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.
Simple conditions for the geometric convergence of the gibbs sampler
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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.