An Iteratively Reweighted Importance Kernel Bayesian Filtering Approach for High-Dimensional Data Processing

Xin Liu ()
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Xin Liu: Mathematical Institute, Peking University, Beijing 100871, China

Mathematics, 2024, vol. 12, issue 19, 1-16

Abstract: This paper proposes an iteratively re-weighted importance kernel Bayes filter (IRe-KBF) method for handling high-dimensional or complex data in Bayesian filtering problems. This innovative approach incorporates importance weights and an iterative re-weighting scheme inspired by iteratively re-weighted Least Squares (IRLS) to enhance the robustness and accuracy of Bayesian inference. The proposed method does not require explicit specification of prior and likelihood distributions; instead, it learns the kernel mean representations from training data. Experimental results demonstrate the superior performance of this method over traditional KBF methods on high-dimensional datasets.

Keywords: kernel Bayesian filtering; re-weighted importance; kernel mean (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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