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Efficient Sequential Monte Carlo Using Interpolation

Research and Teaching Output of the MIT Community

Show simple item record Hover, Franz S. 2010-03-08T22:05:49Z 2010-03-08T22:05:49Z 2010-03-08T22:05:49Z
dc.description.abstract A limitation common to all sequential Monte Carlo algorithms is the computational demand of accurately describing an arbitrary distribution, which may preclude real-time implementation for some systems. We propose using interpolation to construct a high accuracy approximation to the importance density. The surrogate density can then be efficiently evaluated in place of sampling the true importance density, allowing for the propagation of a large number of particles at reduced cost. Numerical examples are given demonstrating the utility of the approach. en
dc.language.iso en_US en
dc.title Efficient Sequential Monte Carlo Using Interpolation en
dc.type Article en

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