Comparing distributions by using dependent normalized random-measure mixtures
J. E. Griffin, M. Kolossiatis, M. F. J. Steel
Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 2013 · Volume 75 · Issue 3
Abstract
A methodology for the simultaneous Bayesian non-parametric modelling of several distributions is developed. Our approach uses normalized random measures with independent increments and builds dependence through the superposition of shared processes. The properties of the prior are described and the modelling possibilities of this framework are explored in detail. Efficient slice sampling methods are developed for inference. Various posterior summaries are introduced which allow better understanding of the differences between distributions. The methods are illustrated on simulated data and examples from survival analysis and stochastic frontier analysis.
Keywords
Bayesian non-parametrics, Dependent distributions, Dirichlet process, Normalized generalized gamma process, Slice sampling, Utility function
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