Finite mixture models have been widely used for modeling probability distribution of real
data sets due to its benefits from analytical tractability. Among the finite mixtures, the finite …

The finite mixture model based on the Student's-t distribution, which is heavily tailed and
more robust than the Gaussian mixture model (GMM), is a flexible and powerful tool to …

A mixture model based on the symmetric Gaussian distribution that simultaneously treats the
feature selection, and the model detection has recently received great attention for pattern …

As a variant of Finite mixture model (FMM), finite Inverted Dirichlet mixture model (IDMM)
can not avoid the conventional challenges, such as how to select the appropriate number of …

The Gaussian hidden Markov model has been successfully used in pattern recognition and
classification applications; however, recently the Student's t‐mixture model is regarded as …

Mixture models have become arguably one of the most widely used statistical approaches to
perform inference on various types of data and have been successfully applied in data …

Mixture models have been widely used as statistical learning paradigms in various
unsupervised machine learning applications, where labeling a vast amount of data is …

In Bayesian analysis of a statistical model, the predictive distribution is obtained by
marginalizing over the parameters with their posterior distributions. Compared to the …

Bayesian estimation of the parameters in beta mixture models (BMM) is analytically
intractable. The numerical solutions to simulate the posterior distribution are available, but …

Technological advances led to the generation of large scale complex data. Thus, extraction
and retrieval of information to automatically discover latent pattern have been largely studied …