The marginal likelihood plays an important role in many areas of Bayesian statistics such as
parameter estimation, model comparison, and model averaging. In most applications …

Amos is short for Analysis of MOment Structures. It implements the general approach to data
analysis known as structural equation modeling (SEM), also known as analysis of …

Statistical procedures such as Bayes factor model selection and Bayesian model averaging
require the computation of normalizing constants (eg, marginal likelihoods). These …

IBM SPSS Amos implements the general approach to data analysis known as structural
equation modeling (SEM), also known as analysis of covariance structures, or causal …

The prominence of finite mixture modelling is greater than ever. Many important statistical
topics like clustering data, outlier treatment, or dealing with unobserved heterogeneity …

IBM SPSS Amos implements the general approach to data analysis known as structural
equation modeling (SEM), also known as analysis of covariance structures, or causal …

In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian
distributions, we present a joint approach to estimate the number of mixture components and …

The desire to train complex machine learning algorithms and to increase the statistical
power in association studies drives neuroimaging research to use ever-larger datasets. The …

K∑ k= 1 ηkfT (y| θk),(10.1) where y is the realisation of a univariate or multivariate, discrete-
or continuousvalued random variable and the component densities fT (y| θk) arise from the …

The use of Markov chain Monte Carlo (MCMC) methods for estimating hierarchical models
involves complex data structures and is often described as a revolutionary development. An …