Efficient Sequential Monte Carlo Using Interpolation
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 d...
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| Format: | Article |
| Language: | en_US |
| Published: |
2010
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| Online Access: | http://hdl.handle.net/1721.1/52403 |
| Summary: | 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. |
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