The "'nested sampling algorithm "'is a computational approach to the problem of comparing models in Bayesian statistics, developed in 2004 by physicist John Skilling.
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Here is a simple version of the nested sampling algorithm, followed by a description of how it computes the marginal probability density Z = P ( D | M ) where M is M1 or M2:
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The nested sampling algorithm was developed by John Skilling specifically to approximate these marginalization integrals, and it has the added benefit of generating samples from the posterior distribution P ( \ theta | D, M1 ).
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To solve this inference problem, Batman uses nested sampling ( http : / / www . inference . phy . cam . ac . uk / bayesys / ) to generate 100 independent samples from " f " ( " m " | " A " ) for each tiled region of the genome, then summarizes the most likely methylation state in 100-bp windows by fitting beta distributions to these samples.
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