Xiaofei Zhou

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2015-252

December 18, 2015

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-252.pdf

We apply coarse-to-fine MCMC to perform Bayesian inference for a seismic monitoring system. While traditional MCMC has difficulty moving between local optima, by applying coarse-to-fine MCMC, we can adjust the resolution of the model and this allows the state to jump between different optima more easily. It is quite similar to simulated annealing. We will use a 1D model as an example, and then compare traditional MCMC with coarse-to-fine MCMC and discuss the scaling behavior.

Advisors: Stuart J. Russell

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BibTeX citation:

@mastersthesis{Zhou:EECS-2015-252,
    Author= {Zhou, Xiaofei},
    Title= {Coarse-to-fine MCMC in a seismic monitoring system},
    School= {EECS Department, University of California, Berkeley},
    Year= {2015},
    Month= {Dec},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-252.html},
    Number= {UCB/EECS-2015-252},
    Abstract= {We apply coarse-to-fine MCMC to perform Bayesian inference for a seismic monitoring system. While traditional MCMC has difficulty moving between local optima, by applying coarse-to-fine MCMC, we can adjust the resolution of the model and this allows the state to jump between different optima more easily. It is quite similar to simulated annealing. We will use a 1D model as an example, and then compare traditional MCMC with coarse-to-fine MCMC and discuss the scaling behavior.},
}

EndNote citation:

%0 Thesis
%A Zhou, Xiaofei 
%T Coarse-to-fine MCMC in a seismic monitoring system
%I EECS Department, University of California, Berkeley
%D 2015
%8 December 18
%@ UCB/EECS-2015-252
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-252.html
%F Zhou:EECS-2015-252