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Another look at Bayes map iterated filtering

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  • Nguyen, Dao

Abstract

A new proof of convergence of Bayes map iterated filtering algorithm, based on supermartingale inequality, is proposed. Relying on log concavity assumption, this approach with verifiable conditions is simpler than the previous approach and is generalizable to more sophisticated algorithms. The assumption could be relaxed at the cost of more technical details.

Suggested Citation

  • Nguyen, Dao, 2016. "Another look at Bayes map iterated filtering," Statistics & Probability Letters, Elsevier, vol. 118(C), pages 32-36.
    • Handle: RePEc:eee:stapro:v:118:y:2016:i:c:p:32-36
    DOI: 10.1016/j.spl.2016.05.013
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    References listed on IDEAS

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    1. Simon N. Wood, 2010. "Statistical inference for noisy nonlinear ecological dynamic systems," Nature, Nature, vol. 466(7310), pages 1102-1104, August.
    2. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
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    Cited by:

    1. Szczepocki Piotr, 2020. "Application of iterated filtering to stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck process," Statistics in Transition New Series, Statistics Poland, vol. 21(2), pages 173-187, June.
    2. Piotr Szczepocki, 2020. "Application of iterated filtering to stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck process," Statistics in Transition New Series, Polish Statistical Association, vol. 21(2), pages 173-187, June.

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