Bayesian inference provides a consistent method for the extraction of information from
physics experiments even in ill-conditioned circumstances. The approach provides a unified …

A finite mixture model is well-known in statistics due to its versatility and is being actively
researched. This paper reviews finite mixture model literature via bibliometric analysis …

The integrated nested Laplace approximation (INLA) is a recent computational method that
can fit Bayesian models in a fraction of the time required by typical Markov chain Monte …

Optimization algorithms and Monte Carlo sampling algorithms have provided the
computational foundations for the rapid growth in applications of statistical machine learning …

The majority of data sets collected by researchers in all disciplines are multivariate, meaning
that several measurements, observations, or recordings are taken on each of the units in the …

Networks are ubiquitous in science and have become a focal point for discussion in
everyday life. Formal statistical models for the analysis of network data have emerged as a …

The deviance information criterion (DIC) introduced by Spiegelhalter et al.(2002) for model
assessment and model comparison is directly inspired by linear and generalised linear …

Drawing on the authors' extensive research in the analysis of categorical longitudinal data,
this book focuses on the formulation of latent Markov models and the practical use of these …

In the past ten years there has been a dramatic increase of interest in the Bayesian analysis
of finite mixture models. This is primarily because of the emergence of Markov chain Monte …

Two fundamental approaches to information averaging are based on linear and logarithmic
combination, yielding the arithmetic average (AA) and geometric average (GA) of the fusing …