A Gibbs sampler for the multi-object posterior is constructed by proving that its conditional distributions are Bernoulli random finite sets with explicit forms, allowing efficient sampling and new smoothing algorithms.
Computationally-tractable approximate PHD and CPHD filters for superpositional sensors
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Multi-Object Posterior Computation via Gibbs Sampling
A Gibbs sampler for the multi-object posterior is constructed by proving that its conditional distributions are Bernoulli random finite sets with explicit forms, allowing efficient sampling and new smoothing algorithms.