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.
Annealing Markov chain Monte Carlo with applications to ancestral inference
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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.