Finite mixture models and hidden Markov models (HMMs) occupy central roles in modern statistical inference and data analysis. Finite mixture models assume that data originate from a latent combination ...

Bayesian nonparametric mixture models represent a powerful statistical framework that extends traditional mixture modelling by allowing the number of mixture components to be inferred from the data ...

In this paper, we introduce a Bayesian approach for clustering data using a sparse finite mixture model (SFMM). The SFMM is a finite mixture model with a large number of components k previously fixed ...

Understanding how and why animals use the environments where they occur is both foundational to behavioral ecology and essential to identify critical habitats for species conservation. However, some ...

Data clustering is the process of grouping data items so that similar items are placed in the same cluster. There are several different clustering techniques, and each technique has many variations.

Although deep learning-based methods have demonstrated promising results in estimating the RUL, most methods consider that each time step's features hold equal importance. When data with varying ...