Auxiliary Variable Based Particle Filters

  • Michael K. Pitt
  • Neil Shephard
Part of the Statistics for Engineering and Information Science book series (ISS)


We model a time series {y t , t = 1, ..., n} using a state-space framework with the {y t |α t } being independent and with the state {α t } assumed to be Markovian. The task will be to use simulation to estimate f(α t |F t ), t = 1, ..., n, where F t is contemporaneously available information. We assume a known measurement density f(y t |α t ) and the ability to simulate from the transition density f(α t+1|α t ). Sometimes we will also assume that we can evaluate f(α t+1|α t ).


Mean Square Error Markov Chain Monte Carlo Particle Filter Markov Chain Monte Carlo Method Stochastic Volatility Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Michael K. Pitt
  • Neil Shephard

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