Informed Asymmetric Dirichlet Priors for Multivariate Bernoulli Mixture Models

TL;DR

This paper introduces a Bayesian clustering method for multivariate binary data using an asymmetric Dirichlet prior and an efficient MCMC algorithm, balancing computational efficiency and full posterior inference.

Contribution

It proposes a novel Bayesian approach with an asymmetric Dirichlet prior fixed on a large component number, enhancing interpretability and efficiency in clustering binary data.

Findings

The method is competitive with existing clustering algorithms.

It can outperform alternatives in specific scenarios.

Demonstrated on ecological presence-absence data.

Abstract

Clustering multivariate binary data is of interest in many scientific fields, including ecology, biomedicine, and social policy. Beyond heuristic clustering algorithms, such data can be modelled using multivariate Bernoulli mixture models. Many Bayesian implementations of these models involve a trade-off between computational efficiency and full posterior inference. We propose instead a Bayesian approach able to provide both aspects. The method fixes the total number of components to a large value and employs an asymmetric Dirichlet prior on the mixture weights. The asymmetric Dirichlet hyperparameters are elicited using the popular Penalized Complexity prior framework, which provides an intuitive way for users to inform the induced distribution of the number of clusters. An efficient MCMC algorithm is then developed to fit the model. Simulations and real-world applications demonstrate that the method is competitive with existing alternatives and can outperform them in certain settings. The proposal is illustrated using an ecological dataset about presence-absence of species across multiple sites, where cluster-specific parameters are modelled on the basis of environmental conditions. Overall, the proposed method provides a computationally efficient, fully Bayesian, and interpretable framework for clustering multivariate binary data, with potential applications across diverse scientific domains.