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The number of modes in a probability density function is representative ofthe model's complexity and can also be viewed as the number of existingsubpopulations. Despite its relevance, little research has been devoted to itsestimation. Focusing on the univariate setting, we propose a novel approachtargeting prediction accuracy inspired by some overlooked aspects of theproblem. We argue for the need for structure in the solutions, the subjectiveand uncertain nature of modes, and the convenience of a holistic view blendingglobal and local density properties. Our method builds upon a combination offlexible kernel estimators and parsimonious compositional splines. Featureexploration, model selection and mode testing are implemented in the Bayesianinference paradigm, providing soft solutions and allowing to incorporate expertjudgement in the process. The usefulness of our proposal is illustrated througha case study in sports analytics, showcasing multiple companion visualisationtools. A thorough simulation study demonstrates that traditionalmodality-driven approaches paradoxically struggle to provide accurate results.In this context, our method emerges as a top-tier alternative offeringinnovative solutions for analysts.

Bayesian taut splines for estimating the number of modes