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Iterated filtering algorithms for latent variable models are improved using a novel Bayes map convergence theory. This new approach offers significant numerical gains for parameter inference in partially observed Markov processes.

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Area of Science:

  • Computational Statistics
  • Machine Learning
  • Stochastic Optimization

Background:

  • Iterated filtering algorithms are used for parameter inference in latent variable models.
  • Existing theoretical support relies on approximating log-likelihood derivatives using conditional moments.

Purpose of the Study:

  • To introduce a new theoretical foundation for iterated filtering algorithms.
  • To develop an improved algorithm for parameter inference in partially observed Markov processes.

Main Methods:

  • Developed a theoretical approach based on the convergence of an iterated Bayes map.
  • Applied this theory to design a novel iterated filtering algorithm.

Main Results:

  • The new theoretical framework provides a robust foundation for iterated filtering.
  • The developed algorithm shows substantial numerical improvements in inferring parameters of partially observed Markov processes.

Conclusions:

  • The iterated Bayes map convergence theory offers a powerful alternative for understanding iterated filtering.
  • This work advances the computational efficiency and accuracy of parameter inference for complex stochastic models.