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A new Bayesian MCMC sampling interface. This enables users to perform posterior sampling using the BUMPS DREAM sampler directly from both the
MultiFitterand BUMPS minimizer classes.samplemethod toMultiFitterthat flattens multi-dataset arrays, resolves parameter aliases, and delegates to the minimizer’s sampling method, supporting both 1D and vectorized multi-dimensional fits.samplemethod in the BUMPS minimizer (minimizer_bumps.py) that builds a BUMPSFitProblemand runs the DREAM sampler, returning posterior draws, parameter names, sampler state, and log-probabilities.