Nested sampling is a Monte Carlo algorithm used for calculating the posterior distribution of parameters in Bayesian statistics. It works by transforming the problem of integration into a series of simpler problems, allowing for efficient exploration of the parameter space. This method is particularly useful for high-dimensional integrals, as it effectively samples from the likelihood function while simultaneously estimating the evidence.
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