Statistical Modelling

Before discussing Bayesian classifcation methods we intoduce the concept of mixture models, often used for unsupervised classification tasks. Mixture models are a diverse set of statistical models which can take an endless number of forms, the discussion here is limited on finite mixture models. The most common form of which is likely the Guassian mixture model (GMM), used for a variety of applications including clustering, an unsupervised classification method, and traditional modelling. Mixture models are often used to cluster like data-points into groups. In the Bayesian paradigm this is done by inferring a latent classification variable for each data-point. ...

Hierarchical models are synonymous with Bayesian methods, which is better equiped to be able to infer the parameters of large and complex models when compared to frequentist methods. They are popular as they allow simple building blocks to be combined to form a large and complex model. The ability of hierarchical models to reflect complex systems means they have been applied to a variety of modelling situations, within all aspects of science. ...