{"ID":6024172,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-09T23:16:22.029503457Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05692","arxiv_id":"2607.05692","title":"Exact computation of posterior distribution of mixture weights in hierarchical Bayesian models","abstract":"Hierarchical mixture models are a powerful tool for modeling data generated from heterogeneous sources, particularly when the mixing proportion $\\boldsymbol{w}$ itself is treated as a random variable with a Dirichlet or Beta-Liouville prior. Such models are widely employed in scenarios where uncertainty in class membership or data-generating processes must be probabilistically quantified. This paper studies the exact marginalization of the mixture weight. For the two-component case we give an $O(n^2)$ dynamic program -- and an $O(n \\log^2 n)$ FFT variant -- for the marginal likelihood, and show that the exact posterior of the weight is a finite mixture of Beta distributions, delivering closed-form posterior summaries, credible intervals and per-observation local false-discovery rates without any sampling. For $K \\ge 3$ components we give an exact joint dynamic program. The gain is largest in the small-sample regime the method is built for: on a real multilevel meta-analysis, a pathway-level dysregulation analysis of leukemia gene expression, and a leukemia-derived gene-panel benchmark with known ground truth, the exact interval for the signal proportion is calibrated where EM gives no interval at all (collapsing to a boundary) and Gaussian/Laplace approximations mis-cover, and it is two orders of magnitude faster than the sampler that would match it. On the large prostate-cancer benchmark, where every method has ample data, it agrees with locfdr on the gene ranking while adding a posterior interval for the null proportion.","short_abstract":"Hierarchical mixture models are a powerful tool for modeling data generated from heterogeneous sources, particularly when the mixing proportion $\\boldsymbol{w}$ itself is treated as a random variable with a Dirichlet or Beta-Liouville prior. Such models are widely employed in scenarios where uncertainty in class member...","url_abs":"https://arxiv.org/abs/2607.05692","url_pdf":"https://arxiv.org/pdf/2607.05692v1","authors":"[\"Georgy Meshcheryakov\"]","published":"2026-07-06T23:20:43Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"stat.ML\"]","methods":"[]","has_code":false}