Consensus Monte Carlo for Random Subsets using Shared Anchors
This enables scalable inference for random subset models like Dirichlet process mixtures and Indian buffet processes, addressing big data challenges in fields such as genomics and healthcare.
The authors developed a consensus Monte Carlo algorithm to scale Bayesian nonparametric models for clustering and feature allocation to big data, demonstrating its effectiveness through simulation studies and applications to MNIST images, pancreatic cancer mutations, and electronic health records.
We present a consensus Monte Carlo algorithm that scales existing Bayesian nonparametric models for clustering and feature allocation to big data. The algorithm is valid for any prior on random subsets such as partitions and latent feature allocation, under essentially any sampling model. Motivated by three case studies, we focus on clustering induced by a Dirichlet process mixture sampling model, inference under an Indian buffet process prior with a binomial sampling model, and with a categorical sampling model. We assess the proposed algorithm with simulation studies and show results for inference with three datasets: an MNIST image dataset, a dataset of pancreatic cancer mutations, and a large set of electronic health records (EHR). Supplementary materials for this article are available online.