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Fig. 5.
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While MCMC methods attempt to generate samples directly from the posterior, NS instead breaks up the posterior into many nested ‘slices’, generates samples from each of them, and then recombines the samples to reconstruct the original distribution using the appropriate weights (Speagle 2020).
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