Objective Bayesian Distribution Fitting // Fits common univariate distributions using registered objective Bayesian priors, including Jeffreys, reference, and maximal data information priors. Model-specific posterior propriety and moment conditions are checked before computation. Exact simulation, marginalization, slice sampling, and adaptive Metropolis algorithms are selected from posterior structure, with a common interface for summaries, diagnostics, prediction, and pointwise log-likelihood evaluation. The reference-prior framework follows Bernardo (1979) doi:10.1111/j.2517-6161.1979.tb01066.x.