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SUMMARY:PHYSTAT Seminar: Accelerating Bayesian Computation: Parallelizing
Markov Chain Monte Carlo
DTSTART;VALUE=DATE-TIME:20200115T100000Z
DTEND;VALUE=DATE-TIME:20200115T110000Z
DTSTAMP;VALUE=DATE-TIME:20200220T071950Z
UID:indico-event-847948@indico.cern.ch
DESCRIPTION:\n\n\nA full-fledged Bayesian computation requries evaluation
of the posterior probability density in the complete parameter space. Th
is can become very time consuming using commonly used algorithms such as M
arkov Chain Monte Carlos. We present ideas on the parallelization of the
Markov Chain Monte Carlo approach via multi-proposal generation and via p
arameter space partitioning. For the former approach\, recent developments
in weighted sample generation are described and initial results presented
. For massive parallelization via parameter space partitioning\, the cal
culation of the marginal likelihood (evidence) is necessary and we solve t
his task with the Adaptive Harmonic Mean Integration (AHMI) algorithm. We
describe the algorithm and it’s mathematical properties\, and report the
results using it on multiple test cases. \n\n\n\n\nhttps://indico.cern.c
h/event/847948/
LOCATION:CERN 503/1-001 - Council Chamber
URL:https://indico.cern.ch/event/847948/
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