Exact computation of posterior distribution of mixture weights in hierarchical Bayesian models
TL;DR
The exact marginalization of the mixture weight is studied, and it is shown 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.
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.