STATISTICS SEMINAR :: Independent Particle Filters

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Sequential Monte Carlo methods, especially the particle filter (PF) and its various modifications, have been used effectively in dealing with stochastic dynamic systems. The standard PF samples the current state through the underlying state dynamics, then uses current observation to evaluate the samples' importance weight. However, in many applications the current observation provides significant information about the current state while the state dynamics is weak. Sampling using the current observation in this case often produces efficient samples. In this paper, we formulate the framework for a new variant of the particle filter, the independent particle filter (IPF). It generates exchangeable samples of the current state from a sampling distribution that is conditionally independent of the previous states, a special case of which uses only the current observation. Each sample can then be matched with multiple samples of the previous states for evaluating the importance weight. We present some theoretical results showing that this strategy improves efficiency of estimation as well as reduces resampling frequency in many situations. We also discuss some extensions of the IPF. Several synthetic examples and one real example are used to demonstrate the effectiveness of the method.


  • Workflow Status: Published
  • Created By: Barbara Christopher
  • Created: 10/08/2010
  • Modified By: Fletcher Moore
  • Modified: 10/07/2016


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