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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 t
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