Assessing the variability of posterior probabilities in Gaussian model-based clustering Article Swipe

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Multivariate statistics Cluster analysis Percentile Statistics Posterior probability Confidence interval Computer science Data set Gaussian Mathematics Data mining Pattern recognition (psychology) Artificial intelligence Bayesian probability Quantum mechanics Physics

Yuchi Zhang , Ryan P. Browne , Jeffrey L. Andrews ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.1080/03610918.2021.1894334 · OA: W3138271423

We propose a variant of the bootstrap to assess the variability of posterior probabilities arising from Gaussian model-based clustering. The bootstrap variant uses predictions based on out-of-bootstrap-sample observations and then constructs confidence intervals for the posterior probabilities using the percentile method. The methodology outperforms the multivariate Delta method approach when comparing empirical coverage probabilities on simulated data. The proposed and multivariate Delta methods are also illustrated on the well-known Iris data set.