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Adaptive Bayesian point-mass estimation
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AI
DOI：10.1016/j.jfranklin.2026.109007.png)
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Abstract
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Parameter estimation is fundamental in many signal-processing tasks. Bayesian methodology provides flexible estimators by updating the probability density (PD) of unknown parameters using observed data. However, beyond exponential-family models, the posterior PD becomes increasingly complex as data accumulates. It calls for approximations-especially in high-rate processing where simplicity is essential.
Keywords:
Adaptive estimation
Approximate algorithm
Bayes recursive estimation
Minimum relative entropy principle
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