Záznam dôkazov metódy

Robust Particle Filter

The Robust Particle Filter is a sequential Monte Carlo method that tracks hidden states in nonlinear, non-Gaussian systems while remaining resistant to outliers and model misspecification. It replaces the standard Gaussian likelihood with a heavy-tailed or bounded-influence density, so that anomalous observations receive downweighted importance and cannot derail the state estimate.

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Taxonomický záznam metódy · bayesian / bayesian

Ristic, B., Arulampalam, S. & Gordon, N. (2004). Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House. · ISBN 978-1580536318
Hurzeler, M. & Kunsch, H. R. (1998). Monte Carlo approximations for general state-space models. Journal of Computational and Graphical Statistics, 7(2), 175-193. · URL

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Same method familyHamiltonian Monte Carlomachine-suggested · Relational suggestion, not evidence.Taxonomic bucketKalman Filtermachine-suggested · Relational suggestion, not evidence.Same method familyParticle Filtermachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRobust Kalman Filtermachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRobust Sequential Monte Carlomachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSequential Monte Carlomachine-suggested · Relational suggestion, not evidence.