This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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523
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Type:
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Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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Abstract - #307479 |
Title:
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SAMC Particle Filter for Nonlinear State Space Models
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Author(s):
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Mingqi Wu*+ and Faming Liang
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Companies:
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Texas A&M University and Texas A&M University
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Address:
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Dept. of Statistics, Texas A&M University, College Station, TX, 77843,
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Keywords:
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Sequential Monte Carlo ;
Stochastic approximation Monte Carlo ;
Particle filter ;
Importance sampling ;
Nonlinear state space model
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Abstract:
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Particle filters provide an attractive approach to approximate a sequence of probability distributions of interest using a large population of important samples/particles. One open problem in particle filter research is that the weight of the particles tend to degenerate as the number of filtration steps increases. In this talk, we propose a stochastic approximation Monte Carlo (SAMC) based particle filter, which avoids the notorious weight degeneracy problem by taking advantage of two attractive features of the SAMC algorithm: (i)superiority in sample space exploration and (ii)ability to generate weight-bounded importance samples. We compare the new particle filter with some existing particle filters on two non-linear state space models. The numerical results indicate that the new particle filter can significantly outperform the others.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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