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

Publisher Summary Mixture distributions comprise a finite or infinite number of components,
possibly of different distributional types, that can describe different features of data. The …

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 …

Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …

History Markov chain Monte Carlo (MCMC) methods, in which plausible values for unknown
quantities are simulated from their appropriate probability distribution, have revolutionised …

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 …

Reveals How HMMs Can Be Used as General-Purpose Time Series ModelsImplements all
methods in RHidden Markov Models for Time Series: An Introduction Using R applies …

We consider the problem of comparing complex hierarchical models in which the number of
parameters is not clearly defined. Using an information theoretic argument we derive a …

Cluster analysis is the automated search for groups of related observations in a dataset.
Most clustering done in practice is based largely on heuristic but intuitively reasonable …

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 …