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arXiv · 2411.16056

Enhancing Computational Efficiency in State-Space Models Using Rao-Blackwellization and 2-Step Approximation

Genshiro Kitagawa
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Abstract

This paper explores a Bayesian self-organization method for state-space models, enabling simultaneous state and parameter estimation without repeated likelihood calculations. While efficient for low-dimensional models, high-dimensional cases like seasonal adjustment require many particles. Using Rao-Blackwellization and a 2-step approximation, the method reduces particle use and computation time while maintaining accuracy, as shown in Monte Carlo evaluations.

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Genshiro Kitagawa. 2024-11-25. Enhancing Computational Efficiency in State-Space Models Using Rao-Blackwellization and 2-Step Approximation. https://arxiv.org/abs/2411.16056

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