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Activity Number:
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151
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 3, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #303797 |
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Title:
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Targeted Sequential Resampling from Large Data Sets in Mixture Modeling
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Author(s):
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Ioanna Manolopoulou*+
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Companies:
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Statistical and Applied Mathematical Sciences Institute
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Address:
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19 T.W. Alexander Drive, Durham, NC, 27709-4006,
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Keywords:
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Sequential Monte Carlo ; Rare event detection ; Large datasets ; Mixture models
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Abstract:
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One of the challenges of Markov Chain Monte Carlo in large data sets is the need to scan through the whole data at each iteration of the sampler, which can be computationally prohibitive. Several approaches have been developed to address this, typically drawing computationally manageable samples of the data. Here we consider the specific case when most of the data provides no information about the parameters of interest. The motivating application arises in flow cytometry, where interest lies in identifying specific rare cell subtypes and characterizing them according to their corresponding markers. We present a MCMC approach where an initial sample of the full data is used to draw a further set of datapoints which contains more information about rare events, and extend it to a Sequential Monte Carlo framework whereby the selected sample is augmented sequentially as estimates improve.
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