Clustering is the task of automatically gathering observations into homogeneous groups,
where the number of groups is unknown. Through its basis in a statistical modeling …

Background At diagnosis tumours are typically composed of a mixture of genomically distinct
malignant cell populations. Bulk sequencing of tumour samples coupled with computational …

We consider approximate Bayesian model choice for model selection problems that involve
models whose Fisher information matrices may fail to be invertible along other competing …

This chapter presents some of the Bayesian solutions to the different interpretations of
picking the “right” number of components in a mixture, before concluding on the ill-posed …

Bayesian cluster analysis offers substantial benefits over algorithmic approaches by
providing not only point estimates but also uncertainty in the clustering structure and …

In model-based clustering mixture models are used to group data points into clusters. A
useful concept introduced for Gaussian mixtures by Malsiner Walli et al.(Stat Comput 26 …

The objective of this work was to use the Bayesian approach, modeling the interaction of
coffee genotypes with the environment, using a bisegmented regression to identify stable …

Infinite Mixtures of Infinite Factor Analysers Page 1 Bayesian Analysis (2020) 15, Number 3,
pp. 937–963 Infinite Mixtures of Infinite Factor Analysers Keefe Murphy ∗ , Cinzia Viroli † …

Background Poor diet is a major risk factor of cardiovascular and chronic diseases,
particularly for low-income female adults. However, the pathways by which race and …

In clinical and epidemiological studies, there is a growing interest in studying the
heterogeneity among patients based on longitudinal characteristics to identify subtypes of …