This article reviews machine learning methods for bioinformatics. It presents modelling
methods, such as supervised classification, clustering and probabilistic graphical models for …

Over the last 20 years, comprehensive strategies for treating measurement error in complex
models and accounting for the use of extra data to estimate measurement error parameters …

We present the R package mixsmsn, which implements routines for maximum likeli-hood
estimation (via an expectation maximization EM-type algorithm) in finite mixture models with …

A new Markov chain Monte Carlo method for the Bayesian analysis of finite mixture
distributions with an unknown number of components is presented. The sampler is …

It is well known that the log-likelihood function for samples coming from normal mixture
distributions may present spurious maxima and singularities. For this reason here we …

Modeling based on finite mixture distributions is a rapidly developing area with an exploding
range of applications. Finite mixture models are nowadays applied in such diverse areas as …

In many applications, it is important to find the mixture with fewest number of components,
known as the mixture complexity, that provides a satisfactory fit to the data. This article …

Editorial: Advances in Mixture Models: Computational Statistics & Data Analysis: Vol 51, No 11
skip to main content ACM Digital Library home ACM home Google, Inc. (search) Advanced …

This paper explores the use of the Learning Automata (LA) algorithm to compute threshold
selection for image segmentation as it is a critical preprocessing step for image analysis …

Likert scaling is one of the oldest and most widely used methods in behavioral science
research, and is one of the key methodologies for attitude measurement. Arguably, there are …