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• W2008352624 abstract "Sequential Monte Carlo (“particle filtering”) methods provide a powerful set of tools for recursive optimal Bayesian filtering in state-space models. However, these methods are based on importance sampling, which is known to be non-robust in several key scenarios, and therefore standard particle filtering methods can fail in these settings. We present a filtering method which solves the key forward recursion using a reparameterized Gibbs sampling method, thus sidestepping the need for importance sampling. In many cases the resulting filter is much more robust and efficient than standard importance-sampling particle filter implementations. We illustrate the method with an application to a nonlinear, non-Gaussian model from neuroscience." @default.
• W2008352624 created "2016-06-24" @default.
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• W2008352624 date "2012-03-01" @default.
• W2008352624 modified "2023-09-26" @default.
• W2008352624 title "Robust particle filters via sequential pairwise reparameterized Gibbs sampling" @default.
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• W2008352624 doi "https://doi.org/10.1109/ciss.2012.6310772" @default.
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