Exact inference via quasi-conjugacy in two-parameter Poisson–Dirichlet hidden Markov models
双参数泊松–狄利克雷隐马尔可夫模型的准共轭精确推断
We introduce a nonparametric model for inferring time-evolving, unobserved probability distributions from discrete-time data consisting of unlabelled partitions. The latent process is a two-parameter Poisson–Dirichlet diffusion, and observations arise via exchangeable sampling. Applications include social and genetic data where only aggregate clustering summaries are observed.To address the intractable likelihood, we develop a tractable inferential framework that avoids…