Author

Gianluca Palmari

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Preprint Aug 2026

Proper-score observation-driven filters: local geometry, estimation, and continuous-time limits

Observation-driven filters usually use the likelihood score, tying their updates to the logarithmic scoring rule. We study recursions driven instead by the negative parameter derivative of a differentiable proper scoring rule, under a declared working family and predictable scaling. This separates three roles: the rule...

Giulia Livieri, Gianluca Palmari · 1 citation

#machine learning Preprint Aug 2026

Online Learning of Scale Parameters in Score-Driven Filters

The central observation is that the negative product-of-scores feedback employed in accelerated score-driven recursions can be read as the stochastic gradient of this predictive loss, offering a new variational perspective.

Fabrizio Lillo, Giulia Livieri, Gianluca Palmari · 0 citations