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Abstract Details
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Activity Number:
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40
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #302738 |
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Title:
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Online Variational Bayesian Inference in Hierarchical Models for Correlated High-Dimensional Data
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Author(s):
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Sylvie Tchumtchoua*+
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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, P.O. Box 14006, Research Triangle Park, NC, 27709-4006,
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Keywords:
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Hierarchical model ;
Nonparametric Bayes ;
Online variational Bayes ;
Conditional autoregressive model ;
Correlated high-dimensional data
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Abstract:
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High-dimensional data with hundreds of thousands of observations are becoming commonplace in many disciplines. The analysis of such data poses many computational challenges, especially when the observations are correlated over time and/or across space. In this paper we propose flexible hierarchical regression models for analyzing such data that accommodate serial and/or spatial correlation. We address the computational challenges involved in fitting these models by adopting an approximate inference framework. We develop an online variational Bayes algorithm that is fast and accurate. The performance of the method is assessed through simulation studies. We applied the methodology to analyze signal intensity in MRI images.
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Authors who are presenting talks have a * after their name.
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