparametric statistics and conditioned diffusion processes. Standard MCMC algorithms typically become arbitrarily slow under the mesh refinement dictated by nonparametric description of the unknown function. We describe an approach to modifying a whole range of MCMC methods which ensures that their speed of convergence is robust under mesh refinement. In the ...

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Publication date:
2012-01-01
UUID:
uuid:8134e243-9306-46f5-8b1f-2c60080ef702
Local pid:
oai:eprints.maths.ox.ac.uk:1492
Deposit date:
2012-03-03

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