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Abstract Details
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Activity Number:
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123
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #303257 |
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Title:
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Reversible Jump Markov Chain Monte Carlo Analysis of Multiple Changes in a Volcano's Eruption Period
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Author(s):
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Jianyu Wang*+ and Robert Wolpert and James Berger
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Companies:
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Duke University and Duke University and Duke University
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Address:
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222 Old Chemistry Building, Durham, NC, 27708, United States
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Keywords:
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Abstract:
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We apply statistical modeling in the risk assessment of volcanic hazards. The goal is to investigate the changes in the eruption frequency of Montserrat's Soufriere Hills volcano and predict the probability of future catastrophic events. First, we will introduce the Reversible Jump Markov Chain Monte Carlo method. We will then describe a penalized mixture prior distribution developed to employ geologists' opinions and deal with consequent difficulty in the calculation of normalizing constants. In addition, we will present a simulation study that illustrates all these ideas. The overall results of the real data showed that our estimates coincide with significant geological changes of the volcano. Ultimately, the value of our approach lies in its ability to model point processes with multiple change points and its extension to other extreme natural hazards.
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