Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC

NeurIPS 2011 Trung T. PhamTat-Jun ChinJin YuDavid Suter

Multi-structure model fitting has traditionally taken a two-stage approach: First, sample a (large) number of model hypotheses, then select the subset of hypotheses that optimise a joint fitting and model selection criterion. This disjoint two-stage approach is arguably suboptimal and inefficient - if the random sampling did not retrieve a good set of hypotheses, the optimised outcome will not represent a good fit... (read more)

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